Complex Model Research Articles

SPEEDY-NEMO: performance and applications of a fully-coupled intermediate-complexity climate model

A fully-coupled general circulation model of intermediate complexity is documented. The study presents an overview of the model climatology and variability, with particular attention to the phenomenology of processes that are relevant for the predictability of the climate system on seasonal-to-decadal time-scales. It is shown that the model can realistically simulate the general circulation of the atmosphere and the ocean, as well as the major modes of climate variability on the examined time-scales: e.g. El Niño-Southern Oscillation, North Atlantic Oscillation, Tropical Atlantic Variability, Pacific Decadal Variability, Atlantic Multi-decadal Variability. Potential applications of the model are discussed, with emphasis on the possibility of generating sets of low-cost large-ensemble retrospective forecasts. We argue that the presented model is suitable to be employed in traditional and innovative model experiments that can play a significant role in future developments of seasonal-to-decadal climate prediction.

The Role of the Mineralogical Composition on Wettability via Flotation Test and Surface Complexation Modeling (SCM)

Minerals are the chief constituents of rocks and have varied properties, such as the surface area, surface charge, site density, etc. Hence, numerous interactions are bound to occur in a reservoir during rock–fluid (i.e., rock, crude oil and brine) interactions. This study seeks to assess the role of the mineralogical composition in the wettability of sandstone rocks (SRs) and mineral mixture (MM) using both surface complexation modeling (SCM) and a flotation test. From the considered sandstone rocks, both the experimental results and the simulated counterparts revealed that the SRs were preferentially hydrophilic. For the MM, when the mass fraction of the hydrophobic mineral was increased, the affinity of the MM became slightly hydrophobic, and vice versa. For the dominant sandstone reservoir rock minerals with predominantly negatively charged surfaces, negligible oil adsorption took place due to the interfacial repulsive forces at the oil–brine and mineral–brine interfaces. For the MM with low calcite content, the wetting preference was influenced by the mineral with a prominent surface area. Our developed model portrayed that the main mechanism of oil adhesion onto sandstone minerals was divalent cation bridging. Nonetheless, adhesion of carboxylate (>COO−) onto the illite, montmorillonite and calcite sites also took place, with the latter being more pronounced.

Average decayed dynamics of one-step transformation process with randomly varying return transition rate

The problem of stochastic averaging of the decayed dynamics of the output state population of a one-step transformation process with constant deterministic forward transition rate and randomly varying return transition rate is solved in approximation where random variation is modeled as a dichotomous stochastic process. The form of the obtained solution represented as a product between bimodal sigmoid rise of average population and its unimodal exponential decay is shown to largely be dependent on the stochastic frequency and amplitude parameters. For example, at high stochastic frequency, the behavior of population is reduced to that of a decayed one-step deterministic system. However, for resonance stochastic amplitude at low stochastic frequency, such behavior coincides with that of three-exponential rise-decay kinetics typical rather of a three-step deterministic slowly decaying process. Thus, there is an equivalence between using a more complex deterministic kinetic model and a less complex stochastic kinetic model for describing the decayed dynamics of different irreversible systems.

The smart effort to build up an innovative organizational culture in a skincare company

