Elastic Model Research Articles

Study of fluid-elastic instability of the spatially bent tube structure in fast reactor intermediate heat exchanger

This study conducts a numerical simulation investigation on the flow field characteristics and fluid-elastic instability of the spatially bent tube structure in the intermediate heat exchanger (IHX) of a fast reactor. The inherent frequencies of the unique structure of the IHX spatially bent tube are calculated by using the wet modal method. They are employing Computational Fluid Dynamics (CFD) in conjunction with a fluid-structure coupling model, establishing an elastic tube model. Considering the bundle concentric circles arrangement, the “transition” arrangement is selected as the model to simulate the fluid-elastic instability. By the analysis of critical flow velocity, displacement, and the main vibration direction, the results indicate that the fluid-elastic instability of the tube bundle with a transition arrangement is mainly manifested by more severe vibrations in the bent tube segment. The instability flow velocity in the straight tube segment, ranging from 4.64 to 4.84 m/s, is greater than the instability flow velocity of 2.85 m/s in the bent tube segment. This study provides theoretical support for the design and operation of the intermediate heat exchanger in fast reactors, offering references for considering tube bundle arrangement and fluid-elastic instability in engineering practice.

Separation of source, attenuation and site parameters of 2 moderate earthquakes in France: an elastic radiative transfer approach

Summary An accurate magnitude estimation is necessary to properly evaluate seismic hazard, especially in low to moderate seismicity areas such as Metropolitan France. However, magnitudes of small earthquakes are subject to large uncertainties caused by major high-frequency propagation effects which are generally not properly considered. To address this issue, we developed a method to separate source, attenuation and site parameters from the elastic radiative transfer modeling of the full energy envelopes of seismograms. The key feature of our approach is the treatment of attenuation -both scattering and absorption- in a simple but realistic velocity model of the Earth’s lithosphere, including a velocity discontinuity at the Moho. To reach this goal, we developed a 2-step inversion procedure, allowing first to extract attenuation parameters for each source-station path from the whole observed energy envelope using the Levenberg-Marquardt and grid-search algorithms, then to determine site amplification and the source displacement spectrum from which the moment magnitude Mw is extracted. In the first step, we use the forward modeling procedure of Heller et al. (2022) in order to simulate energy envelopes by taking into account the full treatment of wave polarization, the focal mechanism of the source and the scattering anisotropy. The inversion procedure is then applied to the 2019 ML 5.2 Le Teil and 2014 ML 4.5 Lourdes earthquakes which both occurred in southern France. Data from 6 stations are selected for each event. The inversion results confirm a significant variability in the attenuation parameters (scattering and absorption) at regional scale and a strong frequency dependence. Scattering appears to be stronger towards the French Alps and Western Pyrenees. Absorption is stronger as frequency increases. Although not very resolvable, the mechanism of scattering appears to be forward or very forward. By inverting the source spectrum, we determine moment magnitudes Mw of 5.02 ± 0.17 for the Le Teil earthquake and 4.17 ± 0.15 for the Lourdes earthquake.

Using Artificial Intelligence to Predict Mortality in AKI Patients: A systematic review/Meta-Analysis

Abstract Background Acute kidney injury (AKI) is associated with increased morbidity/mortality. With artificial intelligence (AI), more dynamic models for mortality prediction in AKI patients have been developed using machine learning (ML) algorithms. The performance of various ML models were reviewed in their ability to predict in-hospital mortality (IHM) for AKI patients. Methods A literature search was conducted through Pubmed, Embase, and Web of Science Databases. Included studies contained variables regarding the efficacy of the AI model (AUC, accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV)). Only original studies which consisted of cross-sectional studies, prospective and retrospective studies were included, while reviews and self-reported outcomes were excluded. There was no restriction on time and geographic location. Results 8 studies with 37 032 AKI patients were included with a mean age of 65.3 years. The IHM was 18.0% in the derivation and 15.8% in the validation cohorts. The pooled (95% CI) AUC was observed to be highest for Broad Learning System (BLS) Models: [0.852 (0.820–0.883) and Elastic Net Final Model (ENF) [0.852 (0.813–0.891)] and lowest for proposed clinical model (PCM) [0.765 (0.716–0.814)]. The pooled (95% CI) AUC of BLS and ENF did not differ significantly from other models except PCM [Delong's test P = 0.022]. PCM exhibited the highest NPV, which supports this model's use as a possible rule out tool. Conclusion Our results show that BLS & ENF Models are equally effective as other ML models, in predicting in-hospital mortality with variability across all models. Additional studies are needed.

