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Wind-fed models offer a unique way to form predictive models of the accretion flow surrounding Sagittarius A*. We present 3D wind-fed magnetohydrodynamic (MHD) and general relativistic magnetohydrodynamic (GRMHD) simulations spanning the entire dynamic range of accretion from parsec scales to the event horizon. We expand on previous work by including non-zero black hole spin and dynamically evolved electron thermodynamics. Initial conditions for these simulations are generated from simulations of the observed Wolf–Rayet stellar winds in the Galactic Centre. The resulting flow tends to be highly magnetized (β ≈ 2) with an ∼r−1 density profile independent of the strength of magnetic fields in the winds. Our simulations reach the magnetically arrested disc (MAD) state for some, but not all cases. In tilted flows, standard and normal evolution (SANE) jets tend to align with the angular momentum of the gas at large scales, even if that direction is perpendicular to the black hole spin axis. Conversely, MAD jets tend to align with the black hole spin axis. The gas angular momentum shows similar behaviour: SANE flows tend to only partially align while MAD flows tend to fully align. With a limited number of dynamical free parameters, our models can produce accretion rates, 230 GHz flux, and unresolved linear polarization fractions roughly consistent with observations for several choices of electron heating fraction. Absent another source of large-scale magnetic field, winds with a higher degree of magnetization (e.g. where the magnetic pressure is 1/100 of the ram pressure in the winds) may be required to get a sufficiently large rotation measure with consistent sign.

Virus infection dynamics is governed by the processes on multiple scales: on the whole organism level, tissue level, and intracellular level. In this paper, we develop a multi-scale multi-compartment model of HIV infection in a simplified setting and the computational methods for numerical realization of the model. The multiscale model describes the processes from various scales and of different nature (cell motility, virus diffusion, intracellular virus replication). Intracellular replication model is based on a Markov chain with time-inhomogeneous propensities that depend on the extracellular level of virions. Reaction diffusion equations used to model free virion diffusion in the lymphoid tissue have moving sources, which are determined by the positions of the infected cells (immune cell motility model) and the rate of virion secretion from them (intracellular model). Immune cell motility model parameterizes the intercellular interaction forces, friction and the stochastic force of active cell motility. Together, this allows for a proper description of the intracellular stochasticity that propagates across multiple scales. A hybrid discrete-continuous stochastic-deterministic algorithm for simulation of the multiscale model based on the uniformization Monte Carlo method is implemented.

Based on a multiscale approach, the in-home built Fortran code SIMFC allows for high-temperature fuel cell simulation from material intrinsic properties to system overall operation.

The healthcare industry is different from other industries–patient data are sensitive, their storage needs to be handled with care and in compliance with regulative, while prediction accuracy needs to be high. This fast expansion in medical image modalities and data collection leads to generation of so called “Big Data” which is time-consuming to be analyzed by medical experts. This paper provides an insight into the Big Data from the aspect of its role in multiscale modelling. Special attention is paid to the workflow, starting from medical image processing all the way to creation of personalized models and their analysis. A review of literature regarding Big Data in healthcare is provided and two proposed solutions are described–carotid artery ultrasound image processing and 3D reconstruction, and drug testing on personalized heart models. Related to the carotid artery ultrasound image processing, the starting point is ultrasound images, which are segmented using convolutional neural network U-net, while segmented masks were further used in 3D reconstruction of geometry. Related to the drug testing on personalized heart model, similar approach was proposed, images were used in creation of personalized 3D geometrical model that is used in computational modelling to determine pressure in the left ventricle before and after drug testing. All the aforementioned methodologies are complex, include Big Data analysis and should be performed using servers or high-performance computing. Future development of Big Data applications in healthcare domains offers a lot of potential due to new data standards, rapid development of research and technology, as well as strong government incentives.

