Simulation Time Research Articles

Investigation of the nanostructure and nanomechanical properties of the interphase in carbon fiber reinforced polyamide-6 composite

Carbon fiber reinforced polyamide-6 (CF/PA-6) composites have been widely applied in automobile, aerospace, and biomedical industries for their high mechanical properties, high thermal resistance and recyclability. On the purpose of finding ways to improve the interfacial properties, the investigation of the nanostructure and nanomechanical properties of the interphase in CF/PA-6 composites were essential. In this study, MD simulation was carried out to show the interfacial formation and nanostructure of the CF/PA-6 composite model directly at the atomic level and compute the radial distribution function, interfacial energy, total energy. Then the nanomechanical properties of the CF/PA-6 composite, such as interfacial thickness, interfacial modules, interfacial adhesion, were investigated by AFM PF-QNM model. The changes of the radical distribution function and energies over the MD simulation time indicated that the PA-6 chains adsorbed and then regularly folded on the CF surface, displaying the interfacial crystallization of the CF/PA-6 composite model. What stood out in the AFM PF-QNM tests were the abrupt decreasing of the interfacial modulus and the sharp increasing of the interfacial adhesion from those of the carbon fiber to those of the PA-6. The average interfacial thickness of the CF/PA-6 composite was 72 nm. Consistent with the simulation results, the interfacial properties were distinct from the properties of the carbon fiber and PA-6, owning to the adsorption and orderly folding of the PA-6 chains on the CF surface and the changes of the RDF and energies.

Embedment of WENO-Z reconstruction in Lagrangian WLS scheme implemented on GPU for strongly-compressible multi-phase flows

It is a notoriously challenge problem for Lagrangian particle methods to solve compressible flows with strong discontinuities and large volume variations. To overcome the problems of numerical oscillations, an accurate and stable Lagrangian Weighted-Least-Square (LWLS) method embedded with WENO-Z scheme (LWLS-WENO-Z) is proposed. To further enhance the numerical accuracy and stability, the particle shifting technique, the multi-level local-refinement technique, and the interface repulsive force are appropriately employed in the numerical scheme. Moreover, the smooth and stable coupled particle method between the LWLS and Roe's Riemann solver (LWLS-RR) is considered for the comparisons, further demonstrating the merit of the proposed LWLS-WENO-Z scheme. To reduce the simulation time, parallel computing with a CUDA-based program for the proposed scheme is implemented on a single GPU. In the numerical experiments, several 1D/2D benchmarks are solved to test the accuracy of the proposed method. Particularly, the challenge 2D underwater explosion and the cavitation bubble collapse and jetting are numerically investigated. Compared to other reference solutions, the present coupled particle method demonstrates robustness and superior accuracy in simulating these strongly-compressible multi-phase flows.

Design of encoded graphene-gold metasurface-based circular ring and square sensors for brain tumor detection and optimization using XGBoost algorithm

This study introduces a circular ring and square metasurfaces-based sensor employing graphene and gold layers for enhanced brain tumor detection. The sensor architecture includes dual graphene circular metasurfaces and a gold square resonator layer on a silicon dioxide substrate. A comprehensive parametric investigation explores the influence of geometric parameters and graphene chemical potential on sensor performance. The sensor achieves a remarkable sensitivity of 769 GHzRIU−1, a high-quality factor of 6.514, a low detection limit of 0.182 RIU, and an FOM of 1.148RIU−1. Electric field intensity analysis identifies optimal transmission frequencies. By employing the XGBoost regressor, the analysis significantly reduces simulation time and resource consumption by at least 80 %. Simulation validation confirms that despite 80 % reduction in simulation time, high prediction accuracy remains achievable, with an impressive R2 score ranging from 0.9 to 1. The proposed sensor presents a promising non-invasive approach for early brain tumor diagnosis, with potential applications in biomedical imaging and other related fields.

Ligand Gaussian Accelerated Molecular Dynamics 3 (LiGaMD3): Improved Calculations of Binding Thermodynamics and Kinetics of Both Small Molecules and Flexible Peptides.

