Entire Simulation Research Articles

Evaluation of Potential EGFR Inhibitors for Non-small Cell Lung Cancer: A Structure-based Virtual Screening and Molecular Dynamics Study

Non-small cell lung cancer is a leading cause of cancer deaths globally. EGFR is one of the attractive drug targets for non small cell lung cancer. It was first identified receptor tyrosine kinase receptor. It plays a critical role in the variety of biological activity apoptosis, cell cycle progression, differentiation, development, and transcription. Somatic mutation occurs in human EGFR. After that the normal function of EGFR was over expressed can leads to develop lung cancer. In this study we have identified three potential compounds using structure based virtual screening. Then thus compounds were evaluated ADME properties, all the compounds are under acceptable range with predicted ADME properties, then thus protein-ligand complex stability were carried out using Gromacs. The stability of the protein-ligand complex is stable throughout entire simulation, especially MET 769 is significant for stability of the complex. Furthermore, invitro cytotoxicity assay were performed for the best one compounds against non small cell lung cancer cell line.

Diverging Projections of Future Droughts in High‐End Climate Scenarios for Different Potential Evapotranspiration Methods: A National‐Scale Assessment for Poland

ABSTRACTIt has been broadly reported that future climate change will most likely affect the spatio‐temporal distribution of water resources and consequently droughts. There is a prevailing notion that an increase in temperature and frequency of heat waves are expected to result in more intense droughts in the coming years. In this study, we aimed to evaluate the effect of the potential evapotranspiration (PET) method selection on future drought projections over Poland. In our study, simulations of the Soil and Water Assessment Tool (SWAT) model were conducted, utilising an ensemble of six EURO‐CORDEX projections, spanning the period from 2006 to 2100 under the RCP8.5 scenario. Two model setups with two different PET methods (Penman‐Monteith—PM and Hargreaves—HAR) were used. For drought conditions evaluation we selected the Standardized Precipitation Index (SPI) and Standardized Precipitation‐Evapotranspiration Index (SPEI) for meteorological drought, Standardized Streamflow Index (SSI) for hydrological drought, and Standardized Soil Moisture Index (SMI) for agricultural drought. The meteorological and hydrological droughts were calculated using a 12‐month time aggregation window, while agricultural drought was calculated using a 3‐month window. Climate projections revealed that by 2080s annual mean temperature and precipitation increase is expected by up to +3.4°C and +10.3% respectively. Under future climate conditions duration and severity of meteorological droughts are projected to decrease. PM method leads to a higher PET increases (1.35 mm year−1) than the HAR method (1.1 mm year−1) throughout the century which entail diverging signal of change for agricultural and hydrological droughts. PM‐ and HAR‐based simulations indicate increase in the total duration and cumulative severity of agricultural droughts, buthowever, for HAR‐based projections, the increase is much less. For hydrological droughts the signal of change is similar for both PET methods, but considerably distinct in magnitude. Considering the entire simulation period, by the end of the century cumulative severity of hydrological droughts is projected to decrease, with a much more pronounced decline for HAR (70% reduction) than for the PM method (35% reduction). Our study demonstrated that methodological choices are crucial to the assessment of future drought risk under climate change.

Enhancing 3D planetary atmosphere simulations with a surrogate radiative transfer model

Abstract This work introduces an approach to enhancing the computational efficiency of 3D atmospheric simulations by integrating a machine-learned surrogate model into the OASIS global circulation model (GCM). Traditional GCMs, which are based on repeatedly numerically integrating physical equations governing atmospheric processes across a series of time-steps, are time-intensive, leading to compromises in spatial and temporal resolution of simulations. This research improves upon this limitation, enabling higher resolution simulations within practical timeframes. Speeding up 3D simulations holds significant implications in multiple domains. Firstly, it facilitates the integration of 3D models into exoplanet inference pipelines, allowing for robust characterisation of exoplanets from a previously unseen wealth of data anticipated from JWST and post-JWST instruments. Secondly, acceleration of 3D models will enable higher resolution atmospheric simulations of Earth and Solar System planets, enabling more detailed insights into their atmospheric physics and chemistry. Our method replaces the radiative transfer module in OASIS with a recurrent neural network-based model trained on simulation inputs and outputs. Radiative transfer is typically one of the slowest components of a GCM, thus providing the largest scope for overall model speed-up. The surrogate model was trained and tested on the specific test case of the Venusian atmosphere, to benchmark the utility of this approach in the case of non-terrestrial atmospheres. This approach yields promising results, with the surrogate-integrated GCM demonstrating above 99.0% accuracy and 147 factor GPU speed-up of the entire simulation compared to using the matched original GCM under Venus-like conditions.

