Multi-domain Model Research Articles

Bi-level planning approach for incorporating the demand-side flexibility of cloud data centers under electricity-carbon markets

With the prevalence of cloud computing applications, cloud data centers (CDCs) are proliferating around the world. However, in the context of hybrid energy‑carbon markets environment, the high energy costs and environmental expenses caused by CDC operation are pressing CDC owners to restructure the future development of CDC in a more economical and low-carbon manner. The potential spatio-temporal transferability and reducibility of workloads provides CDCs with significant flexibility in their operations and thus may interact with the power grid as active demand users. Nonetheless, other than private data centers, public CDCs have no direct control over the workloads submitted by terminal cloud users. As such, this paper presents a bi-level model for CDC allocation planning, so as to incorporate cloud service-demand response (CS-DR) from the perspective of a hybrid electricity-carbon market. The upper level pertains to multi-domain resource collaborative planning model, which determines the optimum siting and sizing of CDCs as well as the incentive design for CS-DR program, with the objective of maximizing the total expected benefits of CDC. The lower level models correspond to the market clearing (electricity-carbon tariff model) of Independent system operator (ISO) and the decision-making of cloud users regarding CS-DR participation. The proposed model belongs to a bi-level mixed integer nonlinear programming problem with two non-convex lower levels, which can be intractable in mathematics. To solve such difficult problem, a hybrid solution method combining multiple linearization techniques and reformulation and decomposition (R&D) strategy based on column-and-constraint generation (C&CG) algorithm is developed. The proposed model is demonstrated on a modified IEEE 30-bus test case, and the simulation results verified the effectiveness of the proposed approach.

Research on Multi-Domain Intelligent Customer Service Dialog Modeling with Integrated Transfer Learning Strategies

Abstract The intelligent customer service dialog model is centered on human-machine dialog, which has good prospects for commercial applications in multiple domains. In this paper, we use the Siamese-LSTM model to do vectorization of questions in the FAQ question and answer database to get the semantic representation vector of sentences, and then use the approximate retrieval algorithm to index the question and answer database and perform approximate nearest-neighbor retrieval of the query. After completing the question query, migration learning is employed to create a mapping between input questions and human responses, enabling the model to produce sentences that are similar to human responses. Tests show that the task success rate gradually stabilizes around 0.80 at about the 100th round and fluctuates up to around 0.986 after that. For the average number of conversation rounds, migration learning improves the conversation efficiency of intelligent customer service, and the average number of conversation rounds gradually stabilizes at about 150 rounds and eventually stabilizes at about 4.2 rounds as the number of training rounds increases. The transfer learning strategy helps machine responses to be as close to human responses as possible.

Multi-site, Multi-domain Airway Tree Modeling.

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to the quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and extensive clinical efforts for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Both quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage (https://atm22.grand-challenge.org/).

Role of nutrition and metabolic regulation in multidomain trials for dementia risk reduction: findings from FINGER and FINGER 2.0 studies