Binding thermodynamics and kinetics play critical roles in drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics of small molecules and flexible peptides using conventional molecular dynamics (cMD), due to limited simulation time scales. Based on our previously developed ligand Gaussian accelerated molecular dynamics (LiGaMD) method, we present a new approach, termed "LiGaMD3″, in which we introduce triple boosts into three individual energy terms that play important roles in small-molecule/peptide dissociation, rebinding, and system conformational changes to improve the sampling efficiency of small-molecule/peptide interactions with target proteins. To validate the performance of LiGaMD3, MDM2 bound by a small molecule (Nutlin 3) and two highly flexible peptides (PMI and P53) were chosen as the model systems. LiGaMD3 could efficiently capture repetitive small-molecule/peptide dissociation and binding events within 2 μs simulations. The predicted binding kinetic constant rates and free energies from LiGaMD3 were in agreement with the available experimental values and previous simulation results. Therefore, LiGaMD3 provides a more general and efficient approach to capture dissociation and binding of both small-molecule ligands and flexible peptides, allowing for accurate prediction of their binding thermodynamics and kinetics.

Molecular structure, molecular docking, molecular dynamics simulation, and drug likeness evaluation of 3,7-dihydroxy-1,2-dimethoxyxanthone for its anticancer activity

A naturally occurring xanthone, named 3,7-dihydroxy-1,2-dimethoxyxanthone was experimentally and computationally investigated for its anticancer activity. Single crystal X-ray diffraction (XRD) analysis revealed that the compound crystallises in the triclinic space group P1. Vibrational frequencies, electronic properties, chemical reactivity descriptors, the molecular structure, and stability of the compound were investigated by DFT calculations using Gaussian 09 W program at the B3LYP/6–311+G(2d,p) level of theory. All calculations showed correlations to experimental data. Its thermodynamic parameters at various temperatures are presented. The title molecule was docked on the proteins of prostate, cervical, breast, and melanoma cancer cells, and the results showed comparable binding energies to the standard chemotherapeutic agent, doxorubicin with small differences in the range 0.2−0.7 kcal mol−1. Given that the compound showed significant potential on the proteins of prostate and cervical cancer cells, the dynamic properties of the bound state of title ligand with the two selected proteins were further examined through a 200 ns molecular dynamics simulation. The results showed relatively overall higher stability through the simulation time than the free target. All these findings were afterwards confirmed by the good in vitro inhibition activity of the compound against human prostate and cervical cancer cell lines with IC50 values of 9.55 and 9.19 µM, respectively in comparison to the standard drug, and additionally demonstrated innocuousness towards normal human kidney cancer cell lines. Moreover, the drug likeness properties indicated that the title compound has a high bioavailability, a good water solubility, a good gastrointestinal absorption, and can be easily synthesized.

Holistic Hydraulic Simulation for Pebble Bed Using Porous Media Approach

The porous media approach is regarded as an appropriate methodology for hydraulic simulations of complex pebble beds in fusion reactors. In order to determine the parameters (permeability α and inertial loss coefficient C) of the porous media accurately, two methods are proposed: the correction method and the fitting method. In this paper, a single-channel model with sequentially packed pebbles is constructed in order to obtain the pressure drop gradient against superficial velocities. Two methods, the correction method and fitting method, are employed to determine the permeability and inertial loss coefficient, and the results are evaluated with comparisons. Based on the results, both the correction method and fitting method are deemed feasible for the parameter determinations. In consideration of the consumption of resources and time for simulation, the fitting method is recommended during the preliminary design phase to shorten the duration of design, while the correction method is suggested to obtain precise results when the design is accomplished. Both of the methods would be evaluated with the data obtained from experiments in the future.

