Homogeneous Model Research Articles

Equivalent plate model of shape memory polymer composite based variable height corrugated structure

In this work, an equivalent plate model for a shape memory polymer composite based corrugation structure with varying height, for morphing application, is developed. A thermodynamically consistent constitutive relation based on temperature dependent phase transformation concept for shape memory polymer matrix is used in the composite model. The homogenization model is based on the equivalence of strain energy of a representative unit volume. Following validation, as an application, commonly used airfoil profile NACA 63210 was considered to demonstrate the model utility. Parametric studies have been undertaken to investigate the influence of corrugation parameters like thickness and number of corrugations.

Modeling the freezing of nanomaterial through the cold storage unit implementing numerical approach

In this attempt, the freezing period in the system was effectively reduced by introducing a fin and incorporating nanoparticles. The attachment of a fin to a vertical cold surface, coupled with an insulated tilted wall, strategically optimized the freezing process. Both the concentration of CuO nano-powders and the sizes of the additives were meticulously examined in the modeling, emphasizing their roles in enhancing the conduction mode. The proposed model, derived through the approximation of the homogeneous model and deliberately neglecting velocity terms, was solved using the Galerkin method and validated against previous data. Size variations from dp = 40 to 50 nm resulted in a notable 49.3 % increase in freezing time, while changes from 30 to 40 nm led to a 20.01 % reduction. Furthermore, the dispersion of CuO nanoparticles with ϕ = 0.02 significantly improved solidification by approximately 27.06 %. These findings offer valuable insights for practical applications, showcasing the potential of this approach to optimize freezing processes in diverse systems.

Synergy in bio-inspired hybrid composites with hierarchically structured fibrous reinforcements

In response to the global energy crisis, high-performance transportation sectors are rapidly embracing lightweight materials to enhance energy efficiency and sustainability, while grappling with the persistent challenges of developing structural materials that meet stringent safety standards with robust mechanical performance and ease of scalability. Thus, this work presents a combined experimental and theoretical framework to develop a profound understanding of the synergistic effect in hybrid composites with bio-inspired fibrous reinforcements, by elucidating the interfacial interactions across multiple length-scales, encompassing atomic covalent bonding to micro-morphology. A model hybrid composite system, containing a self-assembled fibrous reinforcement consisting of nano-sized Graphene Nanoplatelets (GnP) covalently bonded onto chemically-modified micro-sized Glass Fibers (GF), was utilized to showcase the synergistic effect and highlight its associated mechanisms. The interfacial interactions of the reinforcement were optimized by obtaining the maximum density of covalent bonds, which was achieved with 0.5 wt% GnP for the hybrid composites containing 10 wt% GF, increasing the work of adhesion by 33 %, compared to the biphasic GF composites. The composite’s morphology contains minimal agglomeration with ∼68 % of GnPs oriented with the melt flow, supressing high-stress concentration areas, while the formed crystalline microstructure, with ∼18 % β-crystals, allows the matrix to absorb substantial energy. Furthermore, the increased trans-crystallization encapsulating the hierarchical reinforcement induced nanoscale stiffness variations, increasing rigidity, and forming an ∼16 µm gradient interphase that facilitates load transfer. The greatest synergistic effect observed was ∼54 %, ∼37 %, and ∼75 % for the tensile modulus, tensile strength, and impact strength, respectively. Additionally, a theoretical framework accounting for the synergistic effect was formulated, using a two-step core/shell homogenization model, which shows great potential in expediting the design and optimization of innovative hybrid composite materials.

Anisotropic Bianchi Type-1 Cosmological Model Containing Dark Energy

Considering the LRS Bianchi type I metric here we studied the spatial homogeneous and anisotropic dark energy cosmological models in the frame work of B-D scalar tensor theory of gravity along with variable equation of parameter and deceleration parameter. Exact solutions of field equations are obtained with certain physical assumptions in different cases. The physical behavior of the model is also discussed in details.

