Universal Modeling Approach Research Articles

Optimization-Based Pairwise Interaction Point Process (O-PIPP): A Precise and Universal Retinal Mosaic Modeling Approach.

A retinal mosaic, the spatial organization of a population of homotypic neurons, is thought to sample a specific visual feature into the feedforward visual pathway. The purpose of this study was to propose a universal modeling approach for precisely generating retinal mosaics and overcoming the limitations of previous models, especially in modeling abnormal mosaic patterns under disease conditions. Here, we developed the optimization-based pairwise interaction point process (O-PIPP). It incorporates optimization techniques into previous simulation approaches, enabling directional control of the simulation process according to the user-designed optimization target. For the convenience of the community, we implemented the O-PIPP approach into a Python package and a website application. We showed that the O-PIPP can generate more precise neural spatial patterns of healthy and diseased mosaics compared to previous phenomenological approaches. Notably, through modeling the retinal neural circuitry with O-PIPP-simulated retinitis pigmentosa cone mosaics, we elucidated how the cone mosaic rearrangement impacted the information processing of ganglion cells. The O-PIPP provides a precise and universal tool to simulate realistic mosaics, which could help to investigate the function of retinal mosaics in vision.

Turbulent Channel Flow Simulation Using LES Dynamic Model

Turbulent flow modelling has increasingly becomeone of the main scientific research occupations, seeking todevelop a universal modelling approach that can describeaccurately the dynamics of all turbulent flow scales. Modernattempts try to bring together two main objectives; theapplicability of the turbulent flow models to complex flowconfigurations of industrial interest as well as reducing thecomputational costs needed for their resolution. Large EddySimulation (LES) modelling approach has many promisingfeatures among all other turbulent flow modellingapproaches such as RANS or DNS. In this study, LESmodelling approach has been demonstrated and applied toturbulent channel flow at moderate Reynolds number using Fluent solver. The obtained turbulent flowstatistics were compared to their corresponding values of a detailed, well resolved DNS simulation. The obtained LESturbulent flow statistics have an acceptable trend to those ofthe reference DNS results, which reflects the validity of theLES approach to model turbulent flows accurately andencourages its use in predicting turbulent motion forindustrial and engineering applications.

THz Generation via Optical Rectification in Nanomaterials: Universal Modeling Approach and Effective χ¯¯(2)$\bar{\bar{\chi }}^{(2)}$ Description

AbstractOptical rectification (OR) at the nanoscale has attracted an increasing interest in the prospect of providing efficient ultracompact terahertz (THz) sources. Here, a universal modeling approach capable of addressing both isotropic and anisotropic all‐dielectric nonlinear nanomaterials on an ultra‐broad spectral range, covering the highly dispersive phonon‐polariton window, and different orientations of the crystallographic axes with respect to the geometry of the structure is reported. This analysis is exemplified by considering two study cases, that is, nanopillars of AlGaAs and of LiNbO3. A close comparison between the two cases is established in terms of THz generation efficiency from 4 to 14 THz. Phonon‐polariton contributions to the OR process are disentangled from the electronic one, and a model order reduction based on the reciprocity theorem is applied and validated on both the considered configurations. These results, combined with the inspection of the THz near‐field features, pave the way to the design and optimization of nonlinear metasurfaces for THz generation and detection at the nanoscale.

Optimization of alkaline electrolyzer operation in renewable energy power systems: A universal modeling approach for enhanced hydrogen production efficiency and cost-effectiveness

This study presents a comprehensive analysis of an Alkaline Water Electrolysis System (AWES) for hydrogen production, focusing on thermal balance, operating conditions, efficiency, and lifecycle cost. A thermal balance model was developed and further refined using machine learning techniques based on experimental data, to accurately calculate electrolysis power, cooling requirements, and temperature maintenance, enabling cost-effective operation. This study provides insights for optimizing AWES performance and economic feasibility in hydrogen production. Selecting appropriate operating conditions considering current density, inlet temperature, and electricity price is crucial. Implementing these optimizations improves energy efficiency, reduces lifecycle hydrogen production costs, and enables integration with renewable energy systems.

