Standard Statistical Model Research Articles

Atomistic insights into argon clusters and nucleation dynamics

Accurate predictions of nucleation and atomic-level estimates of cluster properties in gas-phase chemical physics have proven challenging. These challenges arise from two primary sources: finite-size effects associated with nanoscopic particles and the emergence of non-standard thermodynamics, particularly at elevated temperatures. This study reexamines the formation of argon clusters using established methodologies such as atomistic simulations, configurational sampling, and statistical thermochemistry. To enhance the representation of condensed-phase argon, we employ an ab initio-based two-body potential, complemented by a three-body Axilrod–Teller potential. Additionally, we address the impact of anharmonicities on cluster stabilities using a recently developed extension to the standard statistical cluster model. The employed anharmonic model is rigorously benchmarked against molecular dynamics simulations. The subsequent analysis demonstrates a robust and consistent agreement between our model and experimental data. Our analysis covers nearly every experimental data point collected between 1971 and 2010, offering valuable insights into the predictive capabilities of the model. Moreover, in contrast to previous studies, our findings indicate that individual measurements are consistently in alignment with each other.

Machine learning for clinical outcome prediction in cerebrovascular and endovascular neurosurgery: systematic review and meta-analysis

BackgroundMachine learning (ML) may be superior to traditional methods for clinical outcome prediction. We sought to systematically review the literature on ML for clinical outcome prediction in cerebrovascular and endovascular...

Comparison of statistical low-frequency earthquake activity models

Slow earthquakes are slow fault slip events. Quantifying and monitoring slow earthquake activity characteristics are important, because they may change before large earthquakes occur. Statistical seismicity models are useful for quantifying seismicity characteristics. However, no standard statistical model exists for slow earthquake activity. This study used a high-quality catalog of low-frequency earthquakes (LFEs), a type of slow earthquake, in the Nankai subduction zone from April 2004 to August 2015 and conducted the first comparison of existing statistical LFE activity models to determine which model better describes LFE activity. Based on this comparison, this study proposes a new hybrid model that incorporates existing model features. The new model considers the LFE activity history in a manner similar to the epidemic-type aftershock sequence (ETAS) model and represents the LFE aftershock rate (subsequent LFE occurrence rate) with a small number of model parameters, as in the Omori–Utsu aftershock law for regular earthquakes. The results show that the proposed model outperforms other existing models. However, the new model cannot reproduce a feature of LFE activity: the sudden cessation of intense LFE bursts. This is because the new model superimposes multiple aftershock activities and predicts extremely high seismicity rates during and after the LFE bursts. I suggest that reproducing and successfully predicting the sudden cessation of intense LFE bursts is critical for the further improvement of statistical LFE activity models. In addition, the empirical equations formulated in this study for the LFE aftershock rates may be useful for future statistical and physical modeling of LFE activity.Graphical

Efficient 3D real-time adaptive AUV sampling of a river plume front

The coastal environment faces multiple challenges due to climate change and human activities. Sustainable marine resource management necessitates knowledge, and development of efficient ocean sampling approaches is increasingly important for understanding the ocean processes. Currents, winds, and freshwater runoff make ocean variables such as salinity very heterogeneous, and standard statistical models can be unreasonable for describing such complex environments. We employ a class of Gaussian Markov random fields that learns complex spatial dependencies and variability from numerical ocean model data. The suggested model further benefits from fast computations using sparse matrices, and this facilitates real-time model updating and adaptive sampling routines on an autonomous underwater vehicle. To justify our approach, we compare its performance in a simulation experiment with a similar approach using a more standard statistical model. We show that our suggested modeling framework outperforms the current state of the art for modeling such spatial fields. Then, the approach is tested in a field experiment using two autonomous underwater vehicles for characterizing the three-dimensional fresh-/saltwater front in the sea outside Trondheim, Norway. One vehicle is running an adaptive path planning algorithm while the other runs a preprogrammed path. The objective of adaptive sampling is to reduce the variance of the excursion set to classify freshwater and more saline fjord water masses. Results show that the adaptive strategy conducts effective sampling of the frontal region of the river plume.