AbstractBackgroundThe successful FINGER multidomain intervention model combined dietary intervention (healthy Nordic diet) with exercise, cognitive training, social activities, and cardiovascular risk monitoring. The FINGER model is being tested and optimized in the World‐Wide FINGERS network of multidomain dementia prevention trials (45+ countries). Advanced FINGER 2.0 models combine lifestyle interventions with multi‐nutrients and/or putative disease‐modifying drugs.MethodThe 11‐year extended follow‐up of FINGER trial participants (N:1260, at‐risk general population) was completed in Q1‐2023. The MIND‐AD pilot‐trial (N:93, prodromal Alzheimer’s disease (AD)) included medical food (Fortasyn Connect), tested alone or in combination with multidomain lifestyle intervention for 6 months. The MET‐FINGER trial (N:600, at‐risk general population enriched with APOE4‐carriers, 2‐year intervention) includes different dosages of metformin (in combination with upgraded FINGER lifestyle intervention).ResultNew nutrition/metabolic related data will be presented from FINGER where eg. adherence to healthier diet was still relatively high in the multidomain arm after the extended follow‐up. Higher adherence was also related to better cognition. Several dietary and metabolic biomarkers have been analyzed revealing underlying mechanisms and responders (eg persons with insulin resistance, higher HOMA, responded less). In the MIND‐AD trial, participants from 4 European countries were randomized to the multidomain lifestyle intervention alone (N:32), or in combination with medical food (N:31), and 30 to the control group. Adherence was high for all intervention domains (78‐87%). Compared with the control group, the group with lifestyle+medical food showed a decrease in vascular risk burden (P = 0.027) and an increase in healthy diet patterns (P = 0.045), and better cognition after the intervention (CDR‐SOB). There were no significant differences between the lifestyle only and control group. MET‐FINGER trial recruitment started in January 2023 (preliminary data presented).ConclusionLonger‐term dietary and lifestyle changes are feasible and effective in older adults in the at‐risk spectrum of AD/dementia. Beneficial effects (adherence to and effects of interventions) seemed to improve with the inclusion of medical food in prodromal AD. The unique long follow‐up of FINGER provides important information about long‐term effects and methodological aspects. The MET‐FINGER trial will provide data on synergistic effects between FINGER intervention and a drug targeting both glucose metabolism and AD pathology.

A Multi-Fidelity Integration Method for Reliability Analysis of Industrial Robots

Abstract A multi-fidelity integration method is proposed to analyze the reliability of multiple performance indicators (MPI) for industrial robots. In order to high-fidelity mapping the performance of industrial robots, a unified multi-domain model (UMDM) is first established. The contribution-degree analysis is then used to classify the input random variables into interacting and non-interacting ones. Thus, the high-dimensional integration of reliability analysis is separated into a low-dimensional integration and multiple one-dimensional integrations in an additive form. Here, the low-dimensional integration consisting of the interacting variables is calculated using the high-precision mixed-degree cubature formula (MDCF), and the computational results are treated as high-fidelity data. The one-dimensional integration consisting of non-interacting variables is then computed by the highly efficient five-point Gaussian Hermite quadrature (FGHQ), and the computational results are named low-fidelity data. A multi-fidelity integration method is constructed by fusing the high-fidelity data and the low-fidelity data to obtain the statistical moments of the MPI. Subsequently, the probability density function and the failure probability of the MPI are estimated using the saddlepoint approximation method. Finally, some representative methods are performed to verify the superiority of the proposed method.

Electrochemical transport modelling and open-source simulation of pore-scale solid–liquid systems

The modelling of electrokinetic flows is a critical aspect spanning many industrial applications and research fields. This has introduced great demand in flexible numerical solvers to describe these flows. The underlying phenomena are microscopic, non-linear, and often involving multiple domains. Therefore often model assumptions and several numerical approximations are introduced to simplify the solution. In this work we present a multi-domain multi-species electrokinetic flow model including complex interface and bulk reactions. After a dimensional analysis and an overview of some limiting regimes, we present a set of general-purpose finite-volume solvers, based on OpenFOAM® , capable of describing an arbitrary number of electrochemical species over multiple interacting (solid or fluid) domains (Icardi and Barnett in F Municchi spnpFoam, 2021. https://doi.org/10.5281/zenodo.4973896). We provide a verification of the computational approach for several cases involving electrokinetic flows, reactions between species, and complex geometries. We first present three one-dimensional verification test-cases, and then show the capability of the solver to tackle two- and three-dimensional electrically driven flows and ionic transport in random porous structures. The purpose of this work is to lay the foundation of a general-purpose open-source flexible modelling tool for problems in electrochemistry and electrokinetics at different scales.