Purpose – The research objective was to examine the predominant errand of work engagement (WE) and the physical work environment (PWE) in the development of innovative organizational culture (IOC) by contemplating the mediating factors of innovative work behaviour (IWB) and employee performance (EP).Design/methodology/approach – The research was conducted through a questionnaire survey of all employees of PT. Alzena Skincare Indonesia, 175 people who produced quantitative primary data. Through the PLS-SEM high-level analysis, the validity and reliability of the questionnaire were checked, and each research hypothesis was evaluated in detail.Findings – The research results showed that an ‘IOC’ in a skincare company can be developed, and ‘WE’ is the principal aspect. However, it was still essential for the company to heed the ‘PWE’. The partial mediating role of ‘IWB’ and ‘EP’ accelerated the formation of an ‘IOC’. Meanwhile, serial mediation of ‘IWB’ and ‘EP’ only supported ‘WE’ in building ‘IOC’, not the ‘PWE’.Research limitations/implications – This research was still limited to PT. Alzena Skincare Indonesia employees, so the results cannot be generalized to other types of businesses. Thus, further research is compulsory to test the research model on other objects and look for other factors that can encourage creation of an innovative organizational culture.Practical implications – The practical implication of this research was that skincare company managers need to build ‘IOC’ by strengthening ‘WE’ and improving the ‘PWE’ that supports it. These two factors created ‘IWB’ and improved ‘EP’, ultimately forming an ‘IOC’.Originality/value – It still needed to uncover research that examined the effect of ‘WE’ and the ‘PWE’ on ‘IOC’, especially if it was serially mediated by ‘IWB’ and ‘EP’. So, the complex model was the main novelty of this study.

Explainable artificial intelligence prediction-based model in laparoscopic liver surgery for segments 7 and 8: an international multicenter study

BackgroundArtificial intelligence (AI) is becoming more useful as a decision-making and outcomes predictor tool. We have developed AI models to predict surgical complexity and the postoperative course in laparoscopic liver surgery for segments 7 and 8.MethodsWe included patients with lesions located in segments 7 and 8 operated by minimally invasive liver surgery from an international multi-institutional database. We have employed AI models to predict surgical complexity and postoperative outcomes. Furthermore, we have applied SHapley Additive exPlanations (SHAP) to make the AI models interpretable. Finally, we analyzed the surgeries not converted to open versus those converted to open.ResultsOverall, 585 patients and 22 variables were included. Multi-layer Perceptron (MLP) showed the highest performance for predicting surgery complexity and Random Forest (RF) for predicting postoperative outcomes. SHAP detected that MLP and RF gave the highest relevance to the variables “resection type” and “largest tumor size” for predicting surgery complexity and postoperative outcomes. In addition, we explored between surgeries converted to open and non-converted, finding statistically significant differences in the variables “tumor location,” “blood loss,” “complications,” and “operation time.”ConclusionWe have observed how the application of SHAP allows us to understand the predictions of AI models in surgical complexity and the postoperative outcomes of laparoscopic liver surgery in segments 7 and 8.

Born this way or formed this way? Distal personality traits and proximal self-efficacy of Malaysian students and their academic performance

It has been acknowledged that academic performance has important consequences in one’s career, thus, a better understanding of both distal and proximal predictors deserves consideration. Based on social cognitive theory, this study contributes to the limited research investigating the academic performance of university students in Malaysia using the trait model which considers the mediation of self-efficacy (proximal characteristic) in the relationship between student personality (distal trait) and academic performance (outcomes). In a sample of 264 participants, self-efficacy positively relates to academic performance and positively mediated effects of all traits (except neuroticism) on academic performance. Contrary to past research, conscientiousness, extraversion, and agreeableness do not exert direct effects on academic achievement but instead through self-efficacy. Openness to experience turned out to be the strongest predictor pointing to a need for in-depth investigations into this dimension and for more complex model incorporating other proximal attributes in predicting academic performance in future research.

Converting between the International Prostate Symptom Score (IPSS) and the Expanded Prostate Cancer Index Composite (EPIC) urinary subscales: modeling and external validation