Development and Internal Validation of Machine Learning Models to Predict Mortality and Disability After Mechanical Thrombectomy for Acute Anterior Circulation Large Vessel Occlusion

Mechanical thrombectomy (MT) improves outcomes in patients with LVO but many still experience mortality or severe disability. We sought to develop machine learning (ML) models that predict 90-day outcomes after MT for LVO. Consecutive patients who underwent MT for LVO between 2015-2021 at a Comprehensive Stroke Center were reviewed. Outcomes included 90-day favorable functional status (mRS 0-2), severe disability (mRS 4-6), and mortality. ML models were trained for each outcome using prethrombectomy data (pre) and with thrombectomy data (post). Three hundred and fifty seven patients met the inclusion criteria. After model screening and hyperparameter tuning the top performing ML model for each outcome and timepoint was random forest (RF). Using only prethrombectomy features, the AUCs for the RFpre models were 0.73 (95% CI 0.62-0.85) for favorable functional status, 0.77 (95% CI 0.65-0.86) for severe disability, and 0.78 (95% CI 0.64-0.88) for mortality. All of these were better than a standard statistical model except for favorable functional status. Each RF model outperformed Pre, SPAN-100, THRIVE, and HIAT scores (P<0.0001 for all). The most predictive features were premorbid mRS, age, and NIHSS. Incorporating MT data, the AUCs for the RFpost models were 0.80 (95% CI 0.67-0.90) for favorable functional status, 0.82 (95% CI 0.69-0.91) for severe disability, and 0.71 (95% CI 0.55-0.84) for mortality. RF models accurately predicted 90-day outcomes after MT and performed better than standard statistical and clinical prediction models.

Demand forecasting using a hybrid model based on artificial neural networks: A study case on electrical products

Purpose: This work aims to evaluate demand forecasting models to determine if using exogenous factors and machine learning techniques helps improve performance compared to univariate statistical models, allowing manufacturing companies to manage demand better.Design/methodology/approach: We implemented a multivariate Auto-Regressive Moving Average with eXogenous input (ARMAX) statistical model and a Neural Network-ARMAX (NN-ARMAX) hybrid model for forecasting. Later, we compared both to a standard univariate statistical model to forecast the demand for electrical products in a Colombian manufacturing company.Findings: The outcomes demonstrated that the NN-ARMAX model outperformed the other two. Indeed, demand management improved with the reduction of overstock and out-of-stock products.Research limitations/implications: The findings and conclusions in this work are limited to Colombian manufacturing companies that sell electrical products to the construction industry. Moreover, the experts from the company that provided us with the data also selected the external factors based on their own experiences, i.e., we might have disregarded potential factors.Practical implications: This work suggests that a model using neural networks and including exogenous variables can improve demand forecasting accuracy, promoting this approach in manufacturing companies dealing with demand planning issues.Originality/value: The findings in this work demonstrate the convenience of using the proposed hybrid model to improve demand forecasting accuracy and thus provide a reliable basis for its implementation in supply chain planning for the electrical/construction sector in Colombian manufacturing companies.

Systematic study of prescission neutron multiplicity: Revealing the role of entrance channel magicity

The prescission neutron multiplicities (νpre) are studied for a systematic understanding of the influence of entrance-channel shell closure in fusion-fission dynamics. The existing νpre data are analyzed for reactions with a wide variety of target and projectile combinations. For completeness, the measured νpre data for the fusion-fission reactions with the doubly closed shell 16O projectile on the targets having either proton (204,206Pb) or both proton and neutron (208Pb) shell closure is reanalyzed. Although the measured νpre for 224Th disagree with the available data reported by Rossner et al. (1992) [1], our results are found to be more consistent and follow a systematic trend as described in this letter. Theoretical analysis is performed within the standard statistical model framework, where dissipation strength is used as a tunable parameter. It is observed that irrespective of the compound nuclear excitation energy, the entrance channel magicity imparts an intriguing impact on the dissipation strength. A similar entrance channel effect in the evaporation residue cross-section is also demonstrated. In conclusion, the present analysis establishes an entanglement of the exit channel observables with the entrance channel shell closure. It contradicts the hypothesis of complete equilibrium in compound nuclear reactions.