The addition of temporal neighborhood makes the logic of prefixes and sub-intervals EXPSPACE-complete

A classic result by Stockmeyer gives a non-elementary lower bound to the emptiness problem for star-free generalized regular expressions. This result is intimately connected to the satisfiability problem for interval temporal logic, notably for formulas that make use of the so-called chop operator. Such an operator can indeed be interpreted as the inverse of the concatenation operation on regular languages, and this correspondence enables reductions between non-emptiness of star-free generalized regular expressions and satisfiability of formulas of the interval temporal logic of chop under the homogeneity assumption. In this paper, we study the complexity of the satisfiability problem for suitable weakenings of the chop interval temporal logic, that can be equivalently viewed as fragments of Halpern and Shoham interval logic. We first consider the logic $\mathsf{BD}_{hom}$ featuring modalities $B$, for \emph{begins}, corresponding to the prefix relation on pairs of intervals, and $D$, for \emph{during}, corresponding to the infix relation. The homogeneous models of $\mathsf{BD}_{hom}$ naturally correspond to languages defined by restricted forms of regular expressions, that use union, complementation, and the inverses of the prefix and infix relations. Such a fragment has been recently shown to be PSPACE-complete . In this paper, we study the extension $\mathsf{BD}_{hom}$ with the temporal neighborhood modality $A$ (corresponding to the Allen relation \emph{Meets}), and prove that it increases both its expressiveness and complexity. In particular, we show that the resulting logic $\mathsf{BDA}_{hom}$ is EXPSPACE-complete.

Conceptual design and feasibility analysis of a heat pipe cooled cermet fuel space nuclear reactor for mars surface application

The compact, safe, light-weight and highly reliable Space Nuclear Reactor (SNR) with heat pipe cooling is an excellent candidate for the potential needs of Mars surface applications. This work proposed a heat pipe reactor concept design which can provide 1 MWth power for more than 8 years. Reactivity control is accomplished by 6 rotating control drums. Cermet fuel with dispersion granules characteristics adopted in this design is processed into prismatic-type tube-in-duct (TID) and composed of Mo-14 wt%Re matrix and highly enriched uranium nitride (UN) granules. Both heterogeneous and homogeneous models of Cermet fuel are evaluated by OpenMC code, indicating an extreme weak resonance self-shielding effect. Neutronics characteristics such as the effective multiplication factor, burnup depth, fuel temperature coefficient, neutron spectrum, reactivity control, neutron flux and power distribution are obtained. Drum stuck accidents and submersion criticality accidents are also analyzed. The designed reactor achieves a shutdown depth of −1521 pcm in the worst launch or reentry accident scenario considered without relying on the reactivity control device. The arrangement forms and content of Spectral Shift Absorber (SSAs) that affect the safety worth are also analyzed. Furthermore, thermal analysis is performed by Fluent and the maximum temperature is lower than 1570K during normal operation. The maximum temperature reaches 1805K and core has sufficient margin to respond to a single point failure of the heat pipe. Overall, the scheme preliminarily satisfies neutronics, thermal and safety requirements.

Analysis of crack-tip stress field in unidirectional fiber-reinforced composites based on 3D micromechanical model

Accurately clarifying the influence of cracks on the mechanical behaviors of unidirectional fiber-reinforced composites (UD FRCs) is of great significance for the structural integrity evaluation of UD FRCs with defects. Although several experiments and numerical analyses have explored the impact of cracks on stress field, satisfactory quantitative characterizations of crack size, a/W and location, Δ, fiber content, Vf have not yet been obtained. In this paper, the accurate and detailed microscopic models containing crack were first established and thoroughly validated. Then, a parametric study was conducted using three-dimensional micromechanical analysis to investigate how the crack size and location, fiber content affect the von Mises, stress triaxiality, Lode angle and J-integral of UD FRCs. Results showed that homogeneous models, whether isotropic or anisotropic, cannot accurately describe the mechanical behavior of cracked UD FRCs. The von Mises stress and J-integral are greatly influenced by the crack and fiber content, while stress triaxiality and Lode angle are not sensitive to changes in these factors. Further, the von Mises and J-integral increase with the decrease of Vf and the increase of a/W, but show a more complex trend in the variation of Δ. These findings will help clarify the failure mechanism of cracked UD FRCs and can be used for reliability evaluation.