Runtime integration of machine learning and simulation for business processes: Time and decision mining predictions

Recent research in Computer Science has investigated the use of Deep Learning (DL) techniques to complement outcomes or decisions within a Discrete Event Simulation (DES) model. The main idea of this combination is to maintain a white box simulation model complement it with information provided by DL models to overcome the unrealistic or oversimplified assumptions of traditional DESs. State-of-the-art techniques in BPM combine Deep Learning and Discrete Event Simulation in a post-integration fashion: first an entire simulation is performed, and then a DL model is used to add waiting times and processing times to the events produced by the simulation model.In this paper, we aim at taking a step further by introducing Rims (Runtime Integration of Machine Learning and Simulation). Instead of complementing the outcome of a complete simulation with the results of predictions a posteriori, Rims provides a tight integration of the predictions of the DL model at runtime during the simulation. This runtime-integration enables us to fully exploit the specific predictions while respecting simulation execution, thus enhancing the performance of the overall system both w.r.t. the single techniques (Business Process Simulation and DL) separately and the post-integration approach. In particular, the runtime integration ensures the accuracy of intercase features for time prediction, such as the number of ongoing traces at a given time, by calculating them during directly the simulation, where all traces are executed in parallel. Additionally, it allows for the incorporation of online queue information in the DL model and enables the integration of other predictive models into the simulator to enhance decision point management within the process model. These enhancements improve the performance of Rims in accurately simulating the real process in terms of control flow, as well as in terms of time and congestion dimensions. Especially in process scenarios with significant congestion – when a limited availability of resources leads to significant event queues for their allocation – the ability of Rims to use queue features to predict waiting times allows it to surpass the state-of-the-art. We evaluated our approach with real-world and synthetic event logs, using various metrics to assess the simulation model’s quality in terms of control-flow, time, and congestion dimensions.

Influence of polypropylene fibers on the tensile mechanical properties of calcium silicate hydrate: molecular simulation.

This research assesses the influence of polypropylene (PP) fibers, both homopolymer and hydroxylated (PPOH), on the tensile properties of calcium silicate hydrate (C-S-H) composites through molecular dynamics (MD) simulations. Our models explore C-S-H matrices integrated with PP and PPOH fibers at varying polymerization degrees. The results demonstrate that both PP and PPOH fibers significantly influence the tensile strength and Young's modulus of the composites. Notably, PPOH fibers contribute to more substantial mechanical enhancements than PP, attributed to the increased polarity and enhanced intermolecular interactions from the hydroxyl groups. The study reveals a nonlinear relationship between polymer additive content and mechanical performance, with optimal properties at a polymerization degree of 20. Additionally, stress-strain analysis indicates that PPOH composites exhibit superior ductility and fracture energy, particularly at polymerization degrees of 20, showing enhanced ultimate strain and fracture energy by up to 9.6% and 13.9%, respectively, compared to PP counterparts. These results highlight the crucial role of tailored polymer additive composition and chemical modifications in maximizing the mechanical efficacy of C-S-H-based materials, enhancing their durability and structural performance. All MD simulations were conducted using LAMMPS. The models employed a combination of Clayff and Cvff force fields. During the entire tensile simulation, the system was configured under the NPT ensemble at 300K.

Documenting research in simulation science to enhance understanding for reusability.