Experimental Validation of Radiance-Based Methods for Simulating Solar Penumbras and Pinhole Projections

ABSTRACT The sun’s vast size creates two distinct shadow phenomena on Earth: solar penumbras and pinhole projections. These sunlight effects are particularly important in daylight perception research and exacting daylighting design. As such, a method to generate accurate and efficient lighting simulations of these sunlight effects is needed to facilitate research and design applications. The present study is the first to investigate the accuracy of three Radiance-based methods for generating solar penumbras and pinhole projections against real-world measurements using various semi-controlled scenarios. In addition, we examine the applicability of these methods in terms of simulation time and file size. These methods involve the use of Radiance’s direct jittering algorithm, utilizing solely Radiance’s stochastic calculation, and subdividing a Radiance sun into many small suns. The simulation results of solar penumbras and pinhole projections per method are compared to real-world measurement data and assessed on their applicability. The results show that employing a subdivided sun offers an accurate and efficient method for simulating scenes with solar penumbras and pinhole projections using Radiance.

Assessment of Obesity as Risk Factor of Lumbar Disc Surgery: Retrospective Analysis of 598 Cases and Simulated Surgery on 3D-Printed Models.

(1) Background: Obesity poses known risks in surgery, including a prolonged operation time and postoperative complications. Given the rising obesity rates and frequent lumbar disc surgeries, understanding these risks is crucial. This study aims to assess the impact of obesity on operation duration and postoperative complications in lumbar disc prolapse surgery. (2) Methods: We retrospectively analyzed 598 patients with monosegmental disc herniation, correlating their body mass index (BMI) as a surrogate parameter for obesity with operation time. Excluding complex cases (multi-segmental herniations or recurrent herniations), complication rates and hospital stays were recorded. Simulated surgeries on 3D-printed models of varying obesity levels examined operation times and instrument suitability. (3) Results: Of these patients, 438 patients had a BMI of <30, and 160 patients had a BMI of ≥30. Complication rates showed no significant differences between groups. Linear regression analysis failed to establish a sole dependency of operation time on BMI, with R2 = 0.039 for the normal-weight group (BMI < 30) and R2 = 0.059 for the obese group (BMI ≥ 30). The simulation operations on the 3D-printed models of varying degrees of obesity showed a significant increase in the simulated operation time with higher levels of obesity. A geometrically inadequate set of surgical instruments was assumed to be a significant factor in the simulated increase in operating time. (4) Conclusions: While various factors influence operation time, obesity alone does not significantly increase it. However, simulated surgeries highlighted the impact of obesity, particularly on instrument limitations. Understanding these complexities is vital for optimizing surgical outcomes in obese patients.

Computing macroscopic reaction rates in reaction-diffusion systems using Monte Carlo simulations.

Stochastic reaction-diffusion models are employed to represent many complex physical, biological, societal, and ecological systems. The macroscopic reaction rates describing the large-scale, long-time kinetics in such systems are effective, scale-dependent renormalized parameters that need to be either measured experimentally or computed by means of a microscopic model. In a Monte Carlo simulation of stochastic reaction-diffusion systems, microscopic probabilities for specific events to happen serve as the input control parameters. To match the results of any computer simulation to observations or experiments carried out on the macroscale, a mapping is required between the microscopic probabilities that define the Monte Carlo algorithm and the macroscopic reaction rates that are experimentally measured. Finding the functional dependence of emergent macroscopic rates on the microscopic probabilities (subject to specific rules of interaction) is a very difficult problem, and there is currently no systematic, accurate analytical way to achieve this goal. Therefore, we introduce a straightforward numerical method of using lattice Monte Carlo simulations to evaluate the macroscopic reaction rates by directly obtaining the count statistics of how many events occur per simulation time step. Our technique is first tested on well-understood fundamental examples, namely, restricted birth processes, diffusion-limited two-particle coagulation, and two-species pair annihilation kinetics. Next we utilize the thus gained experience to investigate how the microscopic algorithmic probabilities become coarse-grained into effective macroscopic rates in more complex model systems such as the Lotka-Volterra model for predator-prey competition and coexistence, as well as the rock-paper-scissors or cyclic Lotka-Volterra model and its May-Leonard variant that capture population dynamics with cyclic dominance motifs. Thereby we achieve a more thorough and deeper understanding of coarse graining in spatially extended stochastic reaction-diffusion systems and the nontrivial relationships between the associated microscopic and macroscopic model parameters, with a focus on ecological systems. The proposed technique should generally provide a useful means to better fit Monte Carlo simulation results to experimental or observational data.