Our study provides a global assessment of water balance components accounting for the uncertainty in globally available precipitation products. This assessment is carried out consistently using a multiscale modelling framework established over more than 6000 GRDC river basins at various spatial resolutions (daily time step, period 1990-2019). The framework is based on the mesoscale Hydrologic Model (mHM; [1,2,3]) equipped with the multiscale parameter regionalization (MPR) scheme. All basins share the same parameterization and are driven with four different state-of-the-art meteorological products: ERA5 reanalysis [4], MSWEP [5], and deterministic EM-Earth v2 [6]. Additionally, the hydrological simulations are benchmarked against E-OBS [7] over Europe and against locally interpolated 1km gridded rain gauge dataset over Germany.Our results show that EM-Earth clearly exhibits the best streamflow performance across North and South America and Asia with respect to the other products. The MSWEP is the best product in Africa, where the overall model’s performance is rather poor. In Australia, MSWEP and EM-Earth have comparable skills. In Europe, the differences get narrower, although slightly better performance is seen for EM-Earth, with a median performance of 0.5 of daily KGE (N basins=2000), mainly due to correcting the under-catch error of rain gauges which is not considered, e.g. in E-OBS. Furthermore, when we zoom into a subset consisting of medium-sized 200 German basins, the in-house high-resolution meteorologic product clearly overperformed all global products, mainly due to better captured temporal correlation and smaller biases. On the other hand, ERA5 leads to very strong positive biases over German basins using standard parameterization. Finally, our study contributes to discussions on objective quantification of the optimal spatial resolution of hydrological studies. [1] https://doi.org/10.5281/zenodo.5119952[2] https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2008WR007327 [3] https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2012WR012195[4] https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3803[5] https://hess.copernicus.org/articles/21/589/2017/hess-21-589-2017.html[6] https://journals.ametsoc.org/view/journals/bams/103/4/BAMS-D-21-0106.1.xml[7] https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2017JD028200

The atomistic structure of calcium-silicate-hydrate (C-S-H) has been studied by applying force field simulations and quantum mechanical methods. Large number of crystallographically equivalent structures were used to account for the disordered nature of C-S-H. An extensive set of periodic structures of C-S-H (with a variation of chemical composition) has been generated and optimised. Special attention has been paid to two crystallographic models for tobermorite, which are used for the construction of C-S-H models. Re-optimisation of atomic structures, initially optimised with ReaxFF force field, by quantum chemical methods (semiempirical and Density functional theory) have been performed. Although the main structural characteristics of C-S-H remain the same in all calculations, the balance between the amount of OH groups and water in the structure is different. The obtained structures (optimised with a different level of theory) are combined into an on-line database and can be used for a future simulation of C-S-H materials.

Mammalian cells produce up to 80% of the commercially available therapeutic proteins, with Chinese Hamster Ovary (CHO) cells being the primary production host. Manufacturing involves a train of reactors, the last of which is typically run in fed-batch mode, where cells grow and produce the required protein. The feeding strategy is decided a priori, from either past operations or the design of experiments and rarely considers the current state of the process. This work proposes a Model Predictive Control (MPC) formulation based on a hybrid kinetic-stoichiometric reactor model to provide optimal feeding policies in real-time, which is agnostic to the culture, hence transferable across CHO cell culture systems. The benefits of the proposed controller formulation are demonstrated through a comparison between an open-loop simulation and closed-loop optimization, using a digital twin as an emulator of the process.

Food processing and consumption involves multiple contacts between biological fluids and solid materials of processing devices of which steel is one of the most common. Due to complexity of these interactions, it is difficult to identify the main control factors in the formation of undesirable deposits on the device surfaces that may affect safety and efficiency of the processes. Mechanistic understanding of biomolecule-metal interactions involving food proteins could improve management of these pertinent industrial processes and consumer safety in food industry and beyond. In this work, we perform a multiscale study of formation of protein corona on iron surfaces and nanoparticles in contact with cow milk proteins. By calculating the binding energies of proteins with the substrate we quantify the adsorption strength and rank proteins by the adsorption affinity. We use a multiscale method involving all-atom and coarse-grained simulations as well as generation of 3D structures of the proteins for this purpose. Finally, using the adsorption energy results, we predict the composition of protein corona on iron nanoparticles and flat surfaces via a competitive adsorption model.