Design Optimization of Piezocomposites Using a Homogenization Model: From Analytical Model to Experimentation.

In the process of activating non-conductive smart-structures using piezoelectric patches, one possible method is to add a conductive layer to ensure electrical contact of both electrodes of the ceramic. Therefore, depending on the stiffness and the thickness of this layer, changes in the overall piezoelectric properties lead to a loss in the electromechanical coupling that can be implemented. The purpose of this work is to study the impact of this added electrode layer depending on its thickness. A model of the effect of this layer on the piezoelectrical coefficients has been derived from the previous approach of Hashimoto and Yamagushi and successfully compared to experimental data. This global model computes the variation of all the piezoelectric coefficients, and more precisely of k31 or d31 for various brass electrode volumes relative to the ceramic volume. A decrease in the lateral electromechanical coupling factor k31 was observed and quantified. NAVY II PZT piezoelectric transducers were characterized using IEEE standard methods, with brass electrode thicknesses ranging from 50 to 400 microns. The model fits very well as shown by the results, leading to good expectations for the use of this design approach for actuators or sensors embedded in smart-structures.

Gas-liquid swirling flow behaviors and liquid-carrying mechanism inside a horizontal pipe

The behaviors of gas-liquid swirling flow under various swirler turns, neglected in previous studies, are essential for swirling flow applications. The role of swirler turns (1, 3, 5) in swirling flow characteristics was systematically investigated in a horizontal pipeline with an inner diameter of 32 mm. The temporal stability and circumferential symmetry of swirling liquid film become better with increasing swirler turns. The periodicity of the swirling liquid film interfacial wave induced by slug flow increases as the swirler turns decrease, while the main wave frequency decreases. Interestingly, for the original slug flow, the downstream void fraction probability density function curves are bimodal with 3 and 5 turns, while it is single-peaked at the swirler with 1 turn. The effectiveness of swirler with 1 turn, 3 turns, and 5 turns in promoting carrying liquid was 100 %, 82.08 %, and 89.22 %, respectively. The mechanism of liquid-carrying was explained in terms of perturbation waves and momentum transfer in the gas-liquid interface. Based on the homogeneous flow model and separated flow model, two predictive models for swirler pressure loss were proposed with maximum errors of 14.67 % and 17.86 %, respectively.

On simulating diffusional phase transformation in diffusion triple

Diffusion triple technique (stand-alone or composed diffusion multiples) is crucial for high-throughput construction of phase diagrams or extracting diffusion coefficients, taking advantage of the broad two-dimensional (2D) composition variations. However, currently there is a lack of methods available for simulating 2D diffusion accompanied by phase transformations inside diffusion triples. In the present work, we take a noticeable step forward by simulating diffusional phase transformation between the contacting interfaces, especially in the tri-junction of diffusion triple. The present method is an extension of the homogenization model (H. Larsson and L. Höglund, Calphad, 33 (2009) 495–501) to 2D simulations, allowing for disclosure of microstructure evolutions in diffusion triples. The practicability of the present method is demonstrated by comparing the simulation results with the experiment of a Fe-Cr-Ni diffusion triple (as a part of a multiple) from the literature.

Initial non-invasive in vivo sensing of the lung using time domain diffuse optics

The in vivo diagnosis and monitoring of pulmonary disorders (caused for example by emphysema, Covid-19, immature lung tissue in infants) could be effectively supported by the non-invasive sensing of the lung through light. With this purpose, we investigated the feasibility of probing the lung by means of time-resolved diffuse optics, leveraging the increased depth (a few centimeters) attained by photons collected after prolonged propagation time (a few nanoseconds). We present an initial study that includes measurements performed on 5 healthy volunteers during a breathing protocol, using a time-resolved broadband diffuse optical spectroscopy system. Those measurements were carried out across the spectral range of 600–1100 nm at a source-detector distance of 3 cm, and at 820 nm over a longer distance (7–9 cm). The preliminary analysis of the in vivo data with a simplified homogeneous model revealed a maximum probing depth of 2.6–3.9 cm, suitable for reaching the lung. Furthermore, we observed variations in signal associated with respiration, particularly evident at long photon propagation times. However, challenges stemming from both intra- and inter-subject variability, along with inconsistencies potentially arising from conflicting scattering and absorption effects on the collected signal, hindered a clear interpretation. Aspects that require further investigation for a more comprehensive understanding are outlined.