Accelerated Simulation of Passive Analog Circuits Over GPU Using Explicit Integration Methods

Analog circuits composed by large number of nodes in a tightly coupled structure pose significant challenges due to their prohibitive CPU simulation time. This work describes a method to speed up the simulation of such circuits by means of the combination of space state formulation of circuit equations with explicit integration methods parallelized over a many-core processor such as a GPU. Although stability of explicit techniques require smaller integration steps compared to implicit methods, the proposed method employs a fast estimate of the maximum allowed step size to guarantee numerical stability, which yields a shorter simulation time for increasing complexity circuit architectures. Moreover, the proposed technique can be straightforward parallelized on a many core architecture. The proposed method is demonstrated with two examples using constant and variable coefficients respectively: an RLC interconnect and a MOS-C network to perform Gaussian filtering of medium resolution images. The results obtained have been compared to a parallel version of SPICE and show improvements up to two orders of magnitude for transient simulations depending of the circuit size.

Benchmarking machine learning strategies for phase-field problems

We present a comprehensive benchmarking framework for evaluating machine-learning approaches applied to phase-field problems. This framework focuses on four key analysis areas crucial for assessing the performance of such approaches in a systematic and structured way. Firstly, interpolation tasks are examined to identify trends in prediction accuracy and accumulation of error over simulation time. Secondly, extrapolation tasks are also evaluated according to the same metrics. Thirdly, the relationship between model performance and data requirements is investigated to understand the impact on predictions and robustness of these approaches. Finally, systematic errors are analyzed to identify specific events or inadvertent rare events triggering high errors. Quantitative metrics evaluating the local and global description of the microstructure evolution, along with other scalar metrics representative of phase-field problems, are used across these four analysis areas. This benchmarking framework provides a path to evaluate the effectiveness and limitations of machine-learning strategies applied to phase-field problems, ultimately facilitating their practical application.

Benzothiazole‐Piperazine Hybrids Effectively Target C4‐2 Castration‐Resistant Prostate Cancer Cells in vitro Implicated through Computational Studies

AbstractA series of new analogs based on benzothiazole‐piperazine conjugates were synthesized and assessed to investigate their anticancer properties using in vitro and in silico techniques. The compounds 3 a–3 h were tested against prostate (C4‐2) cancer cells, and the results revealed that all the compounds showed significant inhibitory activity against C4‐2 cells (&gt;50 % inhibition at 20 μM). Among all, 2‐(4‐(pyrimidin‐2‐yl) piperazin‐1‐yl) benzo[d]thiazole demonstrated the most prominent activity in the cell viability assay conducted displaying an IC50 value of 19.98 μM. In vitro studies have demonstrated that the compound can selectively target the androgen receptor (AR) and reduce the expression of several androgen‐responsive genes, including EAF2, ELL2, PSA and CALR genes, in castration‐resistant prostate cancer cells. Molecular docking results also revealed that all the compounds exhibited good energy binding scores against the androgen receptor target (PB ID: 2PNU) particularly compound 3 g which showed a maximum binding energy of −9.87 kcal/mol followed by other compounds as well as the established prostate cancer drug, enzalutamide, with a binding energy of −5.66 kcal/mol. Docking results were further supported by MD simulation studies, which confirmed that the ligand+protein complex was in stable conformation throughout the simulation time of 100 nanoseconds. Collectively, the present results, stemming from a combination of in vitro and in silico examinations, demonstrate that compound 3 g displays notable anti‐cancer characteristics, implying its viability as a prospective anti‐cancer pharmaceutical subject to clinical validation and subsequent development.