Solid oxide fuel cells are high-efficiency renewable energy devices and considered one of the most promising net-zero carbon energy technologies. Numerical modelling is a powerful tool for the virtual design and optimisation of the next-generation solid oxide fuel cells but needs to tackle issues for incorporating the multi-scale character of the cell and further improving the accuracy and computational efficiency. While most of solid oxide fuel cell models were developed based on closed source platforms which limit the freedom of customisation in numerical discretization schemes and community participation. Here, an open source multi-physics and multiscale platform for advanced SOFC simulations consisting of both cell- and pore-scale performance models was developed using OpenFOAM. The modelling aspects are elucidated in detail, involving the coupling of various physical equations and the implementation of the pore-scale electrode in the performance model. The entire platform was carefully validated against experimental data and the other numerical models which were implemented in commercial software ANSYS Fluent and based on the lattice Boltzmann method. The cell-scale model is subsequently employed to study the effects of different fuels, i.e., pure hydrogen and different ratios of pre-reformed methane gas under various operating temperatures. It is found that the cell-scale model reasonably predicts the effects of these parameters on the cell performance, aligning well with the Fluent model. This study further identified the size of representative element volume with respect to the current density for the anode via the pore-scale model where the realistic microstructures reconstructed by a Xe plasma focused ion beam–scanning electron microscopy are employed as computational domains. It is found that a volume element size of 1243 voxels is sufficient to yield the representative current density of the whole. All these numerical investigations show that OpenFOAM is a potential multi-physics and multi-scale computational platform that is capable of accurately predicting both cell-scale and pore-scale performance and spatial information of solid oxide fuel cells. The developed models are also made public in GitHub to inspire community to further develop around it.

This paper aims at broadening the perspective in life cycle assessment methodology, exploiting multiscale modelling towards the generation of life cycle inventory data during an early-stage product design. Our approach involves the usage of molecular modelling techniques, such as electronic, atomistic or mesoscale models, in combination with continuum models, such as process simulation or finite element methods, to provide data for the generation of life cycle inventories of complex materials and production processes. In particular, each simulation is performed at a specific length and time scale through dedicated software, passing information from the lower to the upper scale. A practical application of the proposed approach will be illustrated for property predictions of nanostructured polymer systems used for manufacturing a marine engine cover. The material formulations capable of fulfilling the marine thermomechanical constraints were defined using in-silico techniques, allowing for the generation of materials life cycle inventories, which have been compared to identify the most environmentally friendly option.

A better understanding of the time-dependent biomechanical behaviour of the biphasic hip articular cartilage (AC) under physiological loadings is important to understand the onset of joint pathology and guide the clinical treatment. Current computational studies for the biphasic hip AC were usually limited to short-term duration or using elaborate loading. The present study aimed to develop a multiscale computational modelling to investigate the long-term biphasic behaviour of the hip AC under physiological loadings over multiple gait cycles. Two-scale computational modelling including a musculoskeletal model and a finite element model of the natural hip was created. These two models were then combined and used to investigate the biphasic behaviour of hip AC over 80 gait cycles. The results showed that the interstitial fluid pressure in the AC supported over 89% of the loading during gait. When the contact area was located at the AC centre, the contact pressure and fluid pressure increased over time from the first cycle to the 80th cycle, while when the contact area approached the edge, these pressures decreased first dramatically and then slowly over time. The peak stresses and strains in the solid matrix of the AC remained at a low level and increased over time from the first cycle to the 80th cycle. This study demonstrated that the long-term temporal variations of the biphasic behaviour of hip AC under physiological loadings are significant. The methodology has potentially important implications in the biomechanical studies of human cartilage and supporting the development of cartilage substitution.

The influence of the inherent fabric anisotropy of sand and its evolution on the footing bearing capacity and failure mechanism has been modelled and analysed with a multiscale approach that couples the finite-element method (FEM) and the discrete-element method (DEM). In this approach, the material constitutive responses are captured by DEM simulations on representative volume elements (RVEs) assigned to the FEM Gauss points. The inherent fabric anisotropy of sand is reflected by the bedding plane angle α of the RVEs composed of elliptic particles, where α measures the average angle between the long axes of the particles and the horizontal direction. The bearing capacity is found to decrease with an increasing α and the reduction can reach as high as 25%. Asymmetric deformation has been observed in the α other than 0° or 90° cases, which is mainly attributable to the different relative principal directions between the stress and the fabric at the two sides of the domain. Further multiscale analyses reveal that the load-bearing structure of the material expressed by the contact-normal-based fabric tensor is more fragile under the footing loading at one side of the domain and in larger α cases, which offers a micromechanical explanation to the observation of the asymmetric deformation and the decreased bearing capacity with increased α.