BackgroundProstate-related quality of life can be assessed with a variety of different questionnaires. The 50-item Expanded Prostate Cancer Index Composite (EPIC) and the International Prostate Symptom Score (IPSS) are two widely used options. The goal of this study was, therefore, to develop and validate a model that is able to convert between the EPIC and the IPSS to enable comparisons across different studies.MethodsThree hundred forty-seven consecutive patients who had previously received radiotherapy and surgery for prostate cancer at two institutions in Switzerland and Germany were contacted via mail and instructed to complete both questionnaires. The Swiss cohort was used to train and internally validate different machine learning models using fourfold cross-validation. The German cohort was used for external validation.ResultsConverting between the EPIC Urinary Irritative/Obstructive subscale and the IPSS using linear regressions resulted in mean absolute errors (MAEs) of 3.88 and 6.12, which is below the respective previously published minimal important differences (MIDs) of 5.2 and 10 points. Converting between the EPIC Urinary Summary and the IPSS was less accurate with MAEs of 5.13 and 10.45, similar to the MIDs. More complex model architectures did not result in improved performance in this study. The study was limited to the German versions of the respective questionnaires.ConclusionsLinear regressions can be used to convert between the IPSS and the EPIC Urinary subscales. While the equations obtained in this study can be used to compare results across clinical trials, they should not be used to inform clinical decision-making in individual patients.Trial registrationThis study was retrospectively registered on clinicaltrials.gov on January 14th, 2022, under the registration number NCT05192876.

Comment on ‘The Generation of Eocene Mafic Dike Swarms During the Exhumation of a Core Complex, Biarjmand Area, NE Iran’ by Azizi et al. (2023), Journal of Petrology, 64, 1–18

Abstract Azizi, H., Daneshvar, N., Asahara, Y., Minami, M. & Anma, R. (2023). The generation of Eocene mafic dike swarms during the exhumation of a Core complex, Biarjmand area, NE Iran. Journal of Petrology64, 1–18 have attributed the E–W-oriented mafic dike swarm in the Biarjmand metamorphic core complex to an Eocene extensional event which is much younger than a previously suggested Late Jurassic–Early Cretaceous age. They proposed that the emplacement of these dikes occurred in a rapid extensional regime coeval with the exhumation of the core complex after gravitational instability in the Central Iran/Eurasia collision zone. I appreciate the opportunity this article provides to shed light on specific aspects of the Late Mesozoic–Early Cenozoic continental extension within the Eurasian sector of the Neotethys subduction system. However, I here bring to attention certain discrepancies within Azizi et al.'s publication. Specifically, the assignment of an Eocene age to the emplacement of the E–W-oriented dike swarm, even though purportedly supported by U–Pb zircon dating, appears to be at odds with field observations and previously published geochronological data. Furthermore, the article contains internal contradictions in its presentation of the core complex model for the study area. It is important to note that Malekpour-Alamdari, A., Axen, G. J., Heizler, M. T. & Hassanzadeh, J. (2017). Large-magnitude continental extension in the northeastern Iranian plateau: insight from K-feldspar 40Ar/39Ar thermochronology from the Shotor Kuh-Biarjmand metamorphic core complex. Geosphere13, 1207–1233 previously documented the geochronological-based metamorphic core complex model of the area. Regrettably, despite its direct relevance, these earlier works have not been acknowledged in Azizi et al.'s paper. In this comment, I outline the problems with the structural and regional geology, the zircon U–Pb age of the dike samples, the age of the dike swarm, and the geodynamic interpretations in Azizi et al.'s work.

Subsampling under distributional constraints

AbstractSome complex models are frequently employed to describe physical and mechanical phenomena. In this setting, we have an input in a general space, and an output where is a very complicated function, whose computational cost for every new input is very high, and may be also very expensive. We are given two sets of observations of , and of different sizes such that only is available. We tackle the problem of selecting a subset of smaller size on which to run the complex model , and such that the empirical distribution of is close to that of . We suggest three algorithms to solve this problem and show their efficiency using simulated datasets and the Airfoil self‐noise data set.

Multitargets Joint Training Lightweight Model for Object Detection of Substation.