Experimental and Numerical Investigations on the Thermal Performance of Three Different Cold Plates Designed for the Electrical Vehicle Battery Module

The thermal performance of battery modules has a crucial role in the performance, safety, and lifetime of battery cells. Commonly, battery models are validated through experimental data to ensure the correctness of model behavior; however, the influences of experimental setups are often not considered in the laboratory environment, especially for prismatic cells such as lithium titanate oxide (LTO) battery cells used in electric vehicles. For this purpose, both experimental and numerical studies of the thermal performance of the battery module consisting of LTO cells was investigated using different cold plates used in electrical and hybrid vehicles. Three different discharging rates were applied to the battery module to obtain comparative results of the cooling performance. In the numerical simulations, heat generation models are typically used to observe the thermal behavior of the battery module; however, in the numerical study, dual potential multi-scale multi-domain (MSMD) battery models were used, with transient flow and heat transfer calculations performed. The numerical results were in good agreement with the experimental data. A new high-performance cold plate was developed for the thermal management of LTO battery cells. In comparison with the other two cold plate configurations, the proposed cold plate configuration dropped the maximum temperature up to 45% for the same operating conditions.

Passive-tracer modelling at super-resolution with Weather Research and Forecasting – Advanced Research WRF (WRF-ARW) to assess mass-balance schemes

Abstract. Super-resolution atmospheric modelling can be used to interpret and optimize environmental observations during top-down emission rate retrieval campaigns (e.g. aircraft-based) by providing complementary data that closely correspond to real-world atmospheric pollution transport and dispersion conditions. For this work, super-resolution model simulations with large-eddy-simulation sub-grid-scale parameterization were developed and implemented using WRF-ARW (Weather Research and Forecasting - Advanced Research WRF). We demonstrate a series of best practices for improved (realistic) modelling of atmospheric pollutant dispersion at super-resolutions. These include careful considerations for grid quality over complex terrain, sub-grid turbulence parameterization at the scale of large eddies, and ensuring local and global tracer mass conservation. The study objective was to resolve small dynamical processes inclusive of spatio-temporal scales of high-speed (e.g. 100 m s−1) airborne measurements. This was achieved by downscaling of reanalysis data from 31.25 km to 50 m through multi-domain model nesting in the horizontal and grid-refining in the vertical. Further, WRF dynamical-solver source code was modified to simulate the release of passive tracers within the finest-resolution domain. Different meteorological case studies and several tracer source emission scenarios were considered. Model-generated fields were evaluated against observational data (surface monitoring network and aircraft campaign data) and also in terms of tracer mass conservation. Results indicated agreement between modelled and observed values within 5 ∘C for temperature, 1 %–25 % for relative humidity, and 1–2 standard deviations for wind fields. Model performance in terms of (global and local) tracer mass conservation was within 2 % to 5 % of model input emissions. We found that, to ensure mass conservation within the modelling domain, tracers should be released on a regular-resolution grid (vertical and horizontal). Further, using our super-resolution modelling products, we investigated emission rate estimations based on flux calculation and mass-balancing. Our results indicate that retrievals under weak advection conditions (horizontal wind speeds < 5 m s−1) are not reliable due to weak correlation between the source emission rate and the downwind tracer mass flux. In this work we demonstrate the development of accurate super-resolution model simulations useful for planning, interpreting, and optimizing top-down retrievals, and we discuss favourable conditions (e.g. meteorological) for reliable mass-balance emission rate estimations.

OAG: Linking Entities Across Large-Scale Heterogeneous Knowledge Graphs

Different knowledge graphs for the same domain are often uniquely housed on the Web. Effectively linking entities from different graphs is critical for building an open and comprehensive knowledge graph. However, linking entities across different sources has thus far faced various challenges, including the increasingly large-scale volume of the data, the heterogeneity of the graphs, and the ambiguity of real-world entities. To address them, we propose a unified framework LinKG. Specifically, we decouple the problem into different linking tasks based on the unique properties of each type of entity. To link word sequence based entities, we propose an LSTM-based method to capture word dependencies. To link entities of large scale, we utilize the hashing technique and convolutional neural networks for scalable and accurate linking. To link ambiguous entities, we propose heterogeneous graph attention networks to leverage heterogeneous structural information. Finally, to validate the design choices of different LinKG modules, we characterize the relationships between different tasks based on the single-domain and multi-domain transfer models. Extensive experiments demonstrate the effectiveness of LinKG with an overall F1-score of 95.15%, based on which we deploy and release the Open Academic Graph (OAG)—the largest publicly available heterogeneous academic graph to date.

