Mapping Scheme Research Articles

Bi-functional and tri-channel image displays based on a single-size nanostructured metasurface enabled by dual-orientation-degeneracy

Benefiting from the extraordinary ability of manipulating lightwaves at the subwavelength scale, nanostructured metasurfaces are expected to achieve multifunctional and multichannel integration to expand functionality and increase information capacity. However, multifunctional and multichannel metasurfaces always consist of various anisotropic nanostructures, inevitably bringing challenges to design and fabrication. In this study, we propose a concept of dual-orientation-degeneracy containing twofold orientation degeneracy. The first-level degeneracy is a one-to-four mapping scheme between the intensity of Channel 1 and orientation angle and the second-level degeneracy refers to a one-to-two mapping between the intensity of Channel 2 and orientation angle. Additionally, we provide a minimalist design of bi-functional and tri-channel image displays based on a single-size nanostructured metasurface. The designed metasurface integrates two functionalities of nanoprinting and holography, which can generate a continuous grayscale meta-image, a binary meta-image and a phase-only holographic image. Three channel displays can be readily switched by polarization controls. More importantly, the metasurface is achieved merely by reconfiguring the orientation angles of the nanostructures with fixed geometries, relieving the structure design and fabrication burden. The presented minimalist design strategy is universal and applicable, which can contribute to advanced research and applications in ultra-compact image displays, high-dense optical storage, multi-folded optical anti-counterfeiting, etc.

Multi-Mode Data Organization and File Retrieval Based on a Primer Library in Large-Scale Digital DNA Storage

At present, the polymerase chain reaction (PCR) amplification-based file retrieval method is the most commonly used and effective means of DNA file retrieval. The number of orthogonal primers limits the number of files that can be accurately accessed, which in turn affects the density in a single oligo pool of digital DNA storage. In this paper, a multi-mode DNA sequence design method based on PCR file retrieval in a single oligonucleotide pool is proposed for high-capacity DNA data storage. Firstly, by analyzing the maximum number of orthogonal primers at each predicted primer length, it was found that the relationship between primer length and the maximum available primer number does not increase linearly, and the maximum number of orthogonal primers is on the order of 104. Next, this paper analyzes the maximum address space capacity of DNA sequences with different types of primer binding sites for file mapping. In the case where the capacity of the primer library is R (where R is even), the number of address spaces that can be mapped by the single-primer DNA sequence design scheme proposed in this paper is four times that of the previous one, and the two-level primer DNA sequence design scheme can reach [R2∙(R2-1)]2 times. Finally, a multi-mode DNA sequence generation method is designed based on the number of files to be stored in the oligonucleotide pool, in order to meet the requirements of the random retrieval of target files in an oligonucleotide pool with large-scale file numbers. The performance of the primers generated by the orthogonal primer library generator proposed in this paper is verified, and the average Gibbs free energy of the most stable heterodimer formed between the orthogonal primers produced is –1 kcal∙(mol∙L−1)−1 (1 kcal = 4.184 kJ). At the same time, by selectively PCR-amplifying the DNA sequences of the two-level primer binding sites for random access, the target sequence can be accurately read with a minimum of 103 reads, when the primer binding site sequences at different positions are mutually different. This paper provides a pipeline for orthogonal primer library generation and multi-mode mapping schemes between files and primers, which can help achieve precise random access to files in large-scale DNA oligo pools.

Selective Medical Image Encryption and Compression Based on DCT, 3D_Att_ResU-Net and 4D Hyperchaotic Map Schemes

Selective Medical Image Encryption and Compression Based on DCT, 3D_Att_ResU-Net and 4D Hyperchaotic Map Schemes

Map Construction and Positioning Method for LiDAR SLAM-Based Navigation of an Agricultural Field Inspection Robot

