Combination Of Multiple Parameters Research Articles

Estimation of virus transport parameters in both unsaturated-saturated zone using numerical simulation and flower pollination algorithm

To develop a virus transport model considering both the unsaturated and saturated zone, it is essential to consider separate parameters for both the zone. This is because the virus fate and transport process depends on the degree of saturation. As such, this paper introduces a parameter estimation model that simultaneously identifies virus transport parameters in an unconfined aquifer for both the unsaturated and saturated zones. The proposed model utilizes a linked simulation-optimization technique to minimize errors between observed and simulated virus concentrations. The simulation model used in this study is termed as “variable detachment model,” this is because the simulation component of this approach incorporates varying adsorption rates, which are influenced by the degree of saturation. This study also highlights the complexities involved in parameter estimation for virus transport models, such as non-uniqueness, instability, and non-identifiability. These challenges arise when multiple parameter combinations can produce similar model predictions, leading to difficulty in determining the actual parameter values. To address these challenges, a meta-heuristic optimization algorithm, Flower Pollination Algorithm (FPA), is used, effectively solving such non-convex problems. The model could estimate all the parameters accurately except the inactivation rates of viruses. Later when the global sensitivity analysis was carried out to understand the relative importance of each parameter, inactivation rates were found to be less influential to the model output. Thus, FPA based parameter estimation model can be used as an alternative for estimating the virus transport parameters in groundwater aquifers.

Predictive value of anthropometric and biochemical indices in non-alcoholic fatty pancreas disease: a cross-sectional study

ObjectivesTriglyceride (TG), triglyceride-glucose index (TyG), body mass index (BMI), TyG-BMI and triglyceride to high-density lipoprotein ratio (TG/HDL) have been reported to be reliable predictors of non-alcoholic fatty liver disease. However,...

Parameters optimization for chatter-free milling tool path based on spindle speed variation method

Existing methods orienting for optimizing cutting parameters in milling process with spindle speed variation (SSV) only optimally selected the axial depth of cut and only considered the acceleration constraint of spindle. This article proposes a systematic optimization method to obtain the best combination of multiple machining parameters (axial and radial depths of cut) and SSV’s parameters (the ratio of variation amplitude and the ratio of variation frequency) so that the chatter-free milling tool path can be shortened. Both acceleration and jerk constraints of spindle are considered. A C3 continuous variable feedrate scheduling algorithm is used to eliminate the influence of spindle speed variation on feed per tooth, which is thus not required to treat as an optimization variable. The machining parameters of each iterative step are updated by following that the number of axial and radial cutting layers could be decreased. Discrete optimization method is combined with chatter stability analysis to solve the problem. A series of milling experiments verify that the proposed method can achieve the shortest tool path with chatter-free condition.

System identifiability in a time-evolving agent-based model.

Mathematical models are a valuable tool for studying and predicting the spread of infectious agents. The accuracy of model simulations and predictions invariably depends on the specification of model parameters. Estimation of these parameters is therefore extremely important; however, while some parameters can be derived from observational studies, the values of others are difficult to measure. Instead, models can be coupled with inference algorithms (i.e., data assimilation methods, or statistical filters), which fit model simulations to existing observations and estimate unobserved model state variables and parameters. Ideally, these inference algorithms should find the best fitting solution for a given model and set of observations; however, as those estimated quantities are unobserved, it is typically uncertain whether the correct parameters have been identified. Further, it is unclear what 'correct' really means for abstract parameters defined based on specific model forms. In this work, we explored the problem of non-identifiability in a stochastic system which, when overlooked, can significantly impede model prediction. We used a network, agent-based model to simulate the transmission of Methicillin-resistant staphylococcus aureus (MRSA) within hospital settings and attempted to infer key model parameters using the Ensemble Adjustment Kalman Filter, an efficient Bayesian inference algorithm. We show that even though the inference method converged and that simulations using the estimated parameters produced an agreement with observations, the true parameters are not fully identifiable. While the model-inference system can exclude a substantial area of parameter space that is unlikely to contain the true parameters, the estimated parameter range still included multiple parameter combinations that can fit observations equally well. We show that analyzing synthetic trajectories can support or contradict claims of identifiability. While we perform this on a specific model system, this approach can be generalized for a variety of stochastic representations of partially observable systems. We also suggest data manipulations intended to improve identifiability that might be applicable in many systems of interest.