One goal of Open Science is to promote reusability, which requires understanding and documentation. Reusability spans a spectrum from the straightforward reuse of existing materials to the extraction and adaptation of specific elements, depending on the maturity of the reused research and the research context. Beyond knowledge, understanding is crucial for enabling reusability. Simply reading an article is often insufficient; thus, publishing the underlying data and software is recommended. While the FAIR principles (Findable, Accessible, Interoperable, Reusable) facilitate the discovery and legal reuse of data and software through metadata, they fall short of promoting comprehensive understanding. By applying insights from the epistemology of simulation to computational mechanics research cases, we tested our hypothesis that the simulation process involves critical components beyond software and data that are essential to understand for reuse. Our findings indicate that reusability in these cases predominantly involves adjusting existing methodologies-a combination of different process steps from an epistemological perspective. Therefore, it is imperative to document not only learned knowledge but also the decisions regarding adjustments, assumptions and applications that concern the entire simulation process. Documentation is vital for understanding and herby enabling true reusability in scientific research, aligning with philosophical considerations of transparency and the nature of scientific knowledge.

Impacts on Biogenic Carbon Dioxide Emission Fluxes Driven by Generated Numerical-Downscaled Climate Scenarios

The aim of this research is to analyze the local impacts (high spatial resolution) of tier 1 CMIP6 climate scenarios (SSP126, SSP245, SSP370 and SSP585) on CO2 biogenic emission fluxes after downscaled climate data over five European regions (national, regional and urban scale) for the period: 2015-2050 using numerical simulations with the WRF/Chem-VPRM tool. VPRM is the Vegetation Photosynthesis and Respiration Model which is coupled with the Weather Research and Forecasting (WRF) model and Chem (Transport). Satellite data is used to derive vegetation indexes used by the VPRM model. The effect of climate is isolated using 2018 (reference year) emissions and land use over the entire simulation period and it is calculated as results of the future simulations minus present (2018). The research is part of the European DISTENDER project, who develops a methodological framework to bring together adaptation and mitigation strategies against the risks of climate change. The increase in temperature leads to higher CO2 emissions from vegetation, as the increase in temperature favors the respiration process of plants. The impacts are spatially and temporally varied and therefore each case study and scenario has its own pattern, with a strong influence on the existing vegetation and local climate Keywords: Climate change, CO2, biogenic emissions, scenarios, downscaling

Targeting cancer with novel tetra-1,2,3-triazole hybrids: Synthesis, biological evaluation, and molecular dynamics

This study involved the synthesis of a new category of tetra-1,2,3-triazole derivatives of types (I), (II), (III), and (IV) 7–14 using a Cu(I)-catalyzed Azide-Alkyne Cycloaddition (CuAAC) procedure, also known as the Click reaction. The reaction between terminal alkynes ethynylbenzene 5 and 2-ethynylpyrazine 6 with substituted aryl azides 1–4 yielded the corresponding 1,2,3-triazole-hybrids 7–14. The synthesized products were analyzed using NMR (1H and 13C), mass spectrometry, and elemental analysis. The anticancer activities of the synthesized compounds were assessed in vitro against three cancer cell lines: colorectal carcinoma (HCT-116), hepatoblastoma (HePG-2), breast adenocarcinoma (MCF-7), and a normal human cell line (WI-38) through the MTT assay. Notably, compounds 10, 14, and 9 demonstrated the most potent inhibitory effects on colon, liver, and breast cancer cells, respectively, while maintaining low toxicity towards normal cells compared to doxorubicin. Moreover, the molecular dynamics simulations revealed that compound 10 demonstrated significant interactions with essential residues of the binding site, resulting in a stable complex throughout the entire simulation run. As a result of these findings, it can be concluded that these tetra-1,2,3-Triazole hybrids may prove to be promising candidates for development as novel anticancer agents.

Numerical Investigation of the Fuel Tank Sloshing Condition of a Commercial Vehicle