In agricultural field inspection robots, constructing accurate environmental maps and achieving precise localization are essential for effective Light Detection And Ranging (LiDAR) Simultaneous Localization And Mapping (SLAM) navigation. However, navigating in occluded environments, such as mapping distortion and substantial cumulative errors, presents challenges. Although current filter-based algorithms and graph optimization-based algorithms are exceptionally outstanding, they exhibit a high degree of complexity. This paper aims to investigate precise mapping and localization methods for robots, facilitating accurate LiDAR SLAM navigation in agricultural environments characterized by occlusions. Initially, a LiDAR SLAM point cloud mapping scheme is proposed based on the LiDAR Odometry And Mapping (LOAM) framework, tailored to the operational requirements of the robot. Then, the GNU Image Manipulation Program (GIMP) is employed for map optimization. This approach simplifies the map optimization process for autonomous navigation systems and aids in converting the Costmap. Finally, the Adaptive Monte Carlo Localization (AMCL) method is implemented for the robot’s positioning, using sensor data from the robot. Experimental results highlight that during outdoor navigation tests, when the robot operates at a speed of 1.6 m/s, the average error between the mapped values and actual measurements is 0.205 m. The results demonstrate that our method effectively prevents navigation mapping distortion and facilitates reliable robot positioning in experimental settings.

Land Cover Mapping in East China for Enhancing High-Resolution Weather Simulation Models

This study was designed to develop a 30 m resolution land cover dataset to improve the performance of regional weather forecasting models in East China. A 10-class land cover mapping scheme was established, reflecting East China’s diverse landscape characteristics and incorporating a new category for plastic greenhouses. Plastic greenhouses are key to understanding surface heterogeneity in agricultural regions, as they can significantly impact local climate conditions, such as heat flux and evapotranspiration, yet they are often not represented in conventional land cover classifications. This is mainly due to the lack of high-resolution datasets capable of detecting these small yet impactful features. For the six-province study area, we selected and processed Landsat 8 imagery from 2015–2018, filtering for cloud cover. Complementary datasets, such as digital elevation models (DEM) and nighttime lighting data, were integrated to enrich the inputs for the Random Forest classification. A comprehensive training dataset was compiled to support Random Forest training and classification accuracy. We developed an automated workflow to manage the data processing, including satellite image selection, preprocessing, classification, and image mosaicking, thereby ensuring the system’s practicality and facilitating future updates. We included three Weather Research and Forecasting (WRF) model experiments in this study to highlight the impact of our land cover maps on daytime and nighttime temperature predictions. The resulting regional land cover dataset achieved an overall accuracy of 83.2% and a Kappa coefficient of 0.81. These accuracy statistics are higher than existing national and global datasets. The model results suggest that the newly developed land cover, combined with a mosaic option in the Unified Noah scheme in WRF, provided the best overall performance for both daytime and nighttime temperature predictions. In addition to supporting the WRF model, our land cover map products, with a planned 3–5-year update schedule, could serve as a valuable data source for ecological assessments in the East China region, informing environmental policy and promoting sustainability.

Fisher Information-Based Optimization of Mapped Fourier Grid Methods

The mapped Fourier grid method (mapped-FGM) is a simple and efficient discrete variable representation (DVR) numerical technique for solving atomic radial Schrödinger differential equations. It is set up on equidistant grid points, and the mapping, a suitable coordinate transformation to the radial variable, deals with the potential energy peculiarities that are incompatible with constant step grids. For a given constrained number of grid points, classical phase space and semiclassical arguments help in selecting the mapping function and the maximum radial extension, while the energy does not generally exhibit a variational extremization trend. In this work, optimal computational parameters and mapping quality are alternatively assessed using the extremization of (coordinate and momentum) Fisher information. A benchmark system (hydrogen atom) is employed, where energy eigenvalues and Fisher information are traced in a standard convergence procedure. High-precision energy eigenvalues exhibit a correlation with the extrema of Fisher information measures. Highly efficient mapping schemes (sometimes classically counterintuitive) also stand out with these measures. Same trends are evidenced in the solution of Dalgarno–Lewis equations, i.e., inhomogeneous counterparts of the radial Schrödinger equation occurring in perturbation theory. A detailed analysis of the results, implications on more complex single valence electron Hamiltonians, and future extensions are also included.