Modeling and simulation of a micro gas-cooled nuclear reactor using Modelica

Highly compact micro nuclear reactors, which offer extensive energy benefits across ocean, land, space, and sky applications, have recently emerged as a popular research topic within the international nuclear industry. Due to its excellent inherent safety characteristics, the gas-cooled graphite-moderated reactor with TRISO fuel has attained extensive attention. Nonetheless, micro-reactors exhibit a high degree of system integration, characterized by the tight coupling and mutual constraints among various system functions. Conventional discipline-specific decoupled design patterns find it challenging to tackle the complexity arising from multi-disciplinary couplings. In response, this paper investigates the application of Modelica, a multi-domain unified modeling language, to construct models for several subsystems, encompassing the reactor, energy conversion system, and control system. This approach aims to enhance support for cross-disciplinary design. The accuracy of the reactor core model was verified by high-fidelity CFD simulation results, demonstrating a good agreement. Further investigations were then conducted on the safety and operational characteristics of the whole system. Typically, two simulations were conducted on the Gas cooled micro nuclear reactor (GCMR) design: one focused on an anticipated transients without scram accident scenario and the other on load-following operation. The simulation results demonstrated that the reactor possesses excellent inherent safety, even during extreme accidents. In such scenarios, the reactor is able to achieve shutdown solely through the negative reactivity resulting from increased core temperature. Furthermore, considering the heat accommodated in the reactor system and the constantly generated decay heat, a passive air-cooling mechanism has been investigated and successfully demonstrated with the model. The reactor also exhibits good load-following performance, which can be achieved by simply adjusting helium inventory (or pressure) and control drum position, while maintaining constant core temperature and power generation efficiency. These results can be leveraged to provide guidelines for further detailed designs of the GCMR.

A FAIR catalog of ontology-driven conceptual models

Multi-domain model catalogs serve as empirical sources of knowledge and insights about specific domains, about the use of a modeling language’s constructs, as well as about the patterns and anti-patterns recurrent in the models of that language crosscutting different domains. They may support domain and language learning, model reuse, knowledge discovery for humans, and reliable automated processing and analysis if built following generally accepted quality requirements for scientific data management. More specifically, not unlike scientific (meta)data, models should be shared according to the FAIR principles (Findability, Accessibility, Interoperability, and Reusability). In this paper, we report on the construction of a FAIR model catalog for Ontology-Driven Conceptual Modeling research, a trending paradigm lying at the intersection of conceptual modeling and ontology engineering in which the Unified Foundational Ontology (UFO) and OntoUML emerged among the most adopted technologies. The catalog, publicly available at https://w3id.org/ontouml-models, currently includes over one hundred and forty models, developed in a variety of contexts and domains.

Network Resource Allocation Algorithm Using Reinforcement Learning Policy-Based Network in a Smart Grid Scenario

The exponential growth in user numbers has resulted in an overwhelming surge in data that the smart grid must process. To tackle this challenge, edge computing emerges as a vital solution. However, the current heuristic resource scheduling approaches often suffer from resource fragmentation and consequently get stuck in local optimum solutions. This paper introduces a novel network resource allocation method for multi-domain virtual networks with the support of edge computing. The approach entails modeling the edge network as a multi-domain virtual network model and formulating resource constraints specific to the edge computing network. Secondly, a policy network is constructed for reinforcement learning (RL) and an optimal resource allocation strategy is obtained under the premise of ensuring resource requirements. In the experimental section, our algorithm is compared with three other algorithms. The experimental results show that the algorithm has an average increase of 5.30%, 8.85%, 15.47% and 22.67% in long-term average revenue–cost ratio, virtual network request acceptance ratio, long-term average revenue and CPU resource utilization, respectively.