A performance-related design for hot-mix asphalt (HMA) incorporating multiple design parameters

Traditionally, the mix design was majorly the trial and error process of optimising aggregate gradation and asphalt content. With the progress of concept and technology, the number of design gyrations (N des) has been gradually regarded as another important design variable. This research explored the potential advantage of mixes designed incorporating multiple design parameters. The paper is divided into two parts. Part 1 designed the varied aggregate gradations with different levels of N des to evaluate the effect of incorporating the N des variable in a traditional process. Part 2 further explored the possible combinations of multiple design parameters, involving binder type, N des, RAP addition and aggregate gradation. The result showed that by selecting a suitable level of N des, the performance of mixes with different aggregate gradations can be well balanced to a certain extent. Besides, the performance of HMAs may be largely improved and some marginal materials can be reutilised by selecting a suitable combination of multiple design parameters.

Asperity Size and Neighboring Segments Can Change the Frictional Response and Fault Slip Behavior: Insights From Laboratory Experiments and Numerical Simulations

AbstractAccurate assessment of the rate and state friction parameters of rocks is essential for producing realistic earthquake rupture scenarios and, in turn, for seismic hazard analysis. Those parameters can be directly measured on samples, or indirectly based on inversion of coseismic or postseismic slip evolution. However, both direct and indirect approaches require assumptions that might bias the results. Aiming to reduce the potential sources of bias, we take advantage of a downscaled analog model reproducing megathrust earthquakes. We couple the simulated annealing algorithm with quasi‐dynamic numerical models to retrieve rate and state parameters reproducing the recurrence time, rupture duration and slip of the analog model, in the ensemble. Then, we focus on how the asperity size and the neighboring segments' properties control the seismic cycle characteristics and the corresponding variability of rate and state parameters. We identify a tradeoff between (a–b) of the asperity and (a–b) of neighboring creeping segments, with multiple parameter combinations that allow mimicking the analog model behavior. Tuning of rate and state parameters is required to fit laboratory experiments with different asperity lengths. Poorly constrained frictional properties of neighboring segments are responsible for uncertainties of (a–b) of the asperity in the order of per mille. Roughly one order of magnitude larger uncertainties derive from asperity size. Those results provide a glimpse of the variability that rate and state friction estimates might have when used as a constraint to model fault slip behavior in nature.

Tensor radiomics: paradigm for systematic incorporation of multi-flavoured radiomics features.

Radiomics features hold significant value as quantitative imaging biomarkers for diagnosis, prognosis, and treatment response assessment. To generate radiomics features and ultimately develop signatures, various factors can be manipulated, including image discretization parameters (e.g., bin number or size), convolutional filters, segmentation perturbation, or multi-modality fusion levels. Typically, only one set of parameters is employed, resulting in a single value or "flavour" for each radiomics feature. In contrast, we propose "tensor radiomics" (TR) where tensors of features calculated using multiple parameter combinations (i.e., flavours) are utilized to optimize the creation of radiomics signatures. We provide illustrative instances of TR implementation in positron emission tomography-computed tomography (PET-CT), magnetic resonance imaging (MRI), and CT by leveraging machine learning (ML) and deep learning (DL) methodologies, as well as reproducibility analyses: (I) to predict overall survival (OS) in lung cancer (CT) and head and neck cancer (PET-CT), TR was employed by varying bin sizes. This approach involved use of a hybrid deep neural network called 'TR-Net' and two ML-based techniques for combining different flavours. (II) TR was constructed by incorporating different segmentation perturbations and various bin sizes to classify the response of late-stage lung cancer to first-line immunotherapy using CT images. (III) In MRI of glioblastoma (GBM), TR was implemented to generate multi-flavour radiomics features, enabling enhanced analysis and interpretation. (IV) TR was employed via multiple PET-CT fusions in head and neck cancer. Flavours based on different fusions were created using Laplacian pyramids and wavelet transforms. Our findings demonstrated that TR outperformed conventional radiomics features in lung cancer CT and head and neck cancer PET-CT images, significantly enhancing OS prediction accuracy. TR also improved classification of lung cancer response to therapy and exhibited notable advantages in reproducibility compared to single-flavour features in MR imaging of GBM. Moreover, in head and neck cancer, TR through multiple PET-CT fusions exhibited improved performance in predicting OS. We conclude that the proposed TR paradigm has significant potential to improve performance in different medical imaging tasks. By incorporating multiple flavours of radiomics features, TR overcomes limitations associated with individual features and shows promise in enhancing prognostic capabilities in clinical settings.