A universal modelling approach and a response index to the vibration analysis for space parallel robots by means of screw theory

Vibration analysis is very important for evaluating the vibration performance of some space parallel robots (SPRs), but most of the matrix analysis methods are case-by-case treatment and the vibration response is difficult to estimate accurately in the design phase. In order to enrich this field, a universal vibration modelling approach and a vibration response index are proposed in this paper. Utilizing the strong universality of the screw theory, a universal dynamic modelling method is proposed, in which the complicated Lie algebra terms are appear explicitly and the Coriolis acceleration term is intentionally independent of each other. Based on the Rayleigh quotient and considering the effects of gravity and joint clearances, a vibration response index is presented to reflect the vibration amplitude of SPRs under the unit initial conditions. Based on the built experimental prototype and some applications, numerical examples are presented and some performances of the vibration index have been fully discussed, and the results show that the mean error between the mathematical models and experimental results is about 5.4%. When researchers are engaged in vibration research on different SPRs, we expect that the proposed mathematical model and the proposed index will provide a convenient way for vibration modelling and optimization.

Assessing the niche of Rhododendron arboreum using entropy and machine learning algorithms: role of atmospheric, ecological, and hydrological variables

Species distribution models (SDMs) have been used extensively in the field of landscape ecology and conservation biology since its origin in the late 1980s. But there is still a void for a universal modeling approach for SDMs. With recent advancements in satellite data and machine learning algorithms, the prediction of species occurrence is more accurate and realistic. Presently, four machine learning and regression-based algorithms, namely, generalized linear model, maximum entropy, boosted regression tree, and random forest (RF) are used to model the geographical distribution of Rhododendron arboreum, which is economically and medicinally important species found in the fragile ecosystem of Himalayas. To establish complex relation between the occurrence data and regional climatic and land use parameters, several satellite products, namely, MODIS, Sentinel-5p, GPM, ECOSTRESS, and shuttle radar topography mission (SRTM), are acquired and used as predictor variables to the different SDM algorithms. The performance evaluation has been conducted using the area under curve (AUC), which showed the best result for Maxent (AUC = 0.871) and poor result was observed for RF (AUC = 0.755) among all. The overall prediction confirmed the distribution of Rhododendron arboreum in the mid to high altitudes of central and southern parts of the Garhwal Division. We provide crucial evidence that combining multisatellite products using machine learning algorithms can provide a much better understanding of species distribution that can eventually help the researchers and policymakers to take the necessary step toward its conservation.

A versatile and modular modeling framework for diverse storage unit simulations

Electrochemical batteries are becoming more essential for our increasingly renewable powered society every day. Therefore, the capability to simulate the behavior of these systems gains importance as well. Each storage unit from the broad technology spectrum comes with its own inherent characteristics and peculiarities proving it difficult to simulate various systems with one universal modeling approach. That however, is the most economical solution considering the effort-benefit ratio. In this work we therefore present and discuss a modular, highly versatile modeling methodology composed of several adapted submodels. The concept is able to simulate electrochemical storage units of diverging technologies as well as system size. Furthermore, it accounts for nonlinearities occurring due to current-voltage characteristics, current-rate as well as temperature dependencies, self-discharge, peripheral influence and aging phenomena. We outline benefits but also challenges of the approach and additionally showcase its sufficient fidelity, straightforward parametrization and versatility. Comprehensive simulation results are presented for a lithium-ion single cell as well as for four distinct storage systems of application relevant capacities, namely a lithium-ion rack, a valve-regulated lead-acid unit, an all-vanadium redox-flow battery and a high-temperature sodium-nickel-chloride system.

Modelling and analysis framework for nonlinear dynamics of submerged tensioned cables

We present a universal modelling approach and a unified analytical framework for nonlinear dynamics of the submerged tensioned anchor cable (STAC) considering the coupling effect of the parametric excitation and vortex-induced vibration (VIV). Through a self-consistent verification method, we demonstrate that only using the first few modal response for dynamic analysis will lead to great errors. In addition, our research shows that in order to ensure the accuracy of the results, at least the first nine-order modal information is required. Another major contribution of this article is to propose an improved Galerkin's discretization method and a standard analytical framework for stability analysis of the STAC. The entire analysis process is analytical, thus ensuring the accuracy and efficiency of the calculation, which can be used to investigate the internal mechanism of the steady-state solution transition from unstable to stable.