In recent years, multiscale modelling approach has begun to receive an overwhelming appreciation as an appropriate technique to characterize the complexity of infectious disease systems. In this study, we develop an embedded multiscale model of paratuberculosis in ruminants at host level that integrates the within-host scale and the between-host. A key feature of embedded multiscale models developed at host level of organization of an infectious disease system is that the within-host scale and the between-host scale influence each other in a reciprocal (i.e., both) way through superinfection, that is, through repeated infection before the host recovers from the initial infectious episode. This key feature is demonstrated in this study through a multiscale model of paratuberculosis in ruminants. The results of this study, through numerical analysis of the multiscale model, show that superinfection influences the dynamics of paratuberculosis only at the start of the infection, while the MAP bacteria replication continuously influences paratuberculosis dynamics throughout the infection until the host recovers from the initial infectious episode. This is largely because the replication of MAP bacteria at the within-host scale sustains the dynamics of paratuberculosis at this scale domain. We further use the embedded multiscale model developed in this study to evaluate the comparative effectiveness of paratuberculosis health interventions that influence the disease dynamics at different scales from efficacy data.

Light-weight fabric membranes have gained increasing popularity over the past years due to their tailorable structural and material performances. These tailorable properties include stretch forming and deep drawing formability that exhibits excellent stretchability and drapeability properties of textiles and textile composites. Since the inception of computerised numerical control for three-dimensional textile-manufacturing machines, technical textiles paved their way to numerous applications, certainly not limited to; aerospace, biomedical, civil engineering, defence, marine and medical industries. Digital interlooping and digital interlacing technology in additive manufacturing greatly advanced the manufacturing processes of textiles. In this work, we consider two branches of technical fabrics, namely plain-woven and weft-knitted. Multiscale modelling is the tool of choice for homogenising periodic structures and has been used extensively to model and analyse the mechanical behaviour of woven and knitted fabrics. But there is a plethora of literature discussing the demerits of such conventional multiscale modelling. These demerits include higher computational costs, rigid numerical models, ineffcient algorithmic computations and inability to incorporate geometric nonlinearities. We propose a data-driven nonlinear multiscale modelling technique to analyse the complex mechanical behaviour of plain-woven and weft-knitted fabrics with a neat extension to fabric material designing. We show how the integration of statistical learning techniques mitigates the weaknesses of conventional multiscale modelling. Moreover, we discuss the avenues that will open in many potential fields with regard to material modelling, structural engineering and textile industries. In the proposed data-driven nonlinear computational homogenisation technique, we effi ciently integrate the microscale and macroscale using Gaussian Process Regression (GPR) statistical learning technique. In the microscale, representative volume elements (RVEs) are modelled using nite deformable isogeometric spatial rods and deformation is homogenised using periodic boundary conditions. This nite deformable rod is profi cient in handling large deformations, rod-to-rod contacts, arbitrary cross-section de finitions and follower loads. Respecting the principle of separation of scales, we construct response databases by applying different homogenised strain states to the RVEs and recording the respective incremental volume-averaged energy values. We use GPR to learn a model using a 5-fold cross-validation technique by optimising the log marginal likelihood. In the macroscale, textiles are modelled as nonlinear orthotropic membranes for which the stresses and material constitutive relations are predicted by the trained GPR model. This coupling between GPR and membrane models is achieved through a systematic and seamless nite element integration using C++ and Python environments. A neat extension to material designing is also discussed with potentials to extend the work into other related fi elds., Cambridge trust and Trinity Hall scholarship

Acetohydroxamic acid (AHA) is a small organic acid with a wide variety of industrial, biological, and pharmacological applications. A deep fundamental molecular level understanding of the mechanisms responsible for the radical-induced reactions of AHA in these environments is necessary to predict and control their behaviour and elucidate their interplay with other attendant chemical species, for example, the oxidative degradation products of AHA. To this end, we present a comprehensive, multiscale computer model for interrogating the radical-induced degradation of AHA in acidic aqueous solutions. Model predictions were critically evaluated by a systematic experimental radiation chemistry investigation, leveraging time-resolved electron pulse irradiation techniques for the measurement of new radical reaction rate coefficients, and steady-state gamma irradiations for the identification and quantification of AHA degradation products: acetic acid, hydroxylamine, nitrous oxide, and molecular hydrogen, with formic acid and methane as minor products. Excellent agreement was achieved between calculation and experiment, indicating that this fundamental model can accurately predict the degradation pathways of AHA under irradiation in acidic aqueous solutions.