Comparing Canadian and Norwegian moisture indices for building climate adaptation

To evaluate the suitability of materials and solutions in building envelopes, it is necessary to quantify the relevant climate loads. The critical climate load is typically a combination of multiple parameters, such as temperature and precipitation. Climate indices may be used for finding critical climate loads, and their use helps guide design choices when adapting to local climates. The purpose of this study is to evaluate the suitability of the Canadian Moisture Index (MI) for use in Norway. The values of MI are linked to design recommendations in the Canadian building code, thus enabling a tangible link between index values and moisture design practice. MI has been calculated for 12 locations in Norway, and compared to two indices already in national use: the driving rain index (DRI) and wood decay potential index (WDPI). The applicability of a climate index as a design tool depends on (1) describing a relevant climate stress; (2) logical differentiation of values, and; (3) translating index values to design recommendations. These are fulfilled for MI in a Canadian context, thus making it applicable as a design tool. However, significant adaptation may be required for the index to be adopted to a Norwegian context. As MI and DRI have a similar field of application, introducing MI into a Norwegian context may therefore be redundant. A drawback with the Norwegian indices is the relative weak link between index values and design recommendations, thus further development of recommendations based on index values may improve their applicability as design tools.

Combination of Multiple MRI Parameters Related to Signal Intensity and Volume for Predicting Response to Neoadjuvant Chemoradiotherapy by Patients with Locally Advanced Rectal Cancer.

MRI of patients with locally advanced rectal cancer (LARC) can predict the pathological complete response (pCR) to preoperative chemoradiation therapy (CRT). Our purpose was to use MRI results to evaluate the diagnostic value of combined changes in signal intensity (SI) and volume (V) of patients with LARC for predicting pCR to CRT. This retrospective study on 100 patients with LARC analyzed clinical and imaging data that were collected from March, 2018, to March, 2020. Before and after CRT, T2-weighted (T2W), apparent diffusion coefficient (ADC), and contrast-enhanced T1-weighted (ceT1W) data were analyzed. Percent changes of V (%#916;V) and relative SI ratio (%#916;SIR) on different sequences were calculated. After CRT, patients had pathological confirmation as pCR or non-pCR. Data were analyzed using nonparametric tests and receiver operating characteristic (ROC) analysis. There were 34 pCR and 66 non-pCR patients. Except for ADC-%#916;SIR, the combined parameters and single parameters had a greater decrease in the pCR group. The combination of ADC-%#916;V and T2W-%#916;SIR had the greatest diagnostic value (AUC=0.85,cutoff=0.23%) and the combination of ADC-%ΔV% and #916;SIR had the best accuracy (89%, cutoff=44.11%). Except for T2W-%#916;V and T2W-%#916;SIR, the different sequences had moderate differences in diagnostic performance. The diagnostic performance of combined parameters or single parameters on ADC and T2W was significantly better than those on ceT1W (p#916;60;0.01). All sequences except ADC-%#916;SIR provided reliable predictions of pCR, although ceT1W data had limited usefulness.</p>.