A Comparative Study on the Modelling of Soybean Particles Based on the Discrete Element Method

To solve the poor universality in the existing modelling approaches of soybean particles, we proposed a soybean particle modelling approach by combining five, nine, and 13 balls. The soybean seeds from three varieties (Suinong42, Jidou17, and Zhongdou39 with a sphericity of 94.78%, 86.86%, and 80.6%, respectively) are chosen as the study objects. By the comparisons between the simulation results and the test results in the “self-flow screening” and “piling angle” tests, it is concluded that the soybean particle modelling approach we presented in this paper is a universal modelling approach appropriate for soybean particles with different sphericities. The five-ball model is appropriate for the soybean particles with high sphericity, and the nine- or 13-ball models are applicable to those with low sphericity. The soybean particle modelling approach we presented is also compared with the ellipsoidal equation modelling approach for soybean particles and with the modelling approaches presented by other researchers. From an overall perspective, the soybean particle modelling approach we presented is better than the ellipsoidal equation modelling approach and those modelling approaches presented by other researchers. Additionally, it is shown that the multiple contacts issue in the multi-ball model has a little influence on the simulation results of soybean particle models. The study in this paper provides a new modelling approach for soybean particles in the DEM simulation of the contacts between soybean particles and the related machines.

Bridging the Architecture Gap: Abstracting Performance-Relevant Properties of Modern Server Processors

We describe a universal modeling approach for predicting single- and multicore runtime of steady-state loops on server processors. To this end we strictly differentiate between application and machine models: An application model comprises the loop code, problem sizes, and other runtime parameters, while a machine model is an abstraction of all performance-relevant properties of a CPU. We introduce a generic method for determining machine models and present results for relevant server-processor architectures by Intel, AMD, IBM, and Marvell/Cavium. Considering this wide range of architectures, the set of features required for adequate performance modeling is surprisingly small. To validate our approach, we compare performance predictions to empirical data for an OpenMP-parallel preconditioned CG algorithm, which includes compute- and memory-bound kernels. Both single- and multicore analysis shows that the model exhibits average and maximum relative errors of 5% and 10%. Deviations from the model and insights gained are discussed in detail.

Structure of surfactant and phospholipid monolayers at the air/water interface modeled from neutron reflectivity data

Specular neutron reflectometry is a powerful technique to resolve interfacial compositions and structures in soft matter. Surprisingly however, even after several decades, a universal modeling approach for the treatment of data of surfactant and phospholipid monolayers at the air/water interface has not yet been established. To address this shortcoming, first a systematic evaluation of the suitability of different models is presented. The result is a comprehensive validation of an optimum model, which is evidently much needed in the field, and which we recommend as a starting point for future data treatment. While its limitations are openly discussed, consequences of failing to take into account various key aspects are critically examined and the systematic errors quantified. On the basis of this physical framework, we go on to show for the first time that neutron reflectometry can be used to quantify directly in situ at the air/water interface the extent of acyl chain compaction of phospholipid monolayers with respect to their phase. The achieved precision of this novel quantification is ∼10%. These advances together enhance significantly the potential for exploitation in future studies data from a broad range of systems including those involving synthetic polymers, proteins, DNA, nanoparticles and drugs.

Colliding drops as coalescing and fragmenting liquid springs

A universal modelling approach of drop fragmentation after head-on drop collisions is presented. In this approach, the colliding drops are seen as liquid springs that coalesce, compress and relax, leading the merged drop to break up if it reaches a critical aspect ratio. Combining energetic balance of the compression and relaxation phases with a Rayleigh-like criterion, we deduce the fragmentation threshold velocity for the collision of two and three drops of the same liquid and of two drops of immiscible liquids. Predictions and experimental results obtained for these three kinds of collisions using various liquids and drop sizes are found to be in good agreement over a wide domain whose boundaries are discussed.

Efficient Noise-Vibration-Harshness Modelling of Servo- and Traction Drives

Abstract: This paper advances a system-simulation based universal modelling approach for NVH-simulation of electric drives. The main idea is to use commercial FE-tools for model parametrisation and implement generic reduced electrodynamic and acoustic models in a system-simulation environment. The NVH-calculation itself is done in a system-simulation framework, which can be easily coupled with control and machine models for electric drives. The proposed approach is worked out in detail. As a first example, the simulation procedure is performed for an outrunner traction drive of electrified foldable scooters.