A More Accurate Systems Modelling and Simulation Framework in Design and Optimization of A System During the Conceptual Phase

Modelling and simulation of complex mechanical systems involving multi-simulation environment, with great accuracy is essential as part of the conceptual design phase of a system under design. The co-simulation between system level model with various subsystems based on Model Based Design (MBD) and component level model is a perfect example. These mathematical models have distinct features such as the type of differential equations used to represent its physics, boundary conditions etc. In this study we use MBD methodology to develop a simulation model that can drive product development and optimization of mechanical systems. We also incorporate a 3-D Computational Fluid Dynamics (CFD) model as one of its subsystem into the System Level (SL) model architecture. This is done by creating a co-simulation interface between these two different modelling environments and exchange boundary conditions via a specific interface. The interaction between multi-domain models through system simulation together with the detailed fluid flow and thermal analysis of the component through CFD is finally explained. Finally, the dynamic behaviour of the complete system and its temporal and spatial scales is shown to be captured in more detail.

A Unified Switched Nonlinear Dynamic Model of an Electric Vehicle for Performance Evaluation

The advanced modeling and estimation of overall system dynamics play a vital role in electric vehicles (EVs), as the selection of components in the powertrain and prediction of performance are the key market qualifiers. The state-space averaged model and small-signal transfer function model are useful for assessing long-term behavior in system-level analysis and for designing the controller parameters, respectively. Both models take less computation time but ignore the high-frequency switching dynamics. Therefore, these two models could be impractical for the development and testing of EV prototypes. On the other hand, the multi-domain model in available simulation tools gives in-depth information about the short-term behavior and loss analysis of power electronic devices in each subsystem, considering the switching dynamics in a long computation time. In this paper, a general mathematical framework for the dynamical analysis of complete EVs is presented using a unified, switched nonlinear model. This equation-based model runs faster than the available module-based simulation tools. Two other models, namely the time domain state-space averaged model and frequency domain small-signal transfer function model, are also developed from the switched nonlinear model for the analysis with less computation time. The design and performance of an EV with two different motors and its controllers are evaluated using the general mathematical framework.

Knowledge-graph-based multi-domain model integration method for digital-twin workshops

Knowledge-graph-based multi-domain model integration method for digital-twin workshops

A Curriculum Learning Approach for Multi-Domain Text Classification Using Keyword Weight Ranking

Text classification is a well-established task in NLP, but it has two major limitations. Firstly, text classification is heavily reliant on domain-specific knowledge, meaning that a classifier that is trained on a given corpus may not perform well when presented with text from another domain. Secondly, text classification models require substantial amounts of annotated data for training, and in certain domains, there may be an insufficient quantity of labeled data available. Consequently, it is essential to explore methods for efficiently utilizing text data from various domains to improve the performance of models across a range of domains. One approach for achieving this is through the use of multi-domain text classification models that leverage adversarial training to extract domain-shared features among all domains as well as the specific features of each domain. After observing the varying distinctness of domain-specific features, our paper introduces a curriculum learning approach using a ranking system based on keyword weight to enhance the effectiveness of multi-domain text classification models. The experimental data from Amazon reviews and FDU-MTL datasets show that our method significantly improves the efficacy of multi-domain text classification models adopting adversarial learning and reaching state-of-the-art outcomes on these two datasets.