This study investigates the impact of baffles on fuel sloshing behavior within truck fuel tanks using numerical simulations. The volume of the fluid multiphase model is employed to analyze the flow dynamics of 25% diesel fuel in a 250 L tank, modeled in a 3D domain. Two configurations were compared: a tank with baffles and one without. The primary focus is to analyze fuel distribution within the intake port region during vehicle acceleration and deceleration maneuvers. The simulated scenario mimics a realistic driving situation. The vehicle accelerates from 0 km/h to 60 km/h over 10 s, followed by a 3-s braking period to reach a complete stop (0 km/h) at the 13-s mark. The simulation then observes the fuel behavior within the tank for an addi-tional 7 s while the vehicle remains stationary. Results reveal significant differences in fuel behavior between baffled and unbaffled tanks. In the absence of baffles, the sloshing motion is substantial, leading to a complete depletion of fuel in the intake port region for a duration of 3 s during both the acceleration and deceleration phases (between 10 and 13 s). Compared to a standard tank, the presence of baffles significantly reduced the sloshing amplitude by approximately 70%. Furthermore, baffles led to a 50% decrease in pressure variations on the tank walls. Temporary fuel starvation can negatively impact engine performance and combustion efficiency. Conversely, the presence of baffles within the tank effectively mitigates sloshing and ensures continuous fuel presence at the intake port the entire simulation. This suggests that baffles play a crucial role in maintaining a stable and consistent fuel supply to the engine, even during dynamic vehicle maneuvers.

Computational Modeling Approach to Profile Hemodynamical Behavior in a Healthy Aorta.

Cardiovascular diseases (CVD) remain the leading cause of mortality among older adults. Early detection is critical as the prognosis for advanced-stage CVD is often poor. Consequently, non-invasive diagnostic tools that can assess hemodynamic function, particularly of the aorta, are essential. Computational fluid dynamics (CFD) has emerged as a promising method for simulating cardiovascular dynamics efficiently and cost-effectively, using increasingly accessible computational resources. This study developed a CFD model to assess the aorta geometry using tetrahedral and polyhedral meshes. A healthy aorta was modeled with mesh sizes ranging from 0.2 to 1 mm. Key hemodynamic parameters, including blood pressure waveform, pressure difference, wall shear stress (WSS), and associated wall parameters like relative residence time (RRT), oscillatory shear index (OSI), and endothelial cell activation potential (ECAP) were evaluated. The performance of the CFD simulations, focusing on accuracy and processing time, was assessed to determine clinical viability. The CFD model demonstrated clinically acceptable results, achieving over 95% accuracy while reducing simulation time by up to 54%. The entire simulation process, from image construction to the post-processing of results, was completed in under 120 min. Both mesh types (tetrahedral and polyhedral) provided reliable outputs for hemodynamic analysis. This study provides a novel demonstration of the impact of mesh type in obtaining accurate hemodynamic data, quickly and efficiently, using CFD simulations for non-invasive aortic assessments. The method is particularly beneficial for routine check-ups, offering improved diagnostics for populations with limited healthcare access or higher cardiovascular disease risk.

Application of Artificial Intelligence Technology in Optimizing Control Parameters of Traffic Signal Group Systems

On the basis of introducing the concept of intersection signal state phase difference, the author proposes a proportional signal phase difference design scheme for traffic signal system control, on the basis of the HCM signal delay calculation formula, a standardized signal cycle and effective green signal ratio optimization design model were compiled, thus forming a complete set of optimization design modes for the control parameters of the urban main road traffic signal group system. The experimental results indicate that, the entire simulation experiment took 6000 seconds to compare the traditional fuzzy algorithm with the controller implemented by the author's fuzzy neural network, each case was simulated 5 times, the data in the Table is the average of 5 times, compared with traditional fuzzy algorithms, the author's method shows an improvement of 20.8% to 45.67% in average vehicle delay time, especially when the traffic flow is different in the east-west and north-south directions, the improvement is more significant.

Simulation Studies of Interfacial Kinetics of Dye-Sensitized Solar Cells Photoanodes in Scanning Electrochemical Microscopy