Translation initiation consistency between in vivo and in vitro bacterial protein expression systems.

Strict on-demand control of protein synthesis is a crucial aspect of synthetic biology. The 5'-terminal untranslated region (5'-UTR) is an essential bacterial genetic element that can be designed for the regulation of translation initiation. However, there is insufficient systematical data on the consistency of 5'-UTR function among various bacterial cells and in vitro protein synthesis systems, which is crucial for the standardization and modularization of genetic elements in synthetic biology. Here, more than 400 expression cassettes comprising the GFP gene under the regulation of various 5'-UTRs were systematically characterized to evaluate the protein translation consistency in the two popular Escherichia coli strains of JM109 and BL21, as well as an in vitro protein expression system based on cell lysate. In contrast to the very strong correlation between the two cellular systems, the consistency between in vivo and in vitro protein translation was lost, whereby both in vivo and in vitro translation evidently deviated from the estimation of the standard statistical thermodynamic model. Finally, we found that the absence of nucleotide C and complex secondary structure in the 5'-UTR significantly improve the efficiency of protein translation, both in vitro and in vivo.

Development of an Epidemic‐Type Aftershock‐Sequence Model Explicitly Incorporating the Seismicity‐Triggering Effects of Slow Slip Events

AbstractSlow slip events (SSEs) at subduction zone plate boundaries sometimes trigger earthquake swarms and megathrust earthquakes. The causal relationship between SSEs and seismicity has been studied worldwide, but the epidemic‐type aftershock‐sequence (ETAS) model, which is a standard statistical model of seismicity, does not explicitly consider the seismicity‐triggering effect of SSEs. Therefore, if an SSE occurs at a plate boundary, probabilistic earthquake forecasts based on the ETAS model fail to predict observed seismicity. Here, we constructed a statistical model named the SSE‐modulated ETAS model by incorporating SSE moment rates estimated from observation data from the global navigation satellite system into the original ETAS model. Our model assumes a linear or power‐law relationship between the SSE moment rates and seismicity rates and estimates its proportionality constant as a new ETAS parameter. We applied this new model to three SSEs and M 2.5 or greater earthquakes in the shallow part of the Hikurangi Trench, New Zealand. The results show that it is better than the original ETAS model, giving a significant reduction in the Akaike information criterion. In addition, we examined the functional forms (e.g., lag time and power exponent) of the equation relating the moment rate of the SSEs to the seismicity rate. We also examine the influence of SSEs on aftershock productivity. Our model can improve short‐term forecasts of seismicity associated with SSEs if the detection and characterization of SSEs can be done in near real time. Our model is also useful for quantifying characteristics of SSE‐induced seismicity.

A special issue on Bayesian inference: challenges, perspectives and prospects.

A special issue on Bayesian inference: challenges, perspectives and prospects.

Generalized extreme value analysis of criticality tallies in Monte Carlo calculation

Generalized extreme value (GEV) is the standard statistical model on the occurrences of extreme observations. The trinity theorem in GEV asserts that properly normalized maxima from blocks of data converge in distribution to one of the Weibull, Gumbel and Fréchet distributions as the data size increases. In this work, the methodology of GEV is applied to criticality tallies in Monte Carlo fission source cycles in order to evaluate the utility value of the distribution tail ends. Numerical results obtained under a sufficiently large number of particles per cycle show that the extreme value index (EVI) in GEV is not capable of being differentiated for the upper and lower distribution tail ends of criticality tallies. It is also shown that the EVI of criticality tallies falls within the range of Weibull distribution including the EVI of Gumbel distribution as the role of a boundary value layer. As a demonstration aimed at practicality, GEV is utilized for the population diagnosis under an insufficient number of particles per cycle. It turns out that the transition from one equilibrium to other equilibrium due to small population size makes the EVIs of upper and lower distribution tail ends depart from each other so that one of them falls in the range of Weibull distribution and the other in that of Fréchet distribution.

Micro-CT Imaging and Mechanical Properties of Ovine Ribs.