Computational modeling of Chlamydomonas reinhardtii cellular radiation properties with synergistic consideration of complex structures and compositions.

The radiation characteristics of microalgae are of great significance for the design of photobioreactors and ocean optical remote sensing. Yet the complex structure of microalgae makes it difficult to theoretically predict its radiation characteristics based on traditional Mie theory. In this work, taking Chlamydomonas reinhardtii as an example, a multi-component cell model with a complex structure is proposed, which considers the organelles and shape of microalgae, and the volume change during the production of Chlamydomonas reinhardtii lipids. The theoretical calculation is carried out using the discrete dipole approximation method, and an improved transmission method is used for experimental measurement. The experimental data are compared and analyzed with the multi-component complex structure model, the homogeneous sphere model and the coated sphere model. The results show that the calculation accuracy of the multi-component complex structure model is higher, the error of the scattering cross-section is reduced by more than 8.6% compared with the homogeneous sphere model and coated sphere model, and the absorption cross-section and the scattering phase function are in good agreement with the experimental results. With the increase of lipids, the absorption cross-section and the scattering phase function vary slightly. However, the scattering cross-section has an observed change with increasing wavelength. In addition, the theoretical calculation error can be reduced when the influence of the culture medium is taken into account.

Biodiesel as a Sustainable Platform Chemical Enabled by Selective Partial Hydrogenation: Compounds Outplace Combustion?!

The hydrogenation of polyunsaturated fatty acids (PUFAs) in vegetable oils and their derivatives is essential for their use in many areas, such as biofuels and food chemistry. However, no attempts have been made to adapt this technology to the requirements of further chemical utilization of fatty acid methyl esters as molecular building blocks, especially for particularly promising double-bond reactions. In this work, we, therefore, use three homogeneous catalytic model reactions (hydroformylation, isomerizing methoxycarbonylation, and ethenolysis) to show, firstly, that it is already known from the literature that high PUFA contents have a negative impact on activity and selectivity. Subsequently, using the example of soybean and canola biodiesel, we demonstrate that these key figures can be drastically improved by a preceding selective partial hydrogenation. This makes it possible to first reduce the share of PUFAs to <1 w % without causing significant overhydrogenation and then to carry out hydroformylation, methoxycarbonylation, and ethenolysis with significantly increased activity (up to twentyfold) and selectivity (up to 80 % increase). With these findings, we hope to convince the scientific and industrial world of the potential of selective partial hydrogenation as a key technology for utilizing renewable raw materials and to encourage its effective use in future work.

Control of fracture toughness of kerogen on artificially-matured shale samples: An energy-based nanoindentation analysis

Understanding controls on mechanical properties of organic-rich shale is essential in exploration and successful production from hydrocarbon reservoirs. In previous works, most researchers have focused on the influence of thermal maturity and organic type on elastic and viscoelastic properties of organic matter. However, fracture toughness, an important parameter in engineering design, has been less investigated. In this study, shale samples with different thermal maturity were obtained by programmed heating. Then, the fracture toughness of organic matter in shale samples was measured using an energy-based nanoindentation method, and the influence of geochemical features was determined. Organic matter was classified as inertinite, vitrinite and bitumen, and the fracture toughness of tested organic matter was in the range of 0.023–0.081 MPa*m0.5. The results indicated that the fracture toughness of vitrinite increased with thermal maturity. Additionally, both the reduced modulus of vitrinite and bitumen increased, while the viscosity of all types of organic matter decreased, with higher levels of thermal maturity. Moreover, a comparison between mechanical parameters of artificially-matured samples, and those of natural samples, was conducted. The fracture toughness of organic matter obtained in this study is crucial for parameter estimation of fracture propagation and homogenization models.