Molecular modelling applications in metalloenzyme design are still scarce due to a series of challenges. In top of that, the simulations of metal-mediated binding and the identification of catalytic competent geometries requiring both large conformational exploration and simulation of fine electronic properties. Here, we demonstrate how the incorporation of new tools in multiscale strategies, namely substrate diffusion exploration, allow taking a step further. As a showcase the enantioselective profiles of the most outstanding variants of an artificial Rh2-based cyclopropanase (GSH, HFF and RFY) developed by Lewis and co-workers have been rationalized. DFT calculations on the free-cofactor-mediated process identify the carbene insertion and the cyclopropanoid formation as crucial events, being the latter the enantiodetermining step, which displays up to 8 competitive orientations easily altered by the protein environment. The key intermediates of the reaction were docked into the protein scaffold showing that some mutated residues have direct interaction with the cofactor and/or the co-substrate. These interactions take form of a direct coordination of Rh in GSH and HFF and a strong hydrophobic patch with the carbene moiety in RFY. Posterior molecular dynamics sustain that the cofactor induces global re-arrangements of the protein. Finally, massive exploration of substrate diffusion, based on the GPathFinder approach, defines this event as the origin of the enantioselectivity in GSH and RFY. For HFF, fine molecular dockings suggest that it is likely related to local interactions upon diffusion. This work shows how modelling of long-range mutations on the catalytic profiles of metalloenzymes may be unavoidable and software simulating substrate diffusion should be applied.

A multiscale mathematical model describing the genesis and ecology of algal-bacterial photogranules and the metals biosorption on their solid matrix within a sequencing batch reactor (SBR) is presented. The granular biofilm is modelled as a spherical free boundary domain with radial symmetry and a vanishing initial value. The free boundary evolution is governed by an ODE accounting for microbial growth, attachment and detachment phenomena. The model is based on systems of PDEs derived from mass conservation principles. Specifically, two systems of nonlinear hyperbolic PDEs model the growth of attached species and the dynamics of free adsorption sites; and two systems of quasi-linear parabolic PDEs govern the diffusive transport and conversion of nutrients and metals. The model is completed with systems of impulsive ordinary differential equations (IDEs) describing the evolution of dissolved substrates, metals, and planktonic and detached biomasses within the granular-based SBR. All main phenomena involved in the process are considered in the mathematical model. Moreover, the dual effect of metal presence on the formation process of photogranules is accounted: metal stimulates the production of EPS by sessile species and negatively affects the metabolic activities of microbial species. To describe the effects related to metal presence, a stimulation term for EPS production and an inhibition term for metal are included in all microbial kinetics. The model is used to examine the role of the microbial species and EPS in the adsorption process, and the effect of metal concentration and adsorption proprieties of biofilm components on the metal removal. Numerical results show that the model accurately describes the photogranules evolution and ecology and confirm the applicability of algal-bacterial photogranules systems for metal-rich wastewater treatment., Comment: 43 pages, 18 figures, preprint version

How morphogenesis depends on cell properties is an active direction of research. Here, we focus on mechanical models of growing plant tissues, where microscopic (sub)cellular structure is taken into account. In order to establish links between microscopic and macroscopic tissue properties, we perform a multiscale analysis of a model of growing plant tissue with subcellular resolution. We use homogenization to rigorously derive the corresponding macroscopic tissue scale model. Tissue scale mechanical properties are computed from all microscopic structural and material properties, taking into account deformation by the growth field. We then consider case studies and numerically compare the detailed microscopic model and the tissue-scale model, both implemented using finite element method. We find that the macroscopic model can be used to efficiently make predictions about several configurations of interest. Our work will help making links between microscopic measurements and macroscopic observations in growing tissues.