Simulation of fretting wear in steel wires under variable coefficient of friction and variable wear coefficient

In this paper, the fretting wear behaviour of steel wires working in coal mining technology is studied numerically. In past studies, the fretting process of steel wires was carried out numerically considering that the coefficient of friction (COF) and wear coefficient (WC) are constant parameters. However, it has been noticed experimentally that COF increases up to a certain number of fretting cycles and then becomes constant, i.e. a steady-state stage, depending on the loading conditions. This increase in COF during the fretting process is also known as the running-up stage. The fretting wear model is modified to evaluate the influence of the varying coefficient of friction (VCOF), which is associated with the variable wear coefficient (VWC), so the influence of VWC is also considered. The subroutine UMESHMOTION used to implement the wear law is also modified to study the effect of VCOF and VWC. Therefore, in this study, the numerical results of a three-dimensional finite element (FE) model are compared, with analytical results of contact area and contact stresses, and with experimental results of peak wear depth. After validating the FE model, the wear scar, the increasing wear depth, wear volume, and the decreasing contact stress with increasing fretting cycles are determined numerically considering VCOF and VWC using cycle jump approach. The energy dissipation effect of frictional force and fretting amplitude is also studied for varying interaction properties of fretting wear models. The numerical simulations are performed by considering both elastic and plastic material properties to analyse the influence of varying interaction properties on fretting wear models at the running-up stage. The results indicate that the VWC model exhibits comparable impacts on both the elastic and plastic models. The results also show that the VWC fretting wear model leads to higher wear scar, wear volume, and wear depth values at the running-up stage as well as at the steady state stage, which are close to the experimental data.

Chromatin phase separation and nuclear shape fluctuations are correlated in a polymer model of the nucleus

ABSTRACT Abnormal cell nuclear shapes are hallmarks of diseases, including progeria, muscular dystrophy, and many cancers. Experiments have shown that disruption of heterochromatin and increases in euchromatin lead to nuclear deformations, such as blebs and ruptures. However, the physical mechanisms through which chromatin governs nuclear shape are poorly understood. To investigate how heterochromatin and euchromatin might govern nuclear morphology, we studied chromatin microphase separation in a composite coarse-grained polymer and elastic shell simulation model. By varying chromatin density, heterochromatin composition, and heterochromatin-lamina interactions, we show how the chromatin phase organization may perturb nuclear shape. Increasing chromatin density stabilizes the lamina against large fluctuations. However, increasing heterochromatin levels or heterochromatin-lamina interactions enhances nuclear shape fluctuations by a “wetting”-like interaction. In contrast, fluctuations are insensitive to heterochromatin’s internal structure. Our simulations suggest that peripheral heterochromatin accumulation could perturb nuclear morphology, while nuclear shape stabilization likely occurs through mechanisms other than chromatin microphase organization.

Γ-convergence of a discrete Kirchhoff rod energy

This work is motivated by the classical discrete elastic rod model by Audoly et al. We derive a discrete version of the Kirchhoff elastic energy for rods undergoing bending and torsion and prove Γ-convergence to the continuous model. This discrete energy is given by the bending and torsion energy of an interpolating conforming polynomial curve and provides a simple formula for the bending energy depending in each discrete segment only on angle and adjacent edge lengths. For the liminf-inequality, we need to introduce penalty terms to ensure arc-length parametrization in the limit. For the recovery sequence a discretization with equal Euclidean distance between consecutive points is constructed. Particular care is taken to treat the interaction between bending and torsion by employing a discrete version of the Bishop frame.