Approach curves in the feedback mode of scanning electrochemical microscopy (SECM) were simulated for photoanodes in dye-sensitized solar cells by finite element simulation using COMSOL Multiphysics and compared to experimental data for the sample set with varying the photoanode film thickness (ZnO, cobalt mediator, organic dye). This reveals quantitatively the effect of the mass transport limitation of the mediator diffusion and organic dye regeneration kinetics in the mesoporous photoanode. The simulation of the mesoporous material was made with a pseudo-homogeneous approach in two space dimensions.The simulation allows to extract the maximum information content from the experimental data. The light adsorption was treated as exponential decay of intensity within the photoanode (Lambert-Beer law), electron injection in the conduction band of ZnO photoanode and loss mechanism was explicitly considered. The light illumination is homogeneous over the entire simulation domain of 1 mm in diameter. Just like in the experiment, the built geometry used for the simulation has a conducting blocking layer (0.6 µm in thickness) to prevent the recombination of electrons with the back contact. The porous dye-sensitized layer has a porosity of 50 - 60% and a thickness of 1- 10 µm. The inner surface holds the dye for absorbing light. The mediator diffuses in the pore of the photoanode (internal solution) and in the bulk of the electrolyte (external diffusion). The transparent conducting glass makes the boundary of the simulation domain. The electrolyte-platinum contact is not modelled. The selected boundary conditions are equivalent to the short-circuit operation of the cell.Interestingly, the simulation suggests that built meshes which had earlier been used for porous electrode simulation could not adequately describe the electron concentration profile for thinner electrodes despite providing a correct ME electrode current for limiting cases. The mesh convergence analysis studies were made to optimize the mesh to reduce computation time and memory size. The electrons in the conduction band were quantitatively determined because transport in the semiconducting photoanode is primarily driven by a thermally activated trapping-untrapping mechanism that can be described by transport equations similar to Fick's laws.The unknown parameters (dye absorption coefficient and recombination rate constant) were adjusted to ensure various light intensities in the simulation correspond to the experiment. An accurate description of the electrons within the semiconductor is critical for explaining the time-dependent behavior of the dye-sensitized photoanode in SECM approach curves. After obtaining agreement with experimental approach curves under steady-state conditions, the same parameters are used to study the current transient obtained under intermittent illumination.

Stellar Spin-down in Post-mass-transfer Binary Systems

Motivated by measurements of the rotation speed of accretor stars in post-mass-transfer (post-MT) systems, we investigate how magnetic braking affects the spin-down of individual stars during binary evolution with the MESAbinary module. Unlike the conventional assumption of tidal synchronization coupled with magnetic braking in binaries, we first calculate whether tides are strong enough to synchronize the orbit. Subsequently, this influences the spin-down of stars and the orbital separation. In this study, we apply four magnetic braking prescriptions to reduce the spin angular momentum of the two stars throughout the entire binary evolution simulation. Our findings reveal that despite magnetic braking causing continuous spin-down of the accretor, when the donor begins to transfer material onto the accretor, the accretor can rapidly spin up to its critical rotation rate. After MT, magnetic braking becomes more important in affecting the angular momentum evolution of the stars. Post-MT accretor stars thus serve as a valuable test bed for observing how the magnetic braking prescriptions operate in spinning down stars from their critical rotation, including the saturation regimes of the magnetic braking. The rotation rate of the accretor star, combined with its mass, could provide age information since the cessation of MT. By comparing the models against observations, the magnetic braking prescription by Garraffo et al. is found to better align with the rotation data of post-MT accretors.

Molecular dynamics simulation on crystal morphology of NH4ClO4.

Ammonium perchlorate (NH4ClO4, abbreviated as AP) has the advantages of high oxygen content, high density, and good compatibility, and has significant application prospects in the field of energetic materials. The crystal morphology has a great influence on the properties and sensibility of energetic materials, and a single experimental means is difficult in exploring the crystals; therefore, the crystal morphology of AP is investigated using molecular dynamics simulation complemented with experiments, to theoretically analyze the differences in AP crystal habit and the interactions between solvent molecules and the main growing crystal surfaces of AP. The results show that AP crystal is mainly composed of five independent crystal surfaces (0 0 1), (0 1 0), (1 0 0), (1 0 1), and (1 0 -1) in vacuum using the BFDH laws, with (0 0 1) surface being the main growth crystal surface. In contrast, in H2O solvent, the (1 0 1) and (1 0 -1) surfaces disappear, and the AP mainly consists of (0 0 1), (0 1 0), and (1 0 0) surfaces with a rectangular shape. The crystal morphology obtained from theoretical prediction is in good agreement with that obtained from experimental culture. This paper can provide a new idea for the cultivation and preparation of AP large crystals, and promote the application of AP crystals in energetic materials. The crystal morphologies of AP in vacuum and H2O solvent under Dreiding force field were predicted based on attachment energy model by using molecular dynamics method in Materials Studio 2019 software. The entire molecular dynamics simulation was carried out under the NTV system, the temperature control method was selected as Anderson, and the system temperature was set to 298 K. The simulation time was set to 40 ps, the step size was set to 1 fs, and the data were outputted every 5000 steps.