The object detection of the substation is the key to ensuring the safety and reliable operation of the substation. The traditional image detection algorithms use the corresponding texture features of single-class objects and would not handle other different class objects easily. The object detection algorithm based on deep networks has generalization, and its sizeable complex backbone limits the application in the substation monitoring terminals with weak computing power. This article proposes a multitargets joint training lightweight model. The proposed model uses the feature maps of the complex model and the labels of objects in images as training multitargets. The feature maps have deeper feature information, and the feature maps of complex networks have higher information entropy than lightweight networks have. This article proposes the heat pixels method to improve the adequate object information because of the imbalance of the proportion between the foreground and the background. The heat pixels method is designed as a kind of reverse network calculation and reflects the object's position to the pixels of the feature maps. The temperature of the pixels indicates the probability of the existence of the objects in the locations. Three different lightweight networks use the complex model feature maps and the traditional tags as the training multitargets. The public dataset VOC and the substation equipment dataset are adopted in the experiments. The experimental results demonstrate that the proposed model can effectively improve object detection accuracy and reduce the time-consuming and calculation amount.

Accounting for unobserved population dynamics and aging error in close-kin mark-recapture assessments.

Obtaining robust estimates of population abundance is a central challenge hindering the conservation and management of many threatened and exploited species. Close-kin mark-recapture (CKMR) is a genetics-based approach that has strong potential to improve the monitoring of data-limited species by enabling estimates of abundance, survival, and other parameters for populations that are challenging to assess. However, CKMR models have received limited sensitivity testing under realistic population dynamics and sampling scenarios, impeding the application of the method in population monitoring programs and stock assessments. Here, we use individual-based simulation to examine how unmodeled population dynamics and aging uncertainty affect the accuracy and precision of CKMR parameter estimates under different sampling strategies. We then present adapted models that correct the biases that arise from model misspecification. Our results demonstrate that a simple base-case CKMR model produces robust estimates of population abundance with stable populations that breed annually; however, if a population trend or non-annual breeding dynamics are present, or if year-specific estimates of abundance are desired, a more complex CKMR model must be constructed. In addition, we show that CKMR can generate reliable abundance estimates for adults from a variety of sampling strategies, including juvenile-focused sampling where adults are never directly observed (and aging error is minimal). Finally, we apply a CKMR model that has been adapted for population growth and intermittent breeding to two decades of genetic data from juvenile lemon sharks (Negaprion brevirostris) in Bimini, Bahamas, to demonstrate how application of CKMR to samples drawn solely from juveniles can contribute to monitoring efforts for highly mobile populations. Overall, this study expands our understanding of the biological factors and sampling decisions that cause bias in CKMR models, identifies key areas for future inquiry, and provides recommendations that can aid biologists in planning and implementing an effective CKMR study, particularly for long-lived data-limited species.

Elicitation and aggregation of multimodal estimates improve wisdom of crowd effects on ordering tasks

We present a wisdom of crowds study where participants are asked to order a small set of images based on the number of dots they contain and then to guess the respective number of dots in each image. We test two input elicitation interfaces—one elicits the two modalities of estimates jointly and the other independently. We show that the latter interface yields higher quality estimates, even though the multimodal estimates tend to be more self-contradictory. The inputs are aggregated via optimization and voting-rule based methods to estimate the true ordering of a larger universal set of images. We demonstrate that the quality of collective estimates from the simpler yet more computationally-efficient voting methods is comparable to that achieved by the more complex optimization model. Lastly, we find that using multiple modalities of estimates from one group yields better collective estimates compared to mixing numerical estimates from one group with the ordinal estimates from a different group.

Aerodynamic shape optimization using design-variables-screening method

Aerodynamic shape optimization involving a complex geometric model or problem may have tens or hundreds of design variables, necessitating multiple accurate but time-consuming computational fluid dynamics simulations to produce optimal designs, which greatly affects the efficiency of optimization and. To address this challenge, this article proposes an efficient optimization method based on design-variables-screening. Within the framework of the method, a complicated input–output relationship is broken down into quantitative effects. The influence of design variables on the objective function is calculated by the Kriging regression model and functional analysis of variance. In the meantime, a screening strategy is proposed to facilitate the selection of design variables for optimization. The less important design variables in the problems of interest are fixed so that the dimensionality of the problems is reduced to save computational cost. Experimental results on the National Advisory Committee for Aeronautics airfoil (NACA0012) demonstrate that the simplified model with the screening strategy achieves nearly the same reduction in drag coefficient as the conventional method that optimizes all design variables. Moreover, it significantly enhances the efficiency of optimization and contributes to the enhancement of flow stability.