Effect of steel fibers and the fiber-concrete adhesion stress on the nonlinear shear behavior of beams

In this article, a theoretical model in nonlinear elasticity is presented to analyze the influence of the percentage of steel fibers and the effect of the fiber-concrete bond stress on the shear force at the rupture of beams subjected to the combined effect of bending moment, normal force, and shear force. For a given beam section, it is defined by a succession of layers of concrete and longitudinal steel elements. Each layer is defined by its height hi, width bi, and position relative to one end of the section YGi. Each longitudinal steel element is also defined by its cross-sectional area and position relative to one end of the section. The steel fibers are defined by volume percentages of 0.5%, 1%, 1.5%, and 2%, taking into account the mechanical nonlinearity of the materials. This model is based on the multilayer analysis of sections and an iterative solution procedure for each layer and each section, considering a given longitudinal deformation state and shear stress. The global equilibrium of the sections is analyzed under the assumption of flat longitudinal deformations but with, in principle, an interdependence of longitudinal normal stresses and shear stresses. In this study, using the principle of virtual work, equilibrium equations for deformations and stresses, as well as partial compatibility equations between concrete deformations and mean deformations, are derived. Comparative examples between ordinary reinforced concrete beams with variable shapes and reinforcement details, and those reinforced with steel fibers, are presented to demonstrate the accuracy of the proposed model for simulating the nonlinear shear response of beams.

An Analytic Element Model for Seepage Forces in Fractured Media

AbstractThere are many situations where groundwater flow has high flow rates, causing large seepage forces. Examples are flows around highly conductive fractures and tunnels. We present a new analytic element for a tunnel in an elastic medium. We combined the analytic element method for groundwater flow with that for linear elasticity, and include the seepage force as a body force in the linearly elastic model. We represent tunnels and fractures as analytic elements. The solution for the case considered is limited to steady state flow and fluid-to-solid coupling. We present examples of the computed seepage forces around a tunnel and a fracture as well as a comparison with another numerical model.

Physics‐constrained symbolic model discovery for polyconvex incompressible hyperelastic materials

AbstractWe present a machine learning framework capable of consistently inferring mathematical expressions of hyperelastic energy functionals for incompressible materials from sparse experimental data and physical laws. To achieve this goal, we propose a polyconvex neural additive model (PNAM) that enables us to express the hyperelastic model in a learnable feature space while enforcing polyconvexity. An upshot of this feature space obtained via the PNAM is that (1) it is spanned by a set of univariate basis functions that can be re‐parametrized with a more complex mathematical form, and (2) the resultant elasticity model is guaranteed to fulfill the polyconvexity, which ensures that the acoustic tensor remains elliptic for any deformation. To further improve the interpretability, we use genetic programming to convert each univariate basis into a compact mathematical expression. The resultant multi‐variable mathematical models obtained from this proposed framework are not only more interpretable but are also proven to fulfill physical laws. By controlling the compactness of the learned symbolic form, the machine learning‐generated mathematical model also requires fewer arithmetic operations than its deep neural network counterparts during deployment. This latter attribute is crucial for scaling large‐scale simulations where the constitutive responses of every integration point must be updated within each incremental time step. We compare our proposed model discovery framework against other state‐of‐the‐art alternatives to assess the robustness and efficiency of the training algorithms and examine the trade‐off between interpretability, accuracy, and precision of the learned symbolic hyperelastic models obtained from different approaches. Our numerical results suggest that our approach extrapolates well outside the training data regime due to the precise incorporation of physics‐based knowledge.