Large eddy simulation of highly underexpanded sonic jets from elliptical nozzles

The injection of high-pressure jets into quiescent air poses significant challenges in fluid dynamics, pertinent to various industrial engineering applications. This study used large eddy simulations on a massively parallel computational framework, employing a grid of over 600 million nodes, to investigate the behavior of highly underexpanded sonic jets from elliptical nozzles at a nozzle pressure ratio of 15. Three elliptical nozzles, with aspect ratios of 1.5, 2.2, and 3.0, each having a sectional area equivalent to that of a circular jet with a diameter of D=1 mm, were analyzed. The aim was to clarify the gasdynamic and mixing characteristics of these jets to guide the design of next-generation injectors. A detailed analysis of the flow provided insights into the mechanisms of turbulence generation and Reynolds stress anisotropy. This was achieved using the componentality contour approach and a modified barycentric color mapping scheme, offering valuable reference data for developing lower-order models. The results indicate a non-axisymmetric radial expansion of the jet boundary in all elliptical injectors, leading to an axis switch phenomenon. The use of elliptical orifices was found to reduce jet penetration, mitigating issues such as fuel impingement in small engine combustion chambers and promoting improved air–fuel mixing quality.

Everyday Dishes from Corn Flour and Groats in the Nutrition of Ukrainians of East Podolian Prydnistrovia in the Late 19th – Early 21st Centuries

The article is devoted to the consideration of an integral component of the traditional daily ration of Ukrainians of East PodolianPrydnistrovia – dishes from corn flour and grits, known as a mandatory food for every day during the late 19th – mid 20th centuries, and some of them (for food variety) are still cooked. The work is based on the analysis of ethnographic works, published sources and the corpus of own field materials (interviews with respondents), recorded during the expeditions to the border settlements of Vinnytsia and the north of Odesa regions in 2013–2016. The author is describing the features of cooking the most common corn products – mamalyha and malai. The gastronomic difficulties of life support under extreme circumstances are considered partially. In particular, attention is focused on the consumption in the region of baderevka and badevka, those are rather modest in terms of nutrition boiled liquid soups with corn flour, and which saved Ukrainians during the difficult times of the post-war famine of 1946–1947. The materials of the conducted research have made it possible to confirm the generalizations of ethnologists-researchers of the Ukrainian-Moldovan borderlands and our preliminary conclusions (based on the study of other components of the life-support system) that in the space of the ethno-contact zone (in particular, in the Transdnistrian territories of Eastern Podillia) a typical ethno-specific food complex partially loses its differentiating function and acquires a special format that combines similar or jointly created ethnocultural practices of both Ukrainians and Moldovans. The analysis of the author’s expedition materials, in addition to the factual disclosure of the subject, also serves as a basis for the spatial characterization of the names and realities of this block of ethnoculture, visualized with the help of a special map scheme introduced into scientific circulation. The concluding remarks outline the prospects for implementing the results for modern cultural preservation practices, primarily in the Vinnytsia and Odesa regions.

Efficient simulation of open quantum systems coupled to a reservoir through multiple channels.

It is challenging to simulate open quantum systems that are connected to a reservoir through multiple channels. For example, vibrations may induce fluctuations in both energy gaps and electronic couplings, which represent two independent channels of system-bath couplings. Systems of this kind are ubiquitous in the processes of excited state radiationless decay. Combined with density matrix renormalization group (DMRG) and matrix product states (MPS) methods, we develop an interaction-picture chain mapping strategy for vibrational reservoirs to simulate the dynamics of these open systems, resulting in time-dependent spatially local system-bath couplings in the chain-mapped Hamiltonian. This transformation causes the entanglement generated by the system-bath interactions to be restricted within a narrow frequency window of vibrational modes, enabling efficient DMRG/MPS dynamical simulations. We demonstrate the utility of this approach by simulating singlet fission dynamics using a generalized spin-boson Hamiltonian with both diagonal and off-diagonal system-bath couplings. This approach generalizes an earlier interaction-picture chain mapping scheme, allowing for efficient and exact simulation of systems with multi-channel system-bath couplings using matrix product states, which may further our understanding of nonlocal exciton-phonon couplings in exciton transport and the non-Condon effect in energy and electron transfer.

A novel learning-based robust model predictive control strategy and case study for application in optimal control of FCEVs

With the gradual implementation of carbon-neutral policies, fuel cell electric vehicles (FCEVs) have gained widespread recognition as a promising solution. Due to multi-source electromechanical coupling, the powertrain of FCEVs constitutes a complex nonlinear system, necessitating an advanced energy management strategy for effective control to enhance economic performance. In this study, a novel learning-based robust model predictive control (LRMPC) is proposed for a 4WD FCEV, integrating a high-confidence velocity estimation model and an accurate state observation model to enhance energy-saving potential, robustness, and real-time application performance under various driving conditions. To enhance robustness and adaptability, deep forest with superior feature fusion abilities is employed to generate future velocity reference datasets based on FCEV operating scenarios. To improve state observation accuracy and control effectiveness, a novel built-in state observation model based on machine learning algorithms is established, leveraging knowledge of nonlinear systems within the FCEV powertrain. Subsequently, to enhance the real-time applicability of LRMPC, an explicit control scheme for multidimensional mapping based on explicit data tables is utilized for accurate state transformation, incorporating input features such as vehicle states and component states. Simulation evaluations and hardware-in-the-loop tests demonstrate the improved economic performance and real-time application ability of LRMPC, showcasing its promising performance.