Propane (C3H8) and butane (C4H10) are short straight-chain alkane molecules that are difficult to convert catalytically. Analogous to propane, butane can be dehydrogenated to butenes (also known as butylenes) or butadiene, which are used industrially as raw materials when synthesizing various chemicals (plastics, rubbers, etc.). In this study, we present results of detailed first-principles-based multiscale modelling of butane dehydrogenation, consisting of three size- and time-scales. The reaction is modelled over Cr2O3(0001) chromium oxide, which is commonly used in the industrial setting. A complete 108-step reaction pathway of butane (C4H10) dehydrogenation was studied, yielding 1-butene (CH2CHCH2CH3) and 2-butene (CH3CHCHCH3), 1-butyne (CHCCH2CH3) and 2-butyne (CH3CCCH3), butadiene (CH2CHCHCH2), butenyne (CH2CHCCH), and ultimately butadiyne (CHCCCH). We include cracking and coking reactions (yielding C1, C2, and C3 hydrocarbons) in the model to provide a thorough description of catalyst deactivation as a function of the temperature and time. Density functional theory calculations with the Hubbard U model were used to study the reaction on the atomistic scale, resulting in the complete energetics and first-principles kinetic parameters for the dehydrogenation reaction. They were cast in a kinetic model using mean-field microkinetics and kinetic Monte Carlo simulations. The former was used to obtain gas equilibrium conditions in the steady-state regime, which were fed in the latter to provide accurate surface kinetics. A full reactor simulation was used to account for the macroscopic properties of the catalytic particles: their loading, specific surface area, and density and reactor parameters: size, design, and feed gas flow. With this approach, we obtained first-principles estimates of the catalytic conversion, selectivity to products, and time dependence of the catalyst activity, which can be paralleled to experimental data. We show that 2-butene is the most abundant product of dehydrogenation, with selectivity above 90% and turn-over frequency above 10–3 s–1 at T = 900 K. Butane conversion is below 5% at such low temperature, but rises above 40% at T > 1100 K. Activity starts to drop after ∼6 h because of surface poisoning with carbon. We conclude that the dehydrogenation of butane is a viable alternative to conventional olefin production processes.

The cane toad (Bufo marinus) is an invasive species in Australia that has a negative impact on native species. Control methods such as trapping, fencing, and water exclusion have been devised to contain the spread of cane toads and reduce their ecological impact. However, implementing these interventions is expensive, and estimating the likely impact of a proposed intervention on spread at a large spatial scale, comprising one or more control methods, is challenging due to the lack of large-scale data and the computational cost of modelling a large number of toads.To address this challenge, we developed a multiscale model which uses individual-level data on cane toad behaviour to estimate the impact of trapping, fencing, and water exclusion when applied at scale in the Pilbara region in north-western Australia. Compared to previous work, our model allows us to explore more complex combinations and tradeoffs of control methods by utilising data sources at different scales.Our results suggest that exclusion of toads from water points is the most effective method for containing the spread of cane toads, and that trapping and fencing alone are unlikely to be sufficient. However, trapping and fencing are still useful supplementary measures in scenarios where exclusion cannot be broadly applied to a large number of water points.Synthesis and applications. Our analyses highlight the importance of limiting access to sheltering and breeding sites in invasive species control. Furthermore, this study illustrates the value of multiscale computational models for exploring scenarios where parameters and calibration data are available at the scale of individuals and small groups, but management questions are framed at a much larger scale.

Acetohydroxamic acid (AHA) has been proposed for inclusion in advanced, single-cycle, used nuclear fuel reprocessing solvent systems for the reduction and complexation of plutonium and neptunium ions. For this application, a detailed description of the fundamental degradation of AHA in dilute aqueous nitric acid is required. To this end, we present a comprehensive, multiscale computer model for the coupled radiolytic and hydrolytic degradation of AHA in aqueous sodium nitrate and nitric acid solutions. Rate coefficients for the reactions of AHA and hydroxylamine (HA) with the oxidizing nitrate radical were measured for the first time using electron pulse radiolysis and used as inputs for the kinetic model. The computer model results are validated by comparison to experimental data from steady-state gamma ray irradiations, for which the agreement is excellent. The presented model accurately predicts the yields of the major degradation products of AHA: acetic acid, HA, nitrous oxide, and molecular hydrogen.