Rényi entropy-complexity causality space: a novel neurocomputational tool for detecting scale-free features in EEG/iEEG data.

Scale-free brain activity, linked with learning, the integration of different time scales, and the formation of mental models, is correlated with a metastable cognitive basis. The spectral slope, a key aspect of scale-free dynamics, was proposed as a potential indicator to distinguish between different sleep stages. Studies suggest that brain networks maintain a consistent scale-free structure across wakefulness, anesthesia, and recovery. Although differences in anesthetic sensitivity between the sexes are recognized, these variations are not evident in clinical electroencephalographic recordings of the cortex. Recently, changes in the slope of the power law exponent of neural activity were found to correlate with changes in Rényi entropy, an extended concept of Shannon's information entropy. These findings establish quantifiers as a promising tool for the study of scale-free dynamics in the brain. Our study presents a novel visual representation called the Rényi entropy-complexity causality space, which encapsulates complexity, permutation entropy, and the Rényi parameter q. The main goal of this study is to define this space for classical dynamical systems within theoretical bounds. In addition, the study aims to investigate how well different time series mimicking scale-free activity can be discriminated. Finally, this tool is used to detect dynamic features in intracranial electroencephalography (iEEG) signals. To achieve these goals, the study implementse the Bandt and Pompe method for ordinal patterns. In this process, each signal is associated with a probability distribution, and the causal measures of Rényi entropy and complexity are computed based on the parameter q. This method is a valuable tool for analyzing simulated time series. It effectively distinguishes elements of correlated noise and provides a straightforward means of examining differences in behaviors, characteristics, and classifications. For the iEEG experimental data, the REM state showed a greater number of significant sex-based differences, while the supramarginal gyrus region showed the most variation across different modes and analyzes. Exploring scale-free brain activity with this framework could provide valuable insights into cognition and neurological disorders. The results may have implications for understanding differences in brain function between the sexes and their possible relevance to neurological disorders.

A reduced model for particle calcination for use in DEM/CFD simulations

We treat the accurate simulation of the calcination reaction in particles, where the particles are large and, thus, the inner-particle processes must be resolved. Because these processes need to be described with coupled partial differential equations (PDEs) that must be solved numerically, the computation times for a single particle are too high for use in simulations that involve many particles. Simulations of this type arise when the Discrete Element Method (DEM) is combined with Computational Fluid Dynamics (CFD) to investigate industrial systems such as quicklime production in lime shaft kilns.We show that, based on proper orthogonal decomposition and Galerkin projection, reduced models can be derived for single particles that provide the same spatial and temporal resolution as the original PDE models at a considerably reduced computational cost. Replacing the finite volume particle models with the reduced models results in an overall reduction of the reactor simulation time by about 40% for the sample system treated here.

Coupled modeling and simulation of tank self-pressurization and thermal stratification

Numerical analysis of thermo-fluid dynamics for cryogenic propellant storage primarily consists of nodal and computational fluid dynamics (CFD) modeling approaches. While nodal approaches prioritize faster computation over fidelity, CFD approaches promise accuracy and fidelity at the expense of significant computational resources. This paper presents an efficient coupling methodology developed to facilitate the simulation of a long-term self-pressurization process of a cryogenic propellant tank as well as associated thermal stratification phenomenon under both normal and reduced gravity conditions. Key highlight of the methodology is the development of a coupling scheme based on a domain decomposition approach that separates the tank into two computational regions at the liquid-vapor interface. SINDA/FLUINT, the nodal code, is utilized to simulate the liquid region, while ANSYS Fluent, the CFD code, handles the vapor region. A data exchange algorithm was developed to compute required boundary conditions for each region using the local thermodynamic properties of assumed infinitely thin interface. The coupling between the nodal and CFD codes was demonstrated by simulating a self-pressurization process in a small size tank using hydrogen as the working fluid. A sensitivity study on the grid sizing and coupling time step was conducted to determine appropriate spatial and ensure a divergent-free explicit data exchange time step size. Using the coupling approach, two numerical case studies were performed to study tank self-pressurization by considering two initial hydrogen fill levels. The effectiveness and efficiency of the coupling methodology was assessed by comparing the temperature and pressure evolution results of the coupled simulation with those obtained solely from the CFD simulation. Results showed that the coupling scheme and data exchange algorithm were successfully implemented, and the coupled simulation agreed well with the CFD simulations. In addition, a stratification number was used to characterize the transient dynamic development of the thermal stratification. Lastly, the computational costs associated with both approaches were compared. Significant reductions in simulation time were achieved for both cases.