Application of Rotor UAV LiDAR in Marine Beach Mapping

Abstract This paper based on the Jiangsu Provincial Offshore Underwater Geographic Information Data Acquisition Pilot (North) project, uses the Dajiang M600 Pro six rotor UAV equipped with the Haida Digital Cloud ARS-200 airborne LiDAR equipment to acquire topographic data of the marine mudflats, and then Terrasolid software is used to process the data to obtain the point cloud results and DEM results of the marine mudflats, and finally, GPS-RTK is used to collect the check points of the surveyed area to check the accuracy of the results. The results show that the precision of point cloud results is better than ± 10cm, which verifies the feasibility of rotary-wing UAV LiDAR technology applied to marine beach mapping, and provides a new technical scheme for marine beach mapping.

AutoGMap: Learning to Map Large-Scale Sparse Graphs on Memristive Crossbars.

The sparse representation of graphs has shown great potential for accelerating the computation of graph applications (e.g., social networks and knowledge graphs) on traditional computing architectures (CPU, GPU, or TPU). But, the exploration of large-scale sparse graph computing on processing-in-memory (PIM) platforms (typically with memristive crossbars) is still in its infancy. To implement the computation or storage of large-scale or batch graphs on memristive crossbars, a natural assumption is that a large-scale crossbar is demanded, but with low utilization. Some recent works question this assumption; to avoid the waste of storage and computational resource, the fixed-size or progressively scheduled "block partition" schemes are proposed. However, these methods are coarse-grained or static and are not effectively sparsity-aware. This work proposes the dynamic sparsity-aware mapping scheme generating method that models the problem with a sequential decision-making model, and optimizes it by reinforcement learning (RL) algorithm (REINFORCE). Our generating model [long short-term memory (LSTM), combined with the dynamic-fill scheme] generates remarkable mapping performance on the small-scale graph/matrix data (complete mapping costs 43% area of the original matrix) and two large-scale matrix data (costing 22.5% area on qh882 and 17.1% area on qh1484). Our method may be extended to sparse graph computing on other PIM architectures, not limited to the memristive device-based platforms.

Application of weather post-processing methods for operational ensemble hydrological forecasting on multiple catchments in Canada

Hydrological forecasts contain biases that need to be addressed for their effective use in operational decision-making in water resources management. Performing post-processing allows reducing the overall systematic bias while improving the distribution and accuracy of hydrological forecasts. In this study, a Quantile Mapping (QM) post-processing method was applied on weather forecasts following three temporal configurations (monthly, seasonal, and annual) of the quantile mapping scheme. The evaluation encompasses 20 catchments in southern Canada, employing a leave-one-out approach with the QM method on ECMWF ensemble weather forecasts spanning 2015–2020 inclusively. These processed forecasts are subsequently utilized as forcings for eight hydrological models, generating ensemble streamflow forecasts over a 6-year period with a lead time of 10 days and a sub-daily timestep of 6 h. The performance of the QM method is mainly assessed using the Continuous Ranked Probability Score (CRPS) metric, in complement with a forecast reliability score (ABDU) and a forecast sharpness metric (NMIQR). Significant improvements are discerned in precipitation forecasts upon the application of QM. Notably, these improvements are translated into enhanced hydrological forecasts for over half of the catchments studied (55 %). Surprisingly, no discernible differences in performance are observed among the three QM configurations in most catchments. Interestingly, there are watersheds where the implementation of QM exhibit either poorer or no change in performance and sharpness compared to raw forecasts.

Dissipative Particle Dynamics Simulation of Protein Folding in Explicit and Implicit Solvents: Coarse-Grained Model for Atomic Resolution.