Reduced-Order Modeling in Rotordynamics and Its Robustness to Random Matrix Perturbation

Abstract The aim of this paper is to apply and compare four reduced-order modeling strategies to compute the unbalanced vibration response of a rotating machine and evaluate its robustness to random matrix perturbation. The full finite element model of the rotor is built using a rotordynamic open source software (ROSS), and is reduced through different methods, namely: (1) modal reduction, (2) Krylov subspace, (3) Guyan reduction, and (4) system equivalent reduction–expansion process (SEREP). To evaluate the robustness of the obtained results, this paper proposes to perturb the stiffness matrix obtained using the reduced-order models applying the random matrix theory. A simple rotor (three discs) and a more complex gas turbine model (21 discs) are analyzed. Results show that Guyan is not the most appropriate reduction technique for the systems analyzed, but the other three strategies yield good results. In addition, the same random perturbation in the reduced-order stiffness matrices produces a similar level of uncertainty on the stochastic unbalanced responses.

Cloud and droplet dynamics during a human coughing event under fully mixed and unmixed room conditions

The study of cloud and droplet dynamics during potential transmission events, such as coughing, is essential for understanding the spread and deposition of aerosols and droplets carrying airborne diseases. This paper reports the refinement of a complex model that couples momentum, temperature, and humidity for accurately simulating the dynamics of aerosol clouds and the dispersion of larger droplets under various conditions within an environmental chamber. The model is then employed to quantify aerosol/droplet exposure of a person standing 1 m away from a host. In addition, a statistical framework sheds light on the impact of backward coupling (droplet to cloud), which is negligible compared to forward coupling (cloud to droplet). The near-field study also provides detailed information on droplet behavior, laying the foundation for large-scale far-field studies.

Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Sets.

One of the potential solutions for model interpretation is to train a surrogate model: a more transparent model that approximates the behavior of the model to be explained. Typically, classification rules or decision trees are used due to their logic-based expressions. However, decision trees can grow too deep, and rule sets can become too large to approximate a complex model. Unlike paths on a decision tree that must share ancestor nodes (conditions), rules are more flexible. However, the unstructured visual representation of rules makes it hard to make inferences across rules. In this paper, we focus on tabular data and present novel algorithmic and interactive solutions to address these issues. First, we present Hierarchical Surrogate Rules (HSR), an algorithm that generates hierarchical rules based on user-defined parameters. We also contribute SuRE, a visual analytics (VA) system that integrates HSR and an interactive surrogate rule visualization, the Feature-Aligned Tree, which depicts rules as trees while aligning features for easier comparison. We evaluate the algorithm in terms of parameter sensitivity, time performance, and comparison with surrogate decision trees and find that it scales reasonably well and overcomes the shortcomings of surrogate decision trees. We evaluate the visualization and the system through a usability study and an observational study with domain experts. Our investigation shows that the participants can use feature-aligned trees to perform non-trivial tasks with very high accuracy. We also discuss many interesting findings, including a rule analysis task characterization, that can be used for visualization design and future research.

A probabilistic solution to geophysical inverse problems in complex variables and its application to complex resistivity imaging

SUMMARY We introduce a novel probabilistic framework for the solution of non-linear geophysical inverse problems in complex variables. By using complex probability distributions, this approach can simultaneously account for individual errors of real and imaginary data parts, independently regularize real and imaginary parts of the complex model, and still take into account cross-sensitivities resulting from a complex forward calculation. The inverse problem is solved by means of optimization. An application of the framework to complex resistivity (CR) imaging demonstrates its advantages over the established inversion approach for CR measurements. We show that CR data, with real and imaginary parts being subject to different errors, can be fitted adequately, accounting for the individual errors and applying independent regularization to the real and imaginary part of the subsurface conductivity. The probabilistic framework itself serves as a basis for the future application of global sampling approaches, such as Markov chain Monte Carlo methods.