Enhanced Detection Precision of the Taiji Program by Frequency Setting Strategy Based on a Hierarchical Optimization Algorithm

For space-based gravitational wave detection, a laser interferometric measurement system composed of a three-spacecraft formation offers the most rewarding bandwidth of astrophysical sources. There are no oscillators available that are stable enough so that each spacecraft could use its own reference frequency. The conversion between reference frequencies and their distribution between all spacecrafts for the synchronization of the different metrology systems is the job of the inter-spacecraft frequency setting strategy, which is important for continuously acquiring scientific data and suppressing measurement noise. We propose a hierarchical optimization algorithm to solve the frequency setting strategy. The optimization objectives are minimum total readout displacement noise and maximum beat-note frequency feasible range. Multiple feasible parameter combinations were obtained for the Taiji program. These optimized parameters include lower and upper bounds of the beat note, sampling frequency, pilot tone signal frequency, ultrastable clock frequencies, and modulation depth. Among the 20 Pareto optimal solutions, the minimum total readout displacement noise was 4.12 pm/Hz, and the maximum feasible beat-note frequency range was 23 MHz. By adjusting the upper bound of beat-note frequency and laser power transmitted by the telescope, we explored the effects of these parameters on the minimum total readout displacement noise and optimal local laser power in greater depth. Our results may serve as a reference for the optimal design of laser interferometry system instrument parameters and may ultimately improve the detection performance and continuous detection time of the Taiji program.

Assessing the quality of comparative genomics data and results with the cogeqc R/Bioconductor package

Abstract Comparative genomics has become an indispensable part of modern biology due to the advancements in high‐throughput sequencing technologies and the accumulation of genomic data in public databases. However, the quality of genomic data and the choice of parameters used in software tools used for comparative genomics can greatly impact the accuracy of results. Here, we present cogeqc, an R/Bioconductor package that provides researchers with a toolkit to assess genome assembly and annotation quality, orthogroup inference, and synteny detection. The package offers context‐guided assessments of assembly and annotation statistics by comparing observed statistics to those of closely‐related species on NCBI. To assess orthogroup inference, cogeqc calculates a protein domain‐aware orthogroup score that aims at maximising the number of shared protein domains within the same orthogroup. The assessment of synteny detection consists in representing anchor gene pairs as a synteny network and analysing its graph properties, such as clustering coefficient, node count, and scale‐free topology fit. The application of cogeqc to real datasets allowed for an evaluation of multiple parameter combinations for orthogroup inference and synteny detection, providing researchers in need for comparative genomics with guidelines to aid in the selection of the most appropriate tools and parameters for their specific data. We demonstrate that the default parameters in orthogroup identification and synteny detection tools are not always the most suitable, highlighting the importance of performing assessments for each dataset. The assessment metrics provided by cogeqc will help researchers generate more accurate and reliable results.

A novel multiscale process simulation to predict the impact of intrinsic heat treatment on local microstructure gradients and bulk hardness of AISI 4140 manufactured by laser powder bed fusion

ABSTRACT Although finite element model based process simulations for the laser powder bed fusion additive manufacturing process have become more common in the recent years, the proposed approaches are often only viable for materials without complex phase transformations. Process simulations for materials such as the quench and tempering steel AISI 4140 typically lead to higher computational cost due to the finer mesh and time steps needed for more complex material models. This study proposes a novel multiscale approach to combine the advantages of the macroscale and mesoscale models into one framework, in order to reduce computational cost while retaining the high accuracy. The implementation of these multiscale methods was validated by experimentally analyzing multiple parameter combinations regarding bulk hardness and local microstructure differences. The results show an accurate prediction of bulk hardness and localised tempering effects while reducing the computational cost in order to simulate the component scale.

A Systematic Study on the Processing Strategy in Femtosecond Laser Scribing via a Two-Temperature Model.