The use of ovine animal models in the study of injury biomechanics and modeling is increasing, due to their favorable size and other physiological characteristics. Along with this increase, there has also been increased interest in the development of in silico ovine models for computational studies to compliment physical experiments. However, there remains a gap in the literature characterizing the morphological and mechanical characteristics of ovine ribs. The objective of this study therefore is to report anatomical and mechanical properties of the ovine ribs using microtomography (micro-CT) and two types of mechanical testing (quasi-static bending and dynamic tension). Using microtomography, young ovine rib samples obtained from a local abattoir were cut into approximately fourteen 38mm sections and scanned. From these scans, the cortical bone thickness and cross-sectional area were measured, and the moment of inertia was calculated to enhance the mechanical testing data. Based on a standard least squares statistical model, the cortical bone thickness varied depending on the region of the cross-section and the position along the length of the rib (p < 0.05), whereas the cross-sectional area remained consistent (p > 0.05). Quasi-static three-point bend testing was completed on ovine rib samples, and the resulting force-displacement data was analyzed to obtain the stiffness (44.67 ± 17.65 N/mm), maximum load (170.54 ± 48.28 N) and displacement at maximum load (7.19 ± 2.75mm), yield load (167.81 ± 48.12 N) and displacement at yield (6.10 ± 2.25mm), and the failure load (110.90 ± 39.30 N) and displacement at failure (18.43 ± 2.10mm). The resulting properties were not significantly affected by the rib (p > 0.05), but by the animal they originated from (p < 0.05). For the dynamic testing, samples were cut into coupons and tested in tension with an average strain rate of 18.9 strain/sec. The resulting dynamic testing properties of elastic modulus (5.16 ± 2.03 GPa), failure stress (63.29 ± 14.02MPa), and failure strain (0.0201 ± 0.0052) did not vary based on loading rate (p > 0.05).

Predictive Models to Evaluate the Interaction Effect of Soil-Tunnel Interaction Parameters on Surface and Subsurface Settlement

Nowadays, the need for subway tunnels has increased considerably with urbanization and population growth in order to facilitate movements. In urban areas, subway tunnels are excavated in shallow depths under densely populated areas and soft ground. Its associated hazards include poor ground conditions and surface settlement induced by tunneling. Various sophisticated variables influence the settlement of the ground surface caused by tunneling. The shield machine's operational parameters are critical due to the complexity of shield-soil interactions, tunnel geometry, and local geological parameters. Since all elements appear to have some effect on tunneling-induced settlement, none stand out as particularly significant; it might be challenging to identify the most important ones. This paper presents a new model of an artificial neural network (ANN) based on the partial dependency approach (PDA) to optimize the lack of explainability of ANN models and evaluate the sensitivity of the model response to tunneling parameters for the prediction of ground surface and subsurface settlement. For this purpose, 239 and 104 points for monitoring surface and subsurface settlement, respectively, were obtained from line Y, the west bond of Crossrail tunnels in London. The parameters of the ground surface, the trough, and the tunnel boring machine (TBM) were used to categorize the 12 potential input parameters that could impact the maximum settlement induced by tunneling. An ANN model and a standard statistical model of multiple linear regression (MLR) were also used to show the capabilities of the ANN model based on PDA in displaying the parameter's interaction impact. Performance indicators such as the correlation coefficient (R2), root mean square error (RMSE), and t-test were generated to measure the prediction performance of the described models. According to the results, geotechnical engineers in general practice should attend closely to index properties to reduce the geotechnical risks related to tunneling-induced ground settlement. The results revealed that the interaction of two parameters that have different effects on the target parameter could change the overall impact of the entire model. Remarkably, the interaction between tunneling parameters was observed more precisely in the subsurface zone than in the surface zone. The comparison results also indicated that the proposed PDA-ANN model is more reliable than the ANN and MLR models in presenting the parameter interaction impact. It can be further applied to establish multivariate models that consider multiple parameters in a single model, better capturing the correlation among different parameters, leading to more realistic demand and reliable ground settlement assessments. This study will benefit underground excavation projects; the experts could make recommendations on the criteria for settlement control and controlling the tunneling parameters based on predicted results. Doi: 10.28991/CEJ-2022-08-11-05 Full Text: PDF

Constraining the astrophysical p process: Cross section measurement of the Kr84(p,γ)Rb85 reaction in inverse kinematics