From germline genome to highly fragmented somatic genome: genome-wide DNA rearrangement during the sexual process in ciliated protists

Genomes are incredibly dynamic within diverse eukaryotes and programmed genome rearrangements (PGR) play important roles in generating genomic diversity. However, genomes and chromosomes in metazoans are usually large in size which prevents our understanding of the origin and evolution of PGR. To expand our knowledge of genomic diversity and the evolutionary origin of complex genome rearrangements, we focus on ciliated protists (ciliates). Ciliates are single-celled eukaryotes with highly fragmented somatic chromosomes and massively scrambled germline genomes. PGR in ciliates occurs extensively by removing massive amounts of repetitive and selfish DNA elements found in the silent germline genome during development of the somatic genome. We report the partial germline genomes of two spirotrich ciliate species, namely Strombidium cf. sulcatum and Halteria grandinella, along with the most compact and highly fragmented somatic genome for S. cf. sulcatum. We provide the first insights into the genome rearrangements of these two species and compare these features with those of other ciliates. Our analyses reveal: (1) DNA sequence loss through evolution and during PGR in S. cf. sulcatum has combined to produce the most compact and efficient nanochromosomes observed to date; (2) the compact, transcriptome-like somatic genome in both species results from extensive removal of a relatively large number of shorter germline-specific DNA sequences; (3) long chromosome breakage site motifs are duplicated and retained in the somatic genome, revealing a complex model of chromosome fragmentation in spirotrichs; (4) gene scrambling and alternative processing are found throughout the core spirotrichs, offering unique opportunities to increase genetic diversity and regulation in this group.

Convergent close-coupling calculations of positron scattering from neon and argon

Positron scattering from the noble gas atoms neon and argon is calculated using the single-center convergent close-coupling (CCC) method. Unlike previous CCC calculations, we have relaxed the frozen-core approximation by fully opening the outermost s and p shells, and find better agreement with experiment for excitation energies and dipole polarizability. We have used this improved structure model to calculate elastic, elastic differential, total, momentum-transfer, electron-loss, and total bound excitation cross sections. We have applied a complex model potential calculation, scaled to our single-center CCC results, to obtain direct ionization and positronium-formation cross sections for these systems. This complex model potential was also used to calculate results between the positronium-formation and ionization thresholds, giving complete results from 10-5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10^{-5}$$\\end{document} eV to 5000 eV. Overall, good agreement is observed with past theory and experiment for most transitions.Graphic abstract

Modeling and Control of a Linear Piezoelectric Actuator

To improve the output displacement of piezoelectric actuators, a linear piezoelectric actuator based on a multistage amplifying mechanism with a small volume, large thrust, high resolution, high precision, and fast response speed is proposed. However, inherent nonlinear characteristics, such as hysteresis and creep, significantly affect the output accuracy of piezoelectric actuators and may cause system instability. Therefore, a complex nonlinear hysteresis mathematical model with a high degree of fit was established. A Play operator was introduced into the backpropagation neural network, and a genetic algorithm (GA) was used to reduce the probability of the fitting of the neural network model falling into a local minimum. Moreover, simulation and experimental test platforms were constructed. The results showed that the maximum displacement of the actuator was 558.3 μm under a driving voltage of 150 V and a driving frequency of 1 Hz. The complex GA-BP neural network model of the piezoelectric actuator not only exhibited high modeling accuracy but also solved the problems of strong randomness and slow convergence. Compared with other control algorithms, the GA-BP fuzzy PID control exhibited higher control precision.