Fractional neutron point kinetics model for reactivity transients of the NuScale and comparison with the classical kinetics approach

In this work, the behavior of the neutron and natural circulation parameters for Reactivity Transients in the PWR reactor called NuScale are analyzed through the novel system equations. It model includes non-integer and ordinary order differentials coupling the neutron point kinetic with the thermos-hydraulic core of the reactor. Transient processes of increase and decrease of the feed flow and the coolant inlet temperature are analyzed to identify the feedback of the thermohydraulic effects with the neutron phenomena in a non-integer order scheme. In the results, it is observed that for short times the power and reactivity have the same behavior with the classical model and the non-integer order model for all values of the anomalous diffusion coefficient simulated. However, for long times of simulation, there is more reactivity and less power, in contrast to the classical model, when reducing the value of the anomalous diffusion coefficient these counter-intuitive effects are increased. Additionally, a delay is observed between the change in reactivity and power. In conclusion, it is observed that sub-diffusive processes are relevant when there are slow changes in reactivity, which can be analyzed through the Fractional Order Neutron Density Equation.

Topology reduction through machine learning to accelerate dynamic simulation of district heating

District heating networks (DHNs) provide an efficient heat distribution solution in urban areas, accomplished through interconnected and insulated pipes linking local heat sources to local consumers. This efficiency is further enhanced by the capacity of these networks to integrate renewable heat sources and thermal storage systems. However, integration of these systems adds complexity to the physical dynamics of the network, necessitating complex dynamic simulation models. These dynamic physical simulations are computationally expensive, limiting their adoption, particularly in large-scale networks. To address this challenge, we propose a methodology utilizing Artificial Neural Networks (ANNs) to reduce the computational time associated with the DHNs dynamic simulations. Our approach consists in replacing predefined clusters of substations within the DHNs with trained surrogate ANNs models, effectively transforming these clusters into single nodes. This creates a hybrid simulation framework combining the predictions of the ANNs models with the accurate physical simulations of remaining substation nodes and pipes. We evaluate different architectures of Artificial Neural Network on diverse clusters from four synthetic DHNs with realistic heating demands. Results demonstrate that ANNs effectively learn cluster dynamics irrespective of topology or heating demand levels. Through our experiments, we achieved a 27% reduction in simulation time by replacing 39% of consumer nodes while maintaining acceptable accuracy in preserving the generated heat powers by sources.

Interpretable machine learning models for displacement demand prediction in reinforced concrete buildings under pulse-like earthquakes

This work proposes a novel procedure to guide the development of machine learning models for estimating the seismic demand in existing reinforced concrete (RC) buildings. The proposed approach is organized across two scales. A large-scale (nonparametric) machine learning model is first obtained by means of Gaussian Process Regression (GPR) using all candidate building attributes and intensity measures. SHapley Additive exPlanations (SHAP) values are utilized to facilitate its interpretation and to assist the rational selection of a small subset of intensity measures, which is finally employed to develop a (symbolic) reduced-scale machine learning model by means of Genetic Programming (GP). Simplified models of archetype buildings are adopted to develop machine learning techniques at both scales, in such a way to alleviate the simulation time for preparing large datasets. Refined models representative of actual buildings are instead considered for the unbiased final assessment.The proposed approach is applied to develop predictive machine learning models for the maximum inter-storey drift in bare frames, pilotis frames and frames with infills under pulse-like seismic ground motions. Consequently, the critical examination of the SHAP values revealed the most significant intensity measures and unfolded interesting patterns depending on the occupancy rate of the infills. Moreover, the final assessment demonstrates that this approach allows the management of a non-homogeneous building stock consisting of very diverse structural systems (i.e., spanning from existing buildings designed against gravity loads only to buildings that comply with outdated seismic codes) while providing satisfactory predictions of the seismic demand with minimum computational effort.