Advancements have been made to dissipative particle dynamics (DPD), a robust coarse-grained (CG) simulation method, to study the folded structures of four miniproteins (1L2Y, 1WN8, 1YRF, and 2I9M) in explicit and implicit solvents. In this endeavor, we aim to establish model parametrization and enhance computational efficiency. Unlike traditional CG models that use empirical force parameters, ex-force parameters (r0(ex), , δd, δp) of DPD particles constructed for specific research purposes can be obtained from atomistic molecular dynamics simulations. On the other hand, im-force parameters (r0(im), c, σ) can be derived from ex-DPD simulations, according to the underlying thermodynamic theory. Based on a mapping scheme proposed for the modeling of amino acids, all-atom proteins can be converted into a CG model. Both ex-/im-DPDs are then carried out to investigate the folding pathways of the four mini-proteins. Structural analysis of the RMSDs shows that the im-simulated proteins have greater structural similarity to native proteins than the ex-simulated ones. The constructed CG models achieve a resolution of Angstrom (Å), a level normally associated with atomic models. Additionally, speed tests reveal that im-DPD accelerates the simulation process and significantly improves simulation efficiency.

Research on agricultural monitoring and progressive map creation in the context of ground-air coordination

Abstract By combining network RTK technology, 2D LiDAR, and UAV, this study investigates how to achieve agricultural monitoring effectively. A collaborative agricultural monitoring scheme was designed to accurately and comprehensively gather agricultural information in response to the demand for targeted sequential monitoring at specified agricultural locations. To establish a low-cost and low computational power-dependent 3D map of obstacle point clouds, a progressive 3D mapping scheme based on 2D LiDAR was designed and implemented. The effectiveness of the design was validated through simulation experiments and partly through real-world testing.

Enhancing Iris Authentication for Managing Multiple Cancellations: Leveraging Quotient Filters

Biometric authentication methods have become increasingly popular for their ability to offer secure and convenient access control. However, concerns about the privacy and security of biometric data have arisen. In this study, we present a novel approach to address these concerns by proposing a cancellable biometric encryption technique for secure and format-preserving iris authentication. Our method leverages the Quotient Filter data structure to transform encrypted iris templates into cancellable templates while preserving their original format. We carefully select an appropriate format-preserving encryption algorithm for iris templates and design a mapping scheme to achieve cancellability. To assess the effectiveness and performance of our approach, extensive experiments are conducted. The quantitative results indicate the efficiency and efficacy of our cancellable biometric encryption technique using the Quotient Filter. Our innovation, the Iris Authentication for Multiple Cancelled Instances Using a Quotient Filter (IAMCIQF), demonstrates competitive performance across several key metrics. IAMCIQF achieves a high level of security strength and strikes a balance between security and efficiency in terms of key size, encryption and decryption speeds and storage efficiency when compared to other existing techniques. The quantitative outcomes underscore IAMCIQF’s potential as a promising solution for attaining secure and format-preserving iris authentication, addressing critical concerns about biometric data security.

Training Neural Mapping Schemes for Satellite Altimetry With Simulation Data

AbstractSatellite altimetry combined with data assimilation and optimal interpolation schemes have deeply renewed our ability to monitor sea surface dynamics. Recently, deep learning schemes have emerged as appealing solutions to address space‐time interpolation problems. However, the training of state‐of‐the‐art neural schemes on real‐world case‐studies is hindered by the sparse space‐time coverage of the sea surface of real altimetry data set. Here, we introduce an innovative approach that leverages state‐of‐the‐art ocean models to train simulation‐based neural schemes for the mapping of sea surface height and demonstrate their performance on real altimetry data sets. We analyze further how the ocean simulation data set used during the training phase impacts this performance. This experimental analysis covers both the resolution from eddy‐present configurations to eddy‐rich ones, forced simulations versus reanalyzes using data assimilation and tide‐free versus tide‐resolving simulations. Our benchmarking framework focuses on a Gulf Stream region for a realistic 5‐altimeter constellation using NEMO ocean simulations and 4DVarNet mapping schemes. All simulation‐based 4DVarNets outperform the operational observation‐driven and reanalysis products, namely DUACS and GLORYS. The more realistic the ocean simulation data set used during the training phase, the better the mapping. The best 4DVarNet mapping was trained from an eddy‐rich and tide‐free simulation data sets. It improves the resolved longitudinal scale from 151 km for DUACS and 241 km for GLORYS to 98 km and reduces the root mean square error by 23% and 61%. These results open research avenues for new synergies between ocean modeling and ocean observation using learning‐based approaches.