Aeroacoustic simulation of human phonation based on the flow-induced vocal fold vibrations including their contact

This paper deals with the numerical simulation of the flow induced sound as generated in the human phonation process including vocal folds (VF) contact. The hybrid approach is used allowing to separate the acoustic and the biomechanical description of the considered problem. The two-dimensional fluid–structure interaction (FSI) problem is modelled by a simplified linear elastic model coupled to the incompressible Navier–Stokes equations in the arbitrary Lagrangian–Eulerian form and special attention is paid to the treatment of the contact problem in the FSI context. Following approach of [Sváček and Horáček, 2021] the incompressible flow model is modified by an addition of fictitious porosity term and the penalization inlet boundary condition is used. Further, the Lighthill acoustic analogy is applied for calculation of sound sources and their propagation through a vocal tract model. All partial differential equations, describing the elastic body vibration, the fluid flow and the acoustics, are approximated by finite element method. Numerical results of one-sided vocal fold paralysis are presented.

Enhanced fracture risk prediction: a novel multi-trait genetic approach integrating polygenic scores of fracture-related traits.

Current polygenic scores (PGS) have limited predictive power for fracture risk. To improve genetic prediction, we developed and evaluated a novel metaPGS combining genetic information from multiple fracture-related traits. We derived individual PGS from genome-wide association studies of 16 fracture-related traits and employed an elastic-net logistic regression model to examine the association between the 16 PGSs and fractures. An optimal metaPGS was constructed by combining 11 significant individual PGSs selected by the elastic regularized regression model. We evaluated the predictive power of the metaPGS alone and in combination with clinical risk factors recommended by guidelines. The discrimination ability of metaPGS was assessed using the concordance index. Reclassification was assessed using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). The metaPGS had a significant association with incident fractures (HR 1.21, 95% CI 1.18-1.25 per standard deviation of metaPGS), which was stronger than previously developed bone mineral density (BMD)-related individual PGSs. Models with PGS_FNBMD, PGS_TBBMD, and metaPGS had slightly higher but statistically non-significant c-index than the base model (0.640, 0.644, 0.644 vs. 0.638). However, the reclassification analysis showed that compared to the base model, the model with metaPGS improves the reclassification of fracture. The metaPGS is a promising approach for stratifying fracture risk in the European population, improving fracture risk prediction by combining genetic information from multiple fracture-related traits.

Impact of climate change and genetic development on Iowa corn yield

The vulnerability of corn yield to high temperature and insufficient rainfall in the US mid-west is widely acknowledged. The impact of extreme weather and genetic development on corn yield is less well known. One of the main reasons is that the multicollinearity in the variables can lead to confounding results. Here we model the impact of climate and genetic development by employing an elastic net regression model to address the multicollinearity issue. This allows us to develop a more robust multiple regression model with higher predictive accuracy. Using granular data for Iowa from 1981-2018, we find that corn yield is vulnerable to high mean summer temperatures particularly in July, a widening diurnal temperature range in June and dry summer conditions (due to extremely low rainfall) from June-August. We find that overall climate impact reduced average annual yield by 0.7%. We also find that genetic development which led to earlier planting dates, widening duration of the reproductive interval, higher growing degree day accumulation and larger net planted area had a beneficial impact on the Iowa corn yield during 1981-2018 resulting in an average annual yield improvement of 1.8% per annum. This provides a basis for optimism that these genetic developments and management practices will continue to adapt and improve in the future to counter the impact of climate change on corn yield. We have also modelled the impact of future climate change using the latest climate projections from the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). These climate projections show that the average temperature during the growing season (MayO-October) will increase by 2.4 -2.9 o C by mid-century while the average spring temperature (March and April) will increase by a relatively slower 1.9 -2.3 o C by mid-century. Additionally, climate projections show that both temperature and rainfall will also become more extreme in the future with the changes varying from spring to summer. Our results show that, just due to climate change alone in Iowa corn yield will decline between 1.4-1.7% per annum until mid-century (or 1.2-2.1% per annum until the late twenty first century).

Two-layer elastic models for single-yeast compressibility with flat microlevers.