Radiosensitizers have proven to be an effective method of improving radiotherapy outcomes, with the distribution of particles being a crucial element to delivering optimal treatment outcomes due to the short range of effect of these particles. Here we present a computational model for the transport of nanoparticles within the tumour, whereby the fluid velocity and particle deposition are obtained and used as input into the convection-diffusion equation to calculate the spatio-temporal concentration of the nanoparticles. The effect of particle surface charge and injection locations on the distribution of nanoparticle concentration within the interstitial fluid and deposited onto cell surfaces is assessed. The computational results demonstrate that negatively charged particles can achieve a more uniform distribution throughout the tumour as compared to uncharged or positively charged particles, with particle volume within the fluid being 100% of tumour volume and deposited particle volume 44.5%. In addition, varying the injection location from the end to the middle of the tumour caused a reduction in particle volume of almost 20% for negatively charged particles. In conclusion, radiosensitizing particles should be negatively charged to maximise their spread and penetration within the tumour. Choosing an appropriate injection location can further improve the distribution of these particles.

This work proposes a mathematical model on partial nitritation/anammox (PN/A) granular bioreactors, with a particular interest in the start-up phase. The formation and growth of granular biofilms is modelled by a spherical free boundary problem with radial symmetry and vanishing initial value. Hyperbolic PDEs describe the advective transport and growth of sessile species inhabiting the granules. Parabolic PDEs describe the diffusive transport and conversion of soluble substrates, and the invasion process mediated by planktonic species. Attachment and detachment phenomena are modelled as continuous and deterministic fluxes at the biofilm-bulk liquid interface. The dynamics of planktonic species and substrates within the bulk liquid are modelled through ODEs. A simulation study is performed to describe the start-up process of PN/A granular systems and the development of anammox granules. The aim is to investigate the role that the invasion process of anaerobic ammonia-oxidizing (anammox) bacteria plays in the formation of anammox granules and explore how it affects the microbial species distribution of anaerobic ammonia-oxidizing, aerobic ammonia-oxidizing, nitrite-oxidizing and heterotrophic bacteria. Moreover, the model is used to study the role of two key parameters in the start-up process: the anammox inoculum size and the inoculum addition time. Numerical results confirm that the model can be used to simulate the start-up process of PN/A granular systems and to predict the evolution of anammox granular biofilms, including the ecology and the microbial composition. In conclusion, after being calibrated, the proposed model could provide quantitatively reliable results and support the start-up procedures of full-scale PN/A granular reactors.

Phosphate…π, also called anion…π, contacts occur between nucleobases and phosphate OP oxygens in r(GNRA) and r(UNNN) U-turn motifs (N = A,G,C,U; R = A,G). We investigated these contacts in detail by using state-of-the-art quantum chemical methods (QM) to characterize some of their physico-chemical properties and to evaluate the ability of the AMBER force field (AFF) to describe these contacts. We found that AFF interaction energies of phosphate…π contacts calculated for model dimethyl phosphate…nucleobase systems are less stabilizing in comparison with double-hybrid DFT methods and that the minimum contact distances are stretched for all nucleobase systems. This distance stretch is also observed in large-scale AFF computations on several r(gcGNRAgc) tetraloop hairpins when compared to QM/MM. Further, classical molecular dynamics (MD) simulations of these tetraloop hairpins confirm this distance stretch and reveal shifted OP2/nucleobase positions when compared to experimental data extracted from high-resolution X ray/cryo EM structures (≤ 2.5 Å) of r(GNRA) tetraloops using the WebFR3D bioinformatic tool. We propose that discrepancies between QM and AFF are caused by a combination of missing polarization, too large AFF Lennard-Jones (LJ) radii of nucleobase carbon atoms and exaggerated short-range repulsion due to an approximate r−12 LJ repulsive term. We put these results in regard with those obtained in earlier investigations on lone pair…π contacts occurring in CpG Z-steps. Charge-transfer calculations do not support any significant n->π* donation effects and hence this label is inappropriate. We also investigated thiophosphate…π contacts for which we calculated less stabilizing interaction energies than for the phosphate…π contacts. We thus challenge suggestions that the experimentally observed enhanced thermodynamic stability of phosphorothioated r(GNRA) tetraloops can be straightforwardly explained by larger London dispersion.