Balancing quality and productivity, especially deciding on the optimal matching strategy for multiple process parameters, is challenging in ultrashort laser processing. In this paper, an economical and new processing strategy was studied based on the laser scribing case. To reveal the temperature evolution under the combination of multiple process parameters in the laser scribing process, a two-temperature model involving a moving laser source was developed. The results indicated that the peak thermal equilibrium temperature between the electron and lattice increased with the increase in the laser fluence, and the temperature evolution at the initial position, influenced by subsequent pulses, was strongly associated with the overlap ratio. The thermal ablation effect was strongly enhanced with the increase in laser fluence. The groove morphology was controllable by selecting the overlap ratio at the same laser fluence. The removal volume per joule (i.e., energy utilization efficiency) and the removal volume per second (i.e., ablation efficiency) were introduced to analyze the ablation characteristics influenced by multiple process parameters. The law derived from statistical analysis is as follows; at the same laser fluence with the same overlap ratio, the energy utilization efficiency is insensitive to changes in the repetition rate, and the ablation efficiency increases as the repetition rate increases. As a result, a decision-making strategy for balancing quality and productivity was created.

An assets maintenance-workforce planning problem under uncertainty: A chance constraints assisted simulation-optimization approach

Preventive and corrective maintenance-workforce planning for assets is a growing area of research, given the presence of uncertainty. We model a Simulation-Optimization framework to solve this challenging large-scale maintenance-workforce planning problem while considering its stochastic nature inside both the simulation and optimization models. A holistic simulation model is proposed through multiple modules to imitate different aspects of the maintenance process, i.e., spare facilities/storage and workforce. The workforce module represents technicians’ types, skills and levels, and employment life-cycle, including recruitment, promotion, and separation/retirement. The present uncertainty in the simulation model demands its corresponding optimization model to cope with the nature of the problem and guarantee a degree of confidence level of the optimal decisions. Therefore, a Chance Constraints Programming (CCP) method is integrated with an evolutionary optimization algorithm to limit the risk level of violating the random constraints while minimizing the total maintenance cost. Discrete Event Simulation (DES) and Self-Adaptive Differential Evolution assisted with Chance Constraints (SA-DE-CC) algorithm are coupled to solve the current problem. The optimization model is proposed to optimize the preventive maintenance frequency and workforce planning problem while implicitly considering corrective maintenance in the simulation model. The Taguchi method performs scenario and risk analysis for multiple CC probability distributions (i.e., Weibull and Normal), confidence levels, and critical parameter combinations. The model’s sensitivity analysis and managerial insights are also discussed.

Using Reference Electrodes to Validate Parameterization of Individual Electrode Soh in the Single Particle Model with Electrolyte

Most model parametrization techniques for reduced order physics-based Lithium-ion battery models like the single particle model rely on minimizing the error in the measured and predicted cell terminal voltage. The battery terminal voltage is the difference between the voltages of the positive and negative electrode solid phase potentials. If the individual contribution of each electrode overpotential to the terminal voltage, is not measured, there could be multiple combinations of parameters that provide similar RMSE for voltage prediction, yielding non-unique solutions and local minima. Misattributing the parameters for kinetics and diffusion in each electrode could result in increased plating or over-conservative designs at higher operating currents.Adding a reference electrode to the battery allows us to measure the individual electrode potentials experimentally. Using this extra measurement, we can parametrize the kinetic and diffusion reactions in positive and negative electrodes with better confidence [1], and validate the electrode-specific state of health parameterization, specifically the increased resistances due to SEI growth and shift in the stoichiometric window as the cell ages [2,3]. In our experiments, we used a pouch cell manufactured in the U of M Battery Lab with Targray NMC 622 Single Crystal cathode powder, Superior SLC 1520-T anode powder, and a custom fabricated Lithium Iron Phosphate (LFP) reference electrode deposited on a porous electrode layer. Further details of the reference electrode are given in [4]. The model was parametrized using discharge data at different C-rates and a modified Hybrid Pulse Power Characterization test. Finally, the RMSE for both the modeled negative and positive electrode potentials are calculated for the validation data set which uses the US06 Drive cycle.