Unicellular organisms such as yeast can survive in very different environments, thanks to a polysaccharide wall that reinforces their extracellular membrane. This wall is not a static structure, as it is expected to be dynamically remodeled according to growth stage, division cycle, environmental osmotic pressure and ageing. It is therefore of great interest to study the mechanics of these organisms, but they are more difficult to study than other mammalian cells, in particular because of their small size (radius of a few microns) and their lack of an adhesion machinery. Using flat cantilevers, we perform compression experiments on single yeast cells (S. cerevisiae) on poly-L-lysine-coated grooved glass plates, in the limit of small deformation using an atomic force microscope (AFM). Thanks to a careful decomposition of force-displacement curves, we extract local scaling exponents that highlight the non-stationary characteristic of the yeast behavior upon compression. Our multi-scale nonlinear analysis of the AFM force-displacement curves provides evidence for non-stationary scaling laws. We propose to model these phenomena based on a two-component elastic system, where each layer follows a different scaling law..

The joint effect of long-term exposure to multiple air pollutants on non-accidental and cause-specific mortality: A longitudinal cohort study

The long-term joint impacts of fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) on mortality are inconclusive. To bridge this research gap, we included 283,568 adults from the Taiwan MJ cohort between 2005 and 2016 and linked with the mortality data until 31 May 2019. Participants’ annual average exposures to PM2.5, NO2, and O3 were estimated using satellite-based spatial-temporal models. We applied elastic net-regularised Cox models to construct a weighted environmental risk score (WERS) for the joint effects of three pollutants on non-accidental, cardiovascular, and cancer mortality and evaluated the contribution of each pollutant. The three pollutants jointly raised non-accidental mortality risk with a WERS hazard ratio (HR) of 1.186 (95% CI: 1.118–1.259) per standard deviation increase in each pollutant and weights of 72.8%, 15.2%, and 12.0% for PM2.5, NO2, and O3, respectively. The WERS increased cardiovascular death risk [HR: 1.248 (1.042–1.496)], with PM2.5 as the first contributor and O3 as the second. The WERS also elevated the cancer death risk [HR: 1.173 (1.083–1.270)], where PM2.5 played the dominant role and NO2 ranked second. Coordinated control of these three pollutants can optimise the health benefits of air quality improvements.

Abstract PO1-15-05: Gene expression signature as a predictor of pathological complete response to neoadjuvant docetaxel, carboplatin, trastuzumab, and pertuzumab treatment in locally advanced HER2-positive breast cancer

Abstract Background: Neoadjuvant pertuzumab and trastuzumab in combination with chemotherapy can achieve pathological complete response (pCR, defined as ypT0/is ypN0) in approximately 40–60% of patients with human epidermal growth factor receptor 2-positive (HER2+) breast cancer (BC). Patients who achieve pCR have significantly improved survival. Therefore, the prediction of pCR is crucial for optimizing neoadjuvant therapy. Methods: To identify a gene expression signature that could predict pCR in patients with HER2+ BC, we used the nCounter Breast Cancer 360TM V2 panel to quantify the expression of 758 genes in 104 HER2+ BC samples from patients who received neoadjuvant docetaxel, carboplatin, trastuzumab, and pertuzumab (TCHP) chemotherapy and subsequent curative breast surgery. Data normalization, differentially expressed gene (DEG) analysis, and gene set enrichment analysis (GSEA) were performed using the NanoTube Bioconductor package. Using the elastic net logistic regression model with optimal hyperparameters determined by 5-fold cross-validation (5-CV), we developed a chemosensitivity score based on the expression of significant DEGs (genes with absolute log2-fold-change ≥0.5 and q-value < 0.1). We tested its predictive value for pCR using multivariable logistic regression analysis. Results: The median age at diagnosis was 49.5 years. The clinical stage was II in 53 (51%) patients and III in 51 (49%) patients at diagnosis. Ninety (86.5%) patients had a HER2 immunohistochemistry (IHC) score of 3+, while the remaining 14 (13.5%) patients had HER2 IHC 2+ but HER2 amplification detected by in situ hybridization. Pathological evaluation of surgical specimens revealed that 55 (52.9%) patients achieved pCR. Menopausal status (p = 0.01), PAM50 intrinsic subtype (p = 0.002), hormone receptor (HR) status (p < 0.001), and HER2 IHC score (p = 0.019) were significantly correlated with the pCR rate. DEG analysis using the PAM50 intrinsic subtype, HR status, and HER2 IHC score as covariates revealed 6 downregulated DEGs and 33 upregulated DEGs in patients who achieved pCR compared to patients who did not (Table 1). The 39 DEGs were significantly enriched in nine preselected MSigDB hallmark gene sets representing immunological processes (p < 0.001). The optimal elastic net logistic regression model containing 39 DEGs achieved the mean 5-CV area under the receiver operating characteristic curve of 0.81. The chemosensitivity score calculated using the optimal model parameters was significantly correlated with pCR after adjustment for the PAM50 intrinsic subtype, HR status, and HER2 IHC score (p < 0.001). GSEA revealed eight hallmark gene sets that were significantly up- or downregulated in samples with pCR, including epithelial-mesenchymal transition (normalized enrichment score [NES] = -2.16; q < 0.001), late estrogen response (NES = -2.01; q = 0.004), early estrogen response (NES = -1.972; q = 0.005), and inflammatory response (NES = 1.53; q = 0.06). Conclusion: Our findings demonstrate that gene expression patterns can predict the response to neoadjuvant TCHP chemotherapy in HER2+ BC. The functional characterization of 39 DEGs suggests that the tumor microenvironment, including adjacent immune cells, plays a significant role in modulating the response to neoadjuvant chemotherapy in HER2+ BC. Table 1. Significant DEGs Citation Format: Junghoon Shin, Ji-Yeon Kim, Eun Yoon Cho, Seok Won Kim, Jeong Eon Lee, Hyunwoo Lee, Yeon Hee Park, Jin Seok Ahn, Young-Hyuck Im. Gene expression signature as a predictor of pathological complete response to neoadjuvant docetaxel, carboplatin, trastuzumab, and pertuzumab treatment in locally advanced HER2-positive breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-15-05.