A stabilised Total Lagrangian Element-Free Galerkin method for transient nonlinear solid dynamics

AbstractThis paper presents a new stabilised Element-Free Galerkin (EFG) method tailored for large strain transient solid dynamics. The method employs a mixed formulation that combines the Total Lagrangian conservation laws for linear momentum with an additional set of geometric strain measures. The main aim of this paper is to adapt the well-established Streamline Upwind Petrov–Galerkin (SUPG) stabilisation methodology to the context of EFG, presenting three key contributions. Firstly, a variational consistent EFG computational framework is introduced, emphasising behaviours associated with nearly incompressible materials. Secondly, the suppression of non-physical numerical artefacts, such as zero-energy modes and locking, through a well-established stabilisation procedure. Thirdly, the stability of the SUPG formulation is demonstrated using the time rate of Hamiltonian of the system, ensuring non-negative entropy production throughout the entire simulation. To assess the stability, robustness and performance of the proposed algorithm, several benchmark examples in the context of isothermal hyperelasticity and large strain plasticity are examined. Results show that the proposed algorithm effectively addresses spurious modes, including hour-glassing and spurious pressure fluctuations commonly observed in classical displacement-based EFG frameworks.

Low-mass planets falling into gaps with cyclonic vortices

ABSTRACT We investigate the planetary migration of low-mass planets ($M_p\in [1,15]\, \mathrm{ M}_{\oplus }$, here $\mathrm{ M}_{\oplus }$ is the Earth mass) in a gaseous disc containing a previously formed gap. We perform high-resolution 3D simulations with the fargo3d code. To create the gap in the surface density of the disc, we use a radial viscosity profile with a bump, which is maintained during the entire simulation time. We find that when the gap is sufficiently deep, the spiral waves excited by the planet trigger the Rossby wave instability, forming cyclonic (underdense) vortices at the edges of the gap. When the planet approaches the gap, it interacts with the vortices, which produce a complex flow structure around the planet. Remarkably, we find a widening of the horseshoe region of the planet produced by the vortex at the outer edge of the gap, which depending on the mass of the planet differs by at least a factor of two with respect to the standard horseshoe width. This inevitably leads to an increase in the co-rotation torque on the planet and produces an efficient trap to halt its inward migration. In some cases, the planet becomes locked in co-rotation with the outer vortex. Under this scenario, our results could explain why low-mass planets do not fall towards the central star within the lifetime of the protoplanetary disc. Lastly, the development of these vortices produces an asymmetric temporal evolution of the gap, which could explain the structures observed in some protoplanetary discs.

Hemodynamics in Coronary Arteries: using Open-Source Software-Simvascular to Investigate the Hemodynamics in Coronary Arteries of the Patient-Specific Modeling

Patient-specific cardiovascular simulation is emerging as a potent tool for basic, translational, and clinical research and has established itself as a paradigm in the field of cardiovascular science. The SimVascular software package, considered a state-of-the-art open-source package, offers a comprehensive pipeline from medical imaging data segmentation to patient-specific blood flow simulation and analysis. In this study, we employ SimVascular to explore a model of coronary arteries in a young and healthy 24-year-old woman. The outcomes of the entire simulation process encompass the assessment of flow and pressure waveforms at both the outlet of the aorta and the coronary arteries, which serve as indicators of blood flow qualities within these vessels. Given the pivotal role of wall shear stress in the development of arterial plaque, SimVascular employs the finite element method to solve the governing equations of incompressible viscous fluids. This approach effectively tackles the Navier-Stokes equations along the vessel wall using meticulously constructed mesh components. Additionally, this paper delves into the issue of cardiovascular blood vessel dynamics via an analysis of wall shear stress data).