Study on Soil Parameter Evolution during Ultra-Large Caisson Sinking Based on Artificial Neural Network Back Analysis

The determination of soil parameters in geotechnical engineering and their variations during the construction process have long been a focal point for engineering designers. While the artificial neural network (ANN) has been employed for back analysis of soil parameters, its application to caisson sinking processes remains limited. This study focuses on the Nanjing Longtan Yangtze River Bridge project, specifically the south anchoring of an ultra-large rectangular caisson. A comprehensive analysis of the sinking process was conducted using 400 finite element method (FEM) models to obtain the structural stress and earth pressure at key locations. Multiple combinations of soil parameters were considered, resulting in a diverse set of simulation results. These results were then utilized as training samples to develop a back-propagating artificial neural network (BP ANN), which utilized the structural stress and earth pressure as input sets and the soil parameters as output sets. The BP ANN was individually trained for each stage of the sinking process. Subsequently, the trained ANN was employed to predict the soil parameters under different working conditions based on actual monitoring data from engineering projects. The obtained soil parameter variations were further analyzed, leading to the following conclusions: (1) The soil parameters estimated by the ANN exhibited strong agreement with the original values from the geological survey report, validating their reliability; (2) The surrounding soil during the caisson sinking exhibited three distinct states: a stable state prior to the arrival of the cutting edges, a strengthened state upon the arrival of the cutting edges, and a disturbed state after the passage of the cutting edges; (3) In the stable state, the soil parameters closely resembled the original values, whereas in the strengthened state, the soil strength and stiffness significantly increased, while the Poisson’s ratio decreased. In the disturbed state, the soil strength and stiffness were slightly lower than the original values. This study represents a valuable exploration of back analysis for caisson engineering. The findings provide important insights for similar engineering design and construction projects.

Si3N4-TiN rotary EDM optimization by Mo-Jaya algorithm with Pareto optimal solution, analysis of micro-structural and geometrical tolerances

Research into rotary electrical discharge machining on high temperature with biomedical application Si3N4-TiN ceramic composite is presented in this paper. Current (I), pulse on time (Ton), pulse off time (Toff), dielectric pressure (DP), speed and spark gap voltage (Sv) are some of the many performance characteristics. Among the factors taken into account is the material removal rate, surface roughness, electrode wear rate, cylindricity, perpendicularity, top radial overcut, bottom radial over cut and run out. Multiple parameter combinations were validated experimentally and the resulting reactions were examined. Mean effects analysis and regression analysis are used to investigate the impacts of individual parameters. To comprehend the instantaneous behavior of the replies, multi-objective Jaya optimization is utilized to optimize the responses simultaneously. The multi-objective problem's outcomes are shown in 3D charts, with each showing the Pareto optimal solution. From this real conclusion, the optimal combinations of answers are extracted and reported. The aggregate optimization result was also shown, which factored in all eight responses. MRR of 0.238 g/min was obtained which is a 10.6% improvement from the experimental values. Electrode wear of 0.0028 g/min was obtained showing a 6.6% reduction. Similarly reduction in values of Surface roughness, top radial overcut and bottom radial over cut, Circularity, Perpendicularity, run out was observed and the percentages are 3.4, 4.7, 4.5, 7.8, 10.0 and 10.53 respectively. Details on the structural and morphological examinations of the various surface abnormalities that occur during the process have been presented.

Systematic analysis of the impacts of symmetry energy parameters on neutron star properties