Research on the widths of protective coal pillars in main roadway based on the damaged foundation beam model

Long-term stability of the protective coal pillars in coal mines can ensure normal and safe use of roadways; in turn, the integrity of the roadway plays an important role in safe and efficient mining in coal mines. The initial damage and rheological damage caused by coal-seam excavation disturbance have important influences on the selection of reasonable widths of the protective coal pillars. Based on damage mechanics and the theory of elastic foundation beams, a coupled damage constitutive relationship of rock-like materials is proposed in this work by considering the influence of threshold and residual strength to establish the mechanical model of the elastic damage foundation beam. By theoretical analysis and numerical calculation, the differential equation of the deflection curve of the beam is solved, the influence range of the advance abutment pressure under the condition of coupling damage is obtained, and the width of the protective coal pillar is determined. The results show that compared with the elastic foundation beam model, after considering the coupling damage of coal, the influence range of the advance abutment pressure increases with time over a certain duration. At the end of this duration, the influence range of the advance abutment pressure remains almost unchanged. For the case considered in this paper, the long-term stability of the roadway is guaranteed when the width of the coal pillar is 120 m. The potential interference or change related to the specific situation of the site can be studied by changing the relevant parameters in the model to study different working conditions, or the damage of the roof rock beam during the mining process of the working face can be considered, which will be the subject of further research in the future.

Velocity Measurements of Powdered Rock at Low Confining Pressures and Comparison to Lunar Shallow Seismic Velocity

AbstractSeismic methods will be useful for future lunar near‐surface characterization, and high‐fidelity elastic models will be required to aid interpretation of seismic observations. To develop an elastic lunar near‐surface model, we performed ultrasonic velocity measurements of lunar regolith simulant at low confining pressure and developed a rock physics model calibrated to these measurements. Grain contact models based on Hertz‐Mindlin theory produce accurate results at high confining pressure (i.e., several hundred meters or more burial depth) but historically fail to predict observed velocities in unconsolidated media at low pressure. Therefore, we heuristically modified existing models to fit our measured data over a range of porosities and confining pressures. To compare with Apollo 14 and 16 active seismic experiments, we used our new heuristic rock physics model to produce lunar subsurface velocity profiles. We performed ray tracing through our velocity profiles to calculate seismic traveltime, which results in good agreement with first arrivals interpreted from the Apollo experiments. Our model suggests a slightly higher velocity‐pressure dependence than inferred from in situ measurements, which may be due to porosity reduction in the lunar regolith from impact‐induced and natural vibrations.