Numerical modelling of multiple excavations in an ultra-deep foundation using an enhanced distinct lattice spring model with modified cam clay model

The control of foundation deformation has garnered significant attention. This study focuses on understanding the deformation induced by excavation activities in real foundation construction scenarios. To accurately model the complex geomechanical responses, an enhanced Distinct Lattice Spring Model (DLSM) was developed. The Modified Cam Clay (MCC) model is employed to represent the nonlinear behaviour of the soil. Integration with one-dimensional structure elements is implemented to model support structures and their interactions with the soil. The birth–death particle method is utilized to simulate the excavation process of the foundation. To improve computational efficiency, a CPU-GPU heterogeneous parallel technique is employed. The enhanced DLSM produces consistent results with the finite element method when calculating foundation-bearing capacity and demonstrates relatively close results compared to finite difference methods when analyzing the stability of foundation slopes under different parameters. Finally, a three-dimensional numerical model is established using the proposed DLSM to simulate the deformation of the foundation caused by the excavations. The entire simulation task is divided into twelve stages, involving in-situ stress, a diaphragm wall, ten supporting structures, and ten excavations. A comparative analysis between the MCC model and the ZP (Zienkiewicz-Pande) model is conducted. The findings indicate that the calculation results of the two are consistent in the early stages of excavation, while those calculated by the MCC in the later stages are relatively small. Sensitivity analysis of the MCC parameters using orthogonal analysis reveals that the porosity of the soil has the greatest impact on the deformation of the foundation pit, followed by the slope of the swelling line, while the slope of the critical state line has a minimal impact. This work provides an alternative high-performance tool for engineering-scale deformation analysis of geotechnical underground excavation.

Transformer enables ion transport behavior evolution and conductivity regulation for solid electrolyte

Ab initio molecular dynamics (AIMD) is an important technique for studying ion transport within solid electrolyte and interface effects between electrode and electrolyte, which is particularly critical for the rational design of new energy materials. However, AIMD is limited by the high-cost density functional theory (DFT) solution process and is unable to reach the time scale of the entire dynamic simulation, resulting in time-consuming AIMD calculations and a considerable scarcity of AIMD-based conductor data. Here, we propose a sequence relational large model based on transformer (T-AIMD) to infer ion diffusion from mean square displacement sequence data and hybrid multi-source material descriptor. T-AIMD successfully learns the whole long-range atomic diffusion to predict the ionic conductivity (σ) of any ion in any crystal structure to find fast-ion conductors, thus reducing the cost of AIMD simulation by a factor of 100. Using T-AIMD, we built the largest database of mixed ion conductors, and the σ of representative solid electrolytes has been successfully validated in previous battery experiments. Further, the manufactured solid-state battery with the predicted promising electrolyte exhibits almost no obvious capacity decay after 50 cycles with a high initial specific capacity of 1270 mAh g−1, which is promising to help devices work in extreme environments while guaranteeing battery life. By speeding up the prediction time of AIMD, the proposed T-AIMD opens the door for scientists to explore the atomic and molecular behaviors of other molecules/materials on long time scales, and will ultimately benefit the exploration of other key scientific questions in the energy field.

A composite spread spectrum sequence for underwater acoustic signal acquisition

AbstractSpread spectrum technology has been widely employed for positioning and communicating with autonomous underwater vehicles (AUVs), but conventional spread spectrum sequences lack confidentiality and reliability in UWA channel. Considering the limitations of conventional sequences and the characteristics of underwater acoustic (UWA) channel, a composite chaotic orthogonal sequence (CCOS) based on the UWA channel is proposed. The confidentiality of the CCOS is superior to that of the m‐sequence, while the autocorrelation performance of the CCOS is superior to that of the orthogonal sequence. Moreover, the CCOS can compensate for the imbalance of logistic chaotic sequence when assigned certain initial values. Acquisition is a crucial component of accessing the spread spectrum signal; therefore, the acquisition performance indicates the applicability of the CCOS. The source–target model is established to simulate communication with an underwater moving target. To simulate the acquisition process, a parallel algorithm based on fast Fourier transform is adopted, and the entire simulation process is completed based on the BELLHOP ray acoustic model. Through data processing, the Doppler shift error is less than half of the frequency‐search element. Furthermore, the acquisition probabilities of the CCOS with different numbers of bits are over 90%, which demonstrates the reliability of the CCOS.