Differentiating anorexia nervosa and atypical anorexia nervosa with absolute weight cut-offs results in a skewed distribution for premorbid weight among youth.

Anorexia nervosa (AN) and atypical AN are conceptualized as distinct illnesses, despite similar characteristics and sequelae. Whereas DSM-5 differentiates youth with AN and atypical AN by the presence of clinical 'underweight' (i.e., 5th BMI percentile for age-and-sex (BMI%)), we hypothesized that using this weight cut-off to discern diagnoses creates a skewed distribution for premorbid weight. Participants included hospitalized youth with AN (n = 165, 43.1%) and atypical AN (n = 218, 56.9%). Frequency analyses and chi-square tests assessed the distribution of premorbid BMI z-scores (BMIz) for diagnosis. Non-parametric Spearman correlations and Stepwise Linear regressions examined relationships between premorbid BMIz, admission BMIz, and weight loss in kg. Premorbid BMIz distributions differed significantly for diagnosis (p < .001), with an underrepresentation of 'overweight/obesity' (i.e., BMI% ≥ 85th) in AN. Despite commensurate weight loss in AN and atypical AN, patients with premorbid 'overweight/obesity' were 8.31 times more likely to have atypical AN than patients with premorbid BMI% < 85th. Premorbid BMIz explained 57% and 39% of the variance in admission BMIz and weight loss, respectively. Findings support a homogenous model of AN and atypical AN, with weight loss predicted by premorbid BMI in both illnesses. Accordingly, premorbid BMI and weight loss (versus presenting BMI) may better denote the presence of an AN-like phenotype across the weight spectrum. Findings also suggest that differentiating diagnoses with BMI% < 5th requires that youth with higher BMIs lose disproportionately more weight for an AN diagnosis. This is problematic given unique treatment barriers experienced in atypical AN. Anorexia nervosa (AN) and atypical AN are considered distinct conditions in youth, with differential diagnosis hinging upon a presenting weight status of 'underweight' (i.e., BMI percentile for age-and-sex (BMI%) < 5th). In our study, youth with premorbid 'overweight/obesity' (BMI% ≥ 85th) disproportionately remained above this threshold, despite similar weight loss. Coupled with prior evidence for commensurate characteristics and sequelae in both diagnoses, we propose that DSM-5 differentiation of AN and atypical AN inadvertently reinforces weight stigma and may contribute to treatment disparities in atypical AN.

Two-Stage Ensemble Deep Learning Model for Precise Leaf Abnormality Detection in Centella asiatica

Leaf abnormalities pose a significant threat to agricultural productivity, particularly in medicinal plants such as Centella asiatica (Linn.) Urban (CAU), where they can severely impact both the yield and the quality of leaf-derived substances. In this study, we focus on the early detection of such leaf diseases in CAU, a critical intervention for minimizing crop damage and ensuring plant health. We propose a novel parallel-Variable Neighborhood Strategy Adaptive Search (parallel-VaNSAS) ensemble deep learning method specifically designed for this purpose. Our approach is distinguished by a two-stage ensemble model, which combines the strengths of advanced image segmentation and Convolutional Neural Networks (CNNs) to detect leaf diseases with high accuracy and efficiency. In the first stage, we employ U-net, Mask-R-CNN, and DeepNetV3++ for the precise image segmentation of leaf abnormalities. This step is crucial for accurately identifying diseased regions, thereby facilitating a focused and effective analysis in the subsequent stage. The second stage utilizes ShuffleNetV2, SqueezeNetV2, and MobileNetV3, which are robust CNN architectures, to classify the segmented images into different categories of leaf diseases. This two-stage methodology significantly improves the quality of disease detection over traditional methods. By employing a combination of ensemble segmentation and diverse CNN models, we achieve a comprehensive and nuanced analysis of leaf diseases. Our model’s efficacy is further enhanced through the integration of four decision fusion strategies: unweighted average (UWA), differential evolution (DE), particle swarm optimization (PSO), and Variable Neighborhood Strategy Adaptive Search (VaNSAS). Through extensive evaluations of the ABL-1 and ABL-2 datasets, which include a total of 14,860 images encompassing eight types of leaf abnormalities, our model demonstrates its superiority. The ensemble segmentation method outperforms single-method approaches by 7.34%, and our heterogeneous ensemble model excels by 8.43% and 14.59% compared to the homogeneous ensemble and single models, respectively. Additionally, image augmentation contributes to a 5.37% improvement in model performance, and the VaNSAS strategy enhances solution quality significantly over other decision fusion methods. Overall, our novel parallel-VaNSAS ensemble deep learning method represents a significant advancement in the detection of leaf diseases in CAU, promising a more effective approach to maintaining crop health and productivity.