PERFORMANCE COMPARISON OF DERIVATIVE-FREE OPTIMIZATION METHODS

Structural optimization for parameters and non-parameters-based modelling are adopted in almost all area of science and engineering due to their inherent advantages in contrary to manual tuning. Typically, in the easiest case regression analysis is used to develop a data-based model for the future applications and forecasting. However, the aforementioned modelling approach may be suitable for well-defined data (i.e., elastic model) but might be difficult for complex data type where linear type model may show very poor performances. As a result, the adaptation of nonlinear type or complex models (e.g. a combination of linear and nonlinear parts) are unavoidable. For instance, modeling of a hysteresis type behavior would not be possible via any linear type models while a Bouc-Wen type nonlinear model would be a better choice. Hence, to avoid early mentioned issues and to achieve better performances, the derivative-free or search-algorithm optimization might be suitable alternatives. Herein, due to the underlying advantages, the derivative-free search algorithms have been adopted to optimize model performances and interpret both linear and nonlinear type data. To be precise, two different derivative-free algorithms namely, (i) Nelder-Mead Simplex Method, and (ii) Genetic Algorithm have been studied. The outcome of this study shows that in both former mentioned cases search-based algorithms have better performance in terms of capturing the behavior of the true data via optimization. The real-life applications of optimization algorithms are numerous as these tools are used in many branches of science and engineering.

The Response of Taupō Volcano to the M7.8 Kaikōura Earthquake

AbstractSeveral studies suggest that large earthquakes (M > 7.0) can act as external triggers of volcanic unrest, and even eruption. This triggering is attributed to either ground shaking (dynamic stresses) or to permanent ground deformation (associated with static stress changes). However, large earthquakes are rare and testing triggering hypotheses has proven difficult. We use geodetic data to show that the 13 November 2016 Kaikōura earthquake (Mw 7.8) triggered local deformation of up to 11 mm at Taupō volcano, 500 km away, which lasted for approximately twelve days. Using elastic geodetic models, we infer that the observed deformation was caused by either aseismic fault slip or a dike intrusion. We then use strong motion data from the surrounding area to show that the Kaikōura earthquake caused maximum dynamic stress changes in the range of 0.15–0.9 MPa in the vicinity of Taupō volcano and conclude that these dynamic stress changes triggered local faulting or dike activity and the associated deformation at Taupō volcano.

The present-day kinematics of the Tianshan orogenic belt constrained by GPS velocities

Since the late Cenozoic, the reactivated Tianshan orogenic belt has accommodated crustal shortening exceeding 200 km, primarily due to the far-field effects of the India-Eurasia plate collision. However, the details of the strain partitioning in the Tianshan Mountain range remain elusive. We interpret a new compilation of GPS velocities covering the whole Tianshan range with a classic elastic block model. Compared to previous studies with a block modeling approach, the Tianshan orogenic belt is further subdivided into several blocks based on geological fault traces and a clustering analysis approach. In addition to obvious crustal shortening on the bounding thrust faults of the Tianshan, our inverted fault slip rates also reveal that faults within the Tianshan orogenic belt, such as the Nalati Fault and the southern margin of the Issyk-Kul Lake Fault, which plays a crucial role in accommodating the tectonic crustal shortening. In the 72°E−78°E region, the internal shortening rate within the mountain is approximately 5–7 mm/yr. Besides crustal shortening, strike-slip motion occurs on faults in the interior of the mountain range as well as in the foreland fold-and-thrust belts, especially in the southern margin of the Tianshan. These findings suggest that the crustal deformation in the Tianshan Mountain range is more complex than previously thought, and the oblique convergence between the Tarim Basin and the Tianshan probably results in both strike-slip and thrust motion.