One of the biggest questions in nuclear astrophysics is understanding where the elements come from and how they are made. This work focuses on the p process, a nucleosynthesis process that consists of a series of photodisintegration reactions responsible for producing stable isotopes on the proton-rich side of stability. These nuclei, known as the p nuclei, cannot be made through the well-known neutron-capture processes. Currently p-process models rely heavily on theory to provide the relevant reaction rates to predict the final p-nuclei abundances and more experimental data is needed. The present work reports on an experiment performed with the SuN detector at the National Superconducting Cyclotron Laboratory, NSCL, at Michigan State University using the ReA facility to measure the $^{84}$Kr(p,$\gamma$)$^{85}$Rb reaction cross section in inverse kinematics. The reverse $^{85}$Rb($\gamma$,p)$^{84}$Kr reaction is a branching point in the p-process reaction network that was highlighted as an important reaction in sensitivity studies in the production of the $^{78}$Kr p nucleus. A new hydrogen gas target was designed and fabricated and a new analysis technique for background subtraction and efficiency calculations of the detector were developed. The experimental cross section is compared to standard statistical model calculations using the NON-SMOKER and TALYS codes.

Neural Network-Based Approach for Evaluating College English Teaching Methodology

A fair and scientific method of evaluating collegiate English instruction is currently lacking. Traditional methods are commonly used to evaluate the quality of college English education in terms of the standard statistical analysis evaluation model. As the evaluation of English teaching is a nonlinear issue, the above strategies have achieved some good results but have certain limitations. They are not scientific and objective in the selection of evaluation indicators, or in the setting of evaluation index weights, and there is a certain degree of subjectivity. Artificial neural network (ANN) is widely used in massive fields due to its characteristics of nonlinear processing, adaptive learning, and high fault tolerance. As a kind of neural network, BP neural network has strong nonlinear mapping ability, so it is feasible and scientific to solve the nonlinear relationship of college English teaching evaluation (ETE). Therefore, in this work, we first designed an ETE system index. Then, a strategy of college ETE with BP network is designed, which can carry out high-performance modeling for the designed teaching evaluation index. In order to alleviate the issue of slow convergence speed for BP network and fall into local optimum, this work also combines particle swarm algorithm with BP network to further improve network performance. Massive experiments have proved the reliability and effectiveness of this work.

Modeling the fusion process with a modified Woods-Saxon potential in Ar40 -induced fusion reactions

The evaporation cross sections in fusion reactions by $^{40}\mathrm{Ar}$ bombarding deformed nuclei were compared to calculations using a modified Woods-Saxon potential model together with the standard statistical model. The results show that the predictions overestimate the experimental yields of these reaction products up to two orders of magnitude as the charge number of compound nuclei (${Z}_{CN}\ensuremath{\ge}85$) increases, and suggest that the fusion process strongly depends on the orientation of the deformed target according to density-constrained time-dependent Hartree-Fock calculations.

LILITA_N21: Updated version of the Monte Carlo fusion–evaporation code

Since the code LILITA was presented in 1981 by Gomez Del Campo and Stockstad (1981)[1] and used to simulate the emission of light particles in the decay of a compound nucleus, relevant improvements have been made. Revisions leading to LILITA_N97 were begun by G. La Rana (Naples), M. Kaplan (CMU, Pittsburgh, PA), J.M. Alexander (Stony Brook, NY) and continued by the Naples Group (Moro et al., 2012) [2]. In this work new key features of the code consisting of the implementation of transmission coefficients based on global parametrization of the optical model potential, a description of the nuclear shape based on the nuclear stratosphere model, the parallelization of the code and a graphical user interface are presented. We expect that the new code version, LILITA_N21, makes it possible a better reproduction of wide data set from literature. In order to illustrate how this could be achieved, the standard statistical model predictions have been compared with those obtained with the new code features. A single set of parameters of the new code simultaneously well reproduces the neutron, proton and α-particle energy spectra, angular distributions, and multiplicities in the fusion–evaporation channel. By way of example, here the calculations are compared with experimental data of the reaction 60Ni+100Mo at the excitation energy of about 280MeV (Charity et al., 2003) [3]. The capability to reduce the execution time by exploiting the code parallelization is also discussed.