Observational constraints on the origin of the elements

Context. The chemical evolution history of slow neutron-capture elements in the Milky Way is still a matter of debate, especially in the metal-poor regime ([Fe/H] &lt; −1). Aims. Based on Gaia-ESO spectroscopic data, a recent study investigated the chemical evolution of neutron-capture elements in the regime [Fe/H] &gt; −1. Here, we aim to complement this study down to [Fe/H] = −3, and focus on Ba, Y, and Sr, along with the abundance ratios of [Ba/Y] and [Sr/Y], which give comprehensive views on s-process nucleosynthesis channels. Methods. We measured the local thermodynamic equilibrium (LTE) and non-local thermodynamic equilibrium (NLTE) abundances of Ba, Y, and Sr in 323 Galactic metal-poor stars using high-resolution optical spectra with high signal-to-noise ratios. We used the spectral fitting code TSFitPy together with 1D model atmospheres, using previously determined LTE and NLTE atmospheric parameters. Results. We find that the NLTE effects are on the order of ∼ − 0.1 to ∼0.2 dex, depending on the element. We find that stars enhanced (deficient) in [Ba/Fe] and [Y/Fe] are also enhanced (deficient) in [Sr/Fe], suggesting a common evolution channel for these three elements. We find that the ratio between heavy and light s-process elements [Ba/Y] varies weakly with [Fe/H] even in the metal-poor regime, which is consistent with the behaviour in the metal-rich regime. The [Ba/Y] scatter at a given metallicity is larger than the abundance measurement uncertainties. Homogeneous chemical evolution models with different yield prescriptions are not able to accurately reproduce the [Ba/Y] scatter in the low-[Fe/H] regime. Adopting the stochastic chemical evolution model by Cescutti &amp; Chiappini allows us to reproduce the observed scatter in the abundance pattern of [Ba/Y] and [Ba/Sr]. Based on our observations, we have ruled out the need for an arbitrary scaling of the r-process contribution, as previously suggested by the authors behind the construction of the model. Conclusions. We show how important it is to properly include NLTE effects when measuring chemical abundances, especially in the metal-poor regime. This work demonstrates that the choice of the Galactic chemical evolution model (stochastic versus one-zone) is key when comparing models to observations. Upcoming large-scale spectroscopic surveys such as 4MOST and WEAVE are poised to deliver high-quality data for many thousands of metal-poor stars and this work gives a typical case study of what could be achieved with such surveys in the future.

Modelling heterogeneity in the classification process in multi-species distribution models can improve predictive performance.