In silico Investigation on the Structural Insights into the Binding of Squalamine Inhibitor with Membrane-Bound Α-Synuclein

Background: Parkinson's disease (PD) and its associated symptoms are closely associated with the self-assembly of α-Synuclein (α-Syn). Squalamine is a naturally occurring chemical substance with established antiviral and anticancer properties, and its profound impact on the α- Syn aggregation both in vivo and in vitro is well studied. Examining its interaction with lipid vesicles, which are known to encourage nucleation, can signify the mechanism of action of squalamine. The squalamine molecule is believed to displace α-Syn from the surfaces of the lipid vesicles, therefore preventing the initial steps in the process of aggregation. Additionally, the squalamine molecule reduces the harmful effects of α-Syn oligomers in human neuroblastoma cells by preventing them from interacting with lipid membranes. Objective: The aim of this study was to perform computational investigation of the conformational changes of membrane-bound α-Syn in the presence of squalamine inhibitor molecule objective: Computational investigation of the conformational changes of membrane-bound α-Synuclein in the presence of squalamine inhibitor molecule Method: Molecular Dynamics (MD) trajectory analysis was carried out to study the structural change of the α-Syn-squalamine conformers as a function of simulation time. The percentage of the secondary structural components of the α-Syn-squalamine complex was determined. Optimization of small molecule inhibitors was carried out using Density Functional Theory (DFT) analysis. Additionally, the values of electrophilicity (ω), nucleophilicity (N), Electron affinity (EA), and ionization potential (IP) were calculated. Results: The docking of the α-Syn-squalamine complex revealed the binding site and the best structure was selected based on the highest docking vina score (-5.8), and the contact residues were listed. From the conformational snapshots of the α-Syn-squalamine complex, it was evident that the α-Syn remained stable, maintaining its integrity throughout the simulation. The α-helical content was found to be retained from the secondary structural content analysis. The ω and N of the squalamine molecule were calculated to be -0.84 and 3.25, respectively. Conclusion: Our findings suggest that in the presence of a squalamine inhibitor molecule, α-Syn adopts a helical conformation that ensures stability and may indicate that the squalamine molecule causes gradual displacement of α-Syn across different regions within the lipid membrane.

Numerical modelling of sawteeth and sawtooth-free regime

To better understand the sawteeth physics and the sawtooth-free regime associated with the hybrid scenario in tokamak experiments, numerical calculations up to quasi-steady state have been carried out for realistic middle-size tokamak plasma parameters, including the bootstrap current perturbation and basing on both the single- and two-fluid equations with the large aspect ratio approximation. Two types of the sawtooth crash are found in multiple sawteeth simulations: (1) For a low equilibrium bootstrap current fraction, the crash is caused by the internal kink mode, as expected; (2) When the bootstrap current density fraction is larger than 10% in the core region, however, the crash is caused by the non-ideal double kink mode, in contrary to the conventional understanding. In this case, a non-monotonic radial profile of the safety factor q with two q=1 surfaces emerges before the crash, caused by the bootstrap current density and plasma resistivity perturbations, although the original equilibrium has only single q = 1 surface. In both types of sawtooth crashes, the crash time in two-fluid simulations is tens of microseconds, as observed in experiments. Furthermore, for a relatively low ion density and finite bootstrap current density fraction, a transition from the sawtooth to the sawtooth-free regime is found, in which flat q profiles with the q value being about unity in the central region, similar to that observed in hybrid scenario experiments, are maintained by the dynamo effect. To enter into the sawtooth-free regime in two-fluid simulations, a much larger Alfvén velocity than that in single-fluid simulations is required due to the diamagnetic drift.