Machine Learning Frameworks to Predict Neoadjuvant Chemotherapy Response in Breast Cancer Using Clinical and Pathological Features.

Neoadjuvant chemotherapy (NAC) is used to treat locally advanced breast cancer (LABC) and high-risk early breast cancer (BC). Pathological complete response (pCR) has prognostic value depending on BC subtype. Rates of pCR, however, can be variable. Predictive modeling is desirable to help identify patients early who may have suboptimal NAC response. Here, we test and compare the predictive performances of machine learning (ML) prediction models to a standard statistical model, using clinical and pathological data. Clinical and pathological variables were collected in 431 patients, including tumor size, patient demographics, histological characteristics, molecular status, and staging information. A standard multivariable logistic regression (MLR) was developed and compared with five ML models: k-nearest neighbor classifier, random forest (RF) classifier, naive Bayes algorithm, support vector machine, and multilayer perceptron model. Model performances were measured using a receiver operating characteristic (ROC) analysis and statistically compared. MLR predictors of NAC response included: estrogen receptor (ER) status, human epidermal growth factor-2 (HER2) status, tumor size, and Nottingham grade. The strongest MLR predictors of pCR included HER2+ versus HER2- BC (odds ratio [OR], 0.13; 95% CI, 0.07 to 0.23; P < .001) and Nottingham grade G3 versus G1-2 (G1-2: OR, 0.36; 95% CI, 0.20 to 0.65; P < .001). The area under the curve (AUC) for the MLR was AUC = 0.64. Among the various ML models, an RF classifier performed best, with an AUC = 0.88, sensitivity of 70.7%, and specificity of 84.6%, and included the following variables: menopausal status, ER status, HER2 status, Nottingham grade, tumor size, nodal status, and presence of inflammatory BC. Modeling performances varied between standard versus ML classification methods. RF ML classifiers demonstrated the best predictive performance among all models.

Caveats with estimating natural mortality rates in stock assessment models using age aggregated catch data and abundance indices

We consider the challenge in estimating the natural mortality, M, in a standard statistical fish stock assessment model based on time series of catch- and abundance-at-age data. Though anecdotal evidence and empirical experience lend support to the fact that this parameter may be difficult to estimate, the current literature lacks a theoretical justification. We first discuss the estimatability of a time-invariant M theoretically and present necessary conditions for a constant M to be identifiable. We then investigate the practical usefulness of this by estimating M from simulated data based on models fitted to 19 fish stocks. Using the same data sets, we next explore several model formulations of time varying M, with a pre-specified mean value. Cross validation is used to assess the prediction performance of the candidate models. Our results show that a time-invariant M can be estimated with reasonable precision for a few stocks with long time series and typically high values of the true M. For most stocks, however, the estimation uncertainty of M is very large. For time-varying M, we find that accounting for variability across age and time using a simple model significantly improves the performance compared to a time-invariant M. No significant improvement is obtained by using complex models, such as, those with time dependencies in variability around mean values of M.

Experimental study of a virtual allowables approach for the design of composite aircraft structures

Statistically derived allowables are representative for the behavior of a composite material system in a given structural context where data variability needs to be contemplated. In particular, they define the strength of the material as characterized by various coupon tests according to ASTM standard procedure subjected to the actual manufacturing process and layup stacking sequences. The current paper presents a detailed comparison between virtual allowables predictions obtained using MSC Digimat and the experimentally determined values of a certified material backed up by a 3x2x3 (batch/panel/specimen) stochastic approach. Tests generate the required data for building material model ab initio of carbon fibers reinforced epoxide resin in form of 2x2 twill woven fabric used for qualifying an innovative composite wing for the Next Generation Tilt Rotor (NGCTR). Numerical models of in-plane tension, compression and shear test methods have been assembled along with an associated material model allowing numerical predictions to be validated with the coupon level experimental results as final purpose. Standard statistical model has been adopted in order to include material and manufacturing process variabilities to define B-basis allowables. The calibrated statistical FEM method performed for different layup configurations have been used to fully characterize the mechanical behavior of the analyzed CFRP material and to predict performances for thicker laminates.