Species distribution models and maps from large-scale biodiversity data are necessary for conservation management. One current issue is that biodiversity data are prone to taxonomic misclassifications. Methods to account for these misclassifications in multi-species distribution models have assumed that the classification probabilities are constant throughout the study. In reality, classification probabilities are likely to vary with several covariates. Failure to account for such heterogeneity can lead to biased prediction of species distributions. Here, we present a general multi-species distribution model that accounts for heterogeneity in the classification process. The proposed model assumes a multinomial generalised linear model for the classification confusion matrix. We compare the performance of the heterogeneous classification model to that of the homogeneous classification model by assessing how well they estimate the parameters in the model and their predictive performance on hold-out samples. We applied the model to gull data from Norway, Denmark and Finland, obtained from the Global Biodiversity Information Facility. Our simulation study showed that accounting for heterogeneity in the classification process increased the precision of true species' identity predictions by 30% and accuracy and recall by 6%. Since all the models in this study accounted for misclassification of some sort, there was no significant effect of accounting for heterogeneity in the classification process on the inference about the ecological process. Applying the model framework to the gull dataset did not improve the predictive performance between the homogeneous and heterogeneous models (with parametric distributions) due to the smaller misclassified sample sizes. However, when machine learning predictive scores were used as weights to inform the species distribution models about the classification process, the precision increased by 70%. We recommend multiple multinomial regression to be used to model the variation in the classification process when the data contains relatively larger misclassified samples. Machine learning prediction scores should be used when the data contains relatively smaller misclassified samples.

A Meso-scale study to predict high-temperature behavior of concrete structures in a hygral-thermal-chemical-mechanical framework

Fire has become one of the common hazards that a concrete structure faces in today’s world. Considering the heterogeneity of concrete and the contrasting nature of the constituents at high temperatures (cement paste dehydrates and shrinks while aggregate expands in volume), the use of a homogeneous macroscopic model to predict concrete's thermo-mechanical performance is questionable. Therefore, a more realistic description of concrete (in terms of explicit consideration of coarse aggregate and cement mortar rather than homogenized concrete medium) is needed to characterize the behavior of a concrete structure under such severe loading conditions (e.g., fire). In fact, the performance of a macro-scale (meter size or higher) concrete structure is the coupled effect of the various physico-chemical processes that occur within its constituents at meso-scale, a scale (mm-cm scale size) where coarse aggregates are randomly distributed in a mortar paste medium. Therefore, it is required to develop a model that explicitly considers the constituents' contrasting nature and the different thermo-hygral-mechanical processes at the scale of occurrence of these processes. Hence, in this contribution, a meso-scale based model is developed in a coupled hygro-thermo-chemo-mechanical (HTCM) framework to investigate the effect of high temperature on the thermo-mechanical performance numerically (e.g., in terms of spalling, deformation, residual capacity, etc.) of fire-exposed plain and reinforced concrete structural elements (e.g., slab, beam, etc.). Simulated results and validation with experimental data (taken from the literature) highlight several crucial aspects related to obtaining a more precise residual capacity of a concrete structure, which is impossible to reproduce with a homogenized macroscopic model. The study highlights that the failing of random concrete parts at different times during high-temperature exposure can not be simulated with a homogenized assumption. Further, a mesoscopic model does not require transient creep strain to be specified explicitly. The primary influencing factors behind this transient creep strain, such as the different thermo-chemical and thermo-mechanical damage of the concrete constituents (cement paste, aggregate), thermal-incompatibility of the components and associated damages, etc., are implicitly taken into account in a meso-scale model.

Chaos in gene regulatory networks: Effects of time delays and interaction structure.

In biological system models, gene expression levels are typically described by regulatory feedback mechanisms. Many studies of gene network models focus on dynamical interactions between components, but often overlook time delays. Here we present an extended model for gene regulatory networks with time delayed negative feedback, which is described by delay differential equations. We analyze nonlinear properties of the model in terms of chaos and compare the conditions with the benchmark homogeneous gene regulatory network model. Chaotic dynamics depend strongly on the inclusion of time delays, but the minimum motifs that show chaos differ when both original and extended models are considered. Our results suggest that, for a particular higher order extension of the gene network, it is possible to observe chaotic dynamics in a two-gene system without adding any self-inhibition. This finding can be explained as a result of modification of the original benchmark model induced by previously unmodeled dynamics. We argue that the inclusion of additional parameters in regulatory gene circuit models substantially enhances the likelihood of observing non-periodic dynamics.