General Simultaneous Localization and Mapping Scheme for mmWave Communication Systems

General Simultaneous Localization and Mapping Scheme for mmWave Communication Systems

REGIONAL MANIFESTATIONS OF THE DETERIORATION OF THE NATURAL CONDITIONS OF HEAT AND MOISTURE SUPPLY OF THE TERRITORY IN THE CONDITIONS OF MAINTAINING THE SUSTAINABLE FUNCTIONING OF ECONOMY SYSTEMS IN THE TERRITORY OF TERNOPIL REGION

The publication examines the influence of regional manifestations of the deterioration of natural conditions (moisture supply and unfavorable thermal parameters) on agricultural management. Global climate changes are superimposed by regional manifestations of adverse processes, such as the emission of greenhouse gases generated by an unbalanced land structure, in which we observe an excessive share of arable land. Excessive plowing of the territory leads to increased production of greenhouse gases by arable land, which complicates the manifestation of the greenhouse effect at the local level. Data on the structure of land plots of Ternopil region and Borsukiv territorial community are given. Attention is drawn to the map scheme of changes in the moisture supply of the territory of Ukraine, according to which a very wet climatic annual balance will be preserved only in the mountainous part of the Carpathians, Ternopil region will go from wet to insufficiently wet in its southeastern part, and over time, according to the forecast of 2100, to an insufficiently wet balance in the greater its territory At the same time, about 55% of the southeastern regions are threatened by dry and very dry climatic annual balance. According to the authors, the search for ways to solve the problem can be found in the optimization of land use. The algorithm of the optimization model is prescribed in the methodology of Professor Mykhailo Grodzynskii and consists in determining and ranking the development priorities of the research area and achieving a scientifically justified ratio between natural and anthropogenic land areas. Based on the materials of the Borsukiv territorial community, an optimization model of land use was created, according to which it is proposed to reduce the share of arable land from 65% to 45% due to the removal of heavily eroded and poorly productive lands from the arable wedge and their transfer to liming and afforestation, the share of which in the land use structure of the territorial community increased respectively 21% and 23%. Such optimization measures will ensure the transition from the emission of greenhouse gases by land to their assimilation by forest and meadow vegetation. Increasing the share of forests by 14% will contribute to a 5% increase in precipitation of local origin, which is especially important for stabilizing the water balance of the territory. An increase in the share of natural vegetation will contribute to the maintenance of ecological balance, restoration of biotic and landscape diversity, improved water balance, and natural living conditions of the population.Increasing or stabilizing the degree of humidification of territories under the conditions of global and regional climate changes is becoming an important scientific and applied problem, the solution of which requires the development and implementation of strategic tasks of sustainable development at the levels of regional, administrative and district territorial entities, the level of territorial communities, Starosty districts. Taking into account the priority criteria for the development of territorial entities is the first stage of optimizing the use of nature in these administrative-territorial entities. The second stage of the landscape and ecological optimization of territories is the substantiation of the ratio of natural and anthropogenic lands. Land optimization will contribute to tireless use of nature and the balancing of basic natural processes with anthropogenic ones. In the future, we envisage the creation of optimization models of land use for all territorial communities of the region, which will optimize the development of natural and economic systems. Keywords: regional manifestations of climatic conditions, land use structure, optimization, territorial community.

Comparison of coarse-grained models for cis-1,4-polyisoprene-graphite system

ABSTRACT Structural and dynamical properties of cis-1,4-polyisoprene-graphite system were compared by four coarse-grained models. Coarse-graining of graphene layers replaced four carbon atoms by a single bead while four mapping schemes were used for the polymer. Pressure-correction was applied to the nonbonded part of the coarse-grained potential among coarse-grained polyisoprene beads. Among structural properties density profiles, bond orientation and conformation tensor as function of distance from the graphite surface were studied. Bulk values were achieved at around 2 nm from the graphite surface. Higher degree of coarse-graining showed comparatively larger fluctuations in data. Among dynamical properties mean square displacement and autocorrelation of end-to-end unit vectors for varied distances from the graphite surface were studied. Dynamics study indicated that pressure-correction method used to optimise pressure of the system resulted in slower dynamics for the higher degree of coarse-graining.