Astrocytes with their specialized morphology are essential for brain homeostasis as metabolic mediators between blood vessels and neurons. In neurodegenerative diseases such as Alzheimer’s disease (AD), astrocytes adopt reactive profiles with molecular and morphological changes that could lead to the impairment of their metabolic support and impact disease progres-sion. However, the underlying mechanisms how metabolic function of human astrocytes is impaired by their morphological changes in AD is still elusive. To address this challenge, we developed and applied a metabolic multiscale modelling approach integrating the dynamics of metabolic energy pathways and physiological astrocyte morphologies acquired in human AD and age-matched control brain samples. The results demonstrate that the complex cell shape and intracellular organization of energetic pathways determine the metabolic profile and support capacity of astrocytes in health and AD conditions. Thus, our mechanistic approach indicates the importance of spatial orchestration in metabolism and allows for the identification of protective mechanisms against disease-associated metabolic impairments.

In this study, near-neutrally stratified planetary boundary layer (PBL) flows over complex terrain are numerically investigated via a one-way coupling of mesoscale numerical weather prediction (NWP) with unsteady computational fluid dynamics (CFD) codes. First, empirical turbulence model constants are modified to accomplish consistent turbulent viscosities between PBL schemes of mesoscale NWP and the $$k - \omega$$ turbulence closure of microscale CFD. Next, to reconstruct turbulent inflows calculated from coarse NWP analysis, inverse distance weighted (IDW) interpolation is optimized and adopted around the coupling interfaces. This method has been shown to decrease prediction uncertainty, as high-aspect-ratio NWP grids are used. Compared with time-series measurements, the proposed approach presents wind velocities in agreement around both upstream and separated regions in the Askervein Hill case, although the turbulent kinetic energy on the leeward side is still underestimated. Another moderately complex terrain with an enlarged domain around Feng Men Hill also exhibits improved numerical results. Moreover, the unsteady simulations reveal that both wind speed-up and wake are highly correlated with inflow characteristics. The successful integration of the nesting simulation with a general CFD code demonstrates the flexibility and efficiency of this procedure in modeling high-resolution unsteady flows over complex terrain.

Data availability statement: The data presented in this study are available on request from the corresponding author. Copyright: © 2021 by the authors. Aerostatic bearing slideways have been increasingly applied in the precision engineering industry and other high-tech sectors over the last two decades or so, due to their considerable advantages over mechanical slideways in terms of high motion accuracy, high speeds, low friction, and environment-friendly operations. However, new challenges in air bearings design and analysis have been occurring and often imposed along the journeys. An industrial-feasible approach for the design and development of aerostatic bearing slideways as standard engineering products is essential and much needed particularly for addressing their rapid demands in diverse precision engineering sectors, and better applications and services in a continuous sustainable manner. This paper presents the multiscale modelling and analysis-based approach for design and development of the aerostatic bearing slideways and its digital twin. The multiscale modelling and analysis and the associated simulation development can be the kernel of the digital twin, which cover the mechanical design, direct drive and control, dynamics tuning of the slideway, and their entire mechatronic system integration. Using this approach and implementation, the performance of an aerostatic bearing slideway can be predicted and assessed in the process. The implementation perspectives for the sideway digital twin are presented and discussed in steps. The digital simulations and digital twin system can be fundamentally important for continuously improving the design and development of aerostatic bearing slideways, and their applications and services in the context of industry 4.0 and beyond.

A major constraint of the behavioral epidemiological models is the underlying assumption that human behavior is static, however, it is known to be highly dynamic, especially in changing and vulnerable situations during a pandemic. To incorporate the dynamicity of human nature in the existing epidemiological models, in this work, we propose a population-wide multi-time-scale theoretical framework that assimilates neuronal plasticity as the basis of altering human emotions and behavior. For that, variable connection weights between different brain regions and their firing frequencies are coupled with a compartmental SIR model to estimate the contact rate. As an illustration, a model of fear conditioning in conjunction with awareness campaigns is developed and simulated. Results indicate that in the presence of fear conditioning there exists an optimum duration of daily broadcast time during which awareness campaigns are most effective in mitigating the pandemic. Further, global sensitivity analysis using the Morris method highlighted that learning rate and firing frequency of the unconditioned circuit are the key regulators in modulating the emergent pandemic waves. The present study makes a case for incorporating neuronal dynamics as a basis of behavioral immune response and has further implications in designing future awareness campaigns.