The impacts of various symmetry energy parameters on the properties of neutron stars (NSs) have been recently investigated, and the outcomes are at variance, as summarized in Table III of Phys. Rev. D 106, 063005 (2022). We have systematically analyzed the correlations of slope and curvature parameters of symmetry energy at the saturation density ($\rho_0=0.16 \text{fm}^{-3}$) with the tidal deformability and stellar radius of non-spinning neutron stars in the mass range of $1.2 - 1.6 M_\odot$ using a large set of minimally constrained equations of state (EoSs). The EoSs at low densities correspond to the nucleonic matter and are constrained by empirical ranges of a few low-order nuclear matter parameters from the finite nuclei data and the pure neutron matter EoS from chiral effective field theory. The EoSs at high densities ($\rho > 1.5 - 2\rho_0$) are obtained by a parametric form for the speed of sound that satisfies the causality condition. Several factors affecting the correlations between the NS properties and the individual symmetry energy parameters usually encountered in the literature are considered. These correlations are quite sensitive to the choice of the distributions of symmetry energy parameters and their interdependence. But, variations of NS properties with the pressure of $\beta -$ equilibrated matter at twice the saturation density remain quite robust which maybe due to the fact that the pressure depends on the combination of multiple nuclear matter parameters that describe the symmetric nuclear matter as well as the density dependence of the symmetry energy. Our results are practically insensitive to the behavior of EoS at high densities.

Atypical primary central nervous system lymphoma and glioblastoma: multiparametric differentiation based on non-enhancing volume, apparent diffusion coefficient, and arterial spin labeling

ObjectivesTo evaluate the multiparametric diagnostic performance with non-enhancing tumor volume, apparent diffusion coefficient (ADC), and arterial spin labeling (ASL) to differentiate between atypical primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM).MethodsOne hundred and fifty-eight patients with pathologically confirmed typical PCNSL (n = 59), atypical PCNSL (hemorrhage, necrosis, or heterogeneous contrast enhancement, n = 29), and GBM (n = 70) were selected. Relative minimum ADC (rADCmin), mean (rADCmean), maximum (rADCmax), and rADCmax-min (rADCdif) were obtained by standardization of the contralateral white matter. Maximum cerebral blood flow (CBFmax) was obtained according to the ASL-CBF map. The regions of interests (ROIs) were manually delineated on the inner side of the tumor to further generate a 3D-ROI and obtain the non-enhancing tumor (nET) volume. The area under the curve (AUC) was used to evaluate the diagnostic performance.ResultsAtypical PCNSLs showed significantly lower rADCmax, rADCmean, and rADCdif than that of GBMs. GBMs showed significantly higher CBFmax and nET volume ratios than that of atypical PCNSLs. Combined three-variable models with rADCmean, CBFmax, and nET volume ratio were superior to one- and two-variable models. The AUC of the three-variable model was 0.96, and the sensitivity and specificity were 90% and 96.55%, respectively.ConclusionThe combined evaluation of rADCmean, CBFmax, and nET volume allowed for reliable differentiation between atypical PCNSL and GBM.Key Points• Atypical PCNSL is easily misdiagnosed as glioblastoma, which leads to unnecessary surgical resection.• The nET volume, ADC, and ASL-derived parameter (CBF) were lower for atypical PCNSL than that for glioblastoma.• The combination of multiple parameters performed well (AUC = 0.96) in the discrimination between atypical PCNSL and glioblastoma.

Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions

Highly accurate simulations of complex phenomena governed by partial differential equations (PDEs) typically require intrusive methods and entail expensive computational costs, which might become prohibitive when approximating steady-state solutions of PDEs for multiple combinations of control parameters and initial conditions. Therefore, constructing efficient reduced order models (ROMs) that enable accurate but fast predictions, while retaining the dynamical characteristics of the physical phenomenon as parameters vary, is of paramount importance. In this work, a data-driven, non-intrusive framework which combines ROM construction with reduced dynamics identification, is presented. Starting from a limited amount of full order solutions, the proposed approach leverages autoencoder neural networks with parametric sparse identification of nonlinear dynamics (SINDy) to construct a low-dimensional dynamical model. This model can be queried to efficiently compute full-time solutions at new parameter instances, as well as directly fed to continuation algorithms. These aim at tracking the evolution of periodic steady-state responses as functions of system parameters, avoiding the computation of the transient phase, and allowing to detect instabilities and bifurcations. Featuring an explicit and parametrized modeling of the reduced dynamics, the proposed data-driven framework presents remarkable capabilities to generalize with respect to both time and parameters. Applications to structural mechanics and fluid dynamics problems illustrate the effectiveness and accuracy of the proposed method.