Support vector regression and synthetically mixed training data for quantifying urban land cover

Exploiting imaging spectrometer data with machine learning algorithms has been demonstrated to be an excellent choice for mapping ecologically meaningful land cover categories in spectrally complex urban environments. However, the potential of kernel-based regression techniques for quantitatively analyzing urban composition has not yet been fully explored. To a great extent, this can be explained by difficulties in deriving quantitative training information that reliably represents pairs of spectral signatures with associated land cover fractions needed for empirical modeling. In this paper we present an approach to circumvent this limitation by combining support vector regression (SVR) with synthetically mixed training data to map sub-pixel fractions of single urban land cover categories of interest. This approach was tested on Hyperspectral Mapper (HyMap) data acquired over Berlin, Germany. Fraction estimates were validated with extensive manual mappings and compared to fractions derived from multiple endmember spectral mixture analysis (MESMA). Our regression results demonstrate that the sets of multiple mixtures yielded high accuracies for quantitative estimates for four spectrally complex urban land cover types, i.e., fractions of impervious rooftops and pavements, as well as grass- and tree-covered areas. Despite the extrapolation uncertainty of SVR, which resulted in fraction values below 0% and above 100%, physically meaningful model outputs were reported for a clear majority of pixels, and visual inspection underpinned the quality of produced fraction maps. Statistical accuracy assessment with detailed reference information for 92 urban blocks showed linear relations with R2 values of 0.86, 0.58, 0.81 and 0.85 for the four categories, respectively. Mean absolute errors (MAE) ranged from 6.4 to 12.8% and block-wise sums of the four individually modeled category fractions were always around 100%. Results of MESMA followed similar trends, but with slightly lower accuracies. Our findings demonstrate that the combination of SVR and synthetically mixed training data enable the use of empirical regression for sub-pixel mapping. Thus, the strengths of kernel-based approaches for quantifying urban land cover from imaging spectrometer data can be well utilized. Remaining uncertainties and limitations were related to the known phenomena of spectral similarity or ambiguity of urban materials, the spectral deficiencies in shaded areas, or the dependency on comprehensive and representative spectral libraries. Therefore, the suggested workflow constitutes a new flexible and extendable universal modeling approach to map land cover fractions.

A Novel Holistic Modeling Approach for Generalized Sound Recognition

Nowadays, generalized sound recognition technology is constantly gaining attention within the generic context of scene analysis and understanding (smart-home, surveillance, bioacoustics, etc.). It is typically achieved using a set of relevant to the task at hand descriptors modelled by means of a statistical tool, e.g., hidden Markov model. This work exhaustively applies the Universal Modeling (UM) (or class-independent) approach on the particular task. The feature extraction engine extracts descriptors belonging to time, frequency and wavelet domains. We describe a novel data selection scheme based on Gaussian mixture model clustering for the creation of the UM. The scheme takes into account the dataset characteristics, adapts itself to them and leads to higher recognition rates than the standard UM approach.

A Modeling Approach of System Reliability Based on Statecharts

Abstract The paper puts forward a modeling approach of system reliability based on statecharts. Firstly, it analyzes the limitation of current reliability modeling approach, and introduces the elements and characters of statecharts. Then, it sets forth reliability modeling approach of state-independent system and state-dependent system. In the end, it reviews the modeling approach of system reliability based on statecharts. System reliability model based on statecharts is a universal modeling approach which can resolves the system reliability dynamics, depict the system reliability vividly combining the other reliability modeling methods.

A stochastic spatial channel model based on wave-propagation modeling

A novel stochastic spatial propagation channel model for wide-band spread spectrum applications is presented. The model is set up for indoor scenarios, although in principle it can be extended easily to urban environments. Deterministic ray-tracing results are used to produce the huge data sets required for the statistical evaluation of the parameters of the proposed model. The approach for the stochastic model is based on physical wave-propagation, where the channel is described by multipath components including angles of arrival at the receiver and angles of transmission at the transmitter. In each modeling step, path properties change according to the movement of the radio stations (RSs). The appearance and disappearance of multipath components is modeled by a genetic process. Especially, changing delay times of the propagation paths yield a realistic Doppler behavior of the channel. A new approach to model the directions of multipath components in three dimensions has been developed. By relating the angles of arrival (or transmission) to the direct line between transmitter and receiver, a universal modeling approach, which is independent of the actual geometry, becomes possible. The novel stochastic spatial channel model allows the simulation of complex systems including arbitrary antenna configurations and patterns. A detailed description of the overall model is presented together with some initial simulation results.