Non-linear and multi-domain modelling of a free piston engine with linear electric machine

In the current technological era, with the rapid advances in low-carbon power generation solutions, there has been renewed interest in the Free Piston Engine (FPE) as a viable thermal propulsion technology. However, describing and capturing the intrinsic non-linear dynamics of the FPE is computationally challenging and, understandably, is generally characterised by a simplified linear description, which may inherently omit the dominant dynamics.Consequently, this study considers the non-linear and multi-directional nature of the coupled translation loads during motoring and power generation. Thus, presenting a non-linear and multi-domain description of the FPE. The proposed model was implemented in an opposed-piston free-piston engine configuration and validated during motoring and generating against experimental data captured from a prototype with identical parameters. The simulation results strongly correlate to the experimental data with similar translator displacement, in-cylinder pressure and bounce chamber pressure traces. Furthermore, the study demonstrated that the proposed modelling approach captured the dominant dynamics of the FPE and proved satisfactory accuracy and performance. In addition to validating the proposed modelling approach, this paper concludes with a simulation study that demonstrates the embedding of a novel surrogate Chemical Kinetics (CK) description of the hydrous ethanol auto-ignition process and the resultant influence on the FPE dynamics, most importantly, on gaseous emission formation.Therefore, this paper aims to extend the current knowledge in this novel and emerging technology by considering the characteristic non-linearity and multi-directional nature of its coupling with multi-physical domains such as mechanical, chemical and electrical.

Phase field study on the flexoelectric response of dielectric–ferroelectric multilayers

We report a theoretical modeling of the flexoelectric response of dielectric–ferroelectric (DE–FE) multilayers based on phase field simulations in the framework of the Landau–Ginzburg–Devonshire (LGD) theory. The correlation between negative capacitance and flexoelectric response is revealed, and the single-domain and multi-domain models are compared. It shows that the dielectric layers drive the ferroelectric layer into a negative capacitance regime, and the flexoelectric response of the multilayer is maximal when the negative capacitance of the ferroelectric layer has a minimal absolute value. Moreover, the flexoelectric response peak will be shifted to a lower temperature by increasing the thickness of dielectric layer, indicating a possibility of achieving a stronger flexoelectric response at room temperature compared with that of pure ferroelectric. However, while the single-domain model shows that the flexoelectric response peak is simply shifted to a lower temperature with near constant peak value and width, the multi-domain model reveals a significant suppressing of the flexoelectric peak by the dielectric layer. This is attributed to the formation of the vortex domain state, which eases the depolarization effect and leads to large absolute value of negative capacitance of the ferroelectric layer. Our work provides new insights into flexoelectricity in ferroelectric heterostructures.

A spatially distributed model of brain metabolism highlights the role of diffusion in brain energy metabolism

The different active roles of neurons and astrocytes during neuronal activation are associated with the metabolic processes necessary to supply the energy needed for their respective tasks at rest and during neuronal activation. Metabolism, in turn, relies on the delivery of metabolites and removal of toxic byproducts through diffusion processes and the cerebral blood flow. A comprehensive mathematical model of brain metabolism should account not only for the biochemical processes and the interaction of neurons and astrocytes, but also the diffusion of metabolites. In the present article, we present a computational methodology based on a multidomain model of the brain tissue and a homogenization argument for the diffusion processes. In our spatially distributed compartment model, communication between compartments occur both through local transport fluxes, as is the case within local astrocyte-neuron complexes, and through diffusion of some substances in some of the compartments. The model assumes that diffusion takes place in the extracellular space (ECS) and in the astrocyte compartment. In the astrocyte compartment, the diffusion across the syncytium network is implemented as a function of gap junction strength. The diffusion process is implemented numerically by means of a finite element method (FEM) based spatial discretization, and robust stiff solvers are used to time integrate the resulting large system. Computed experiments show the effects of ECS tortuosity, gap junction strength and spatial anisotropy in the astrocyte network on the brain energy metabolism.

Multi-domain Mapping design Method of Automatic Disassembly Equipment for Small Electromechanical Products

The design of automatic disassembly equipment for small end-of-life (EOL) electromechanical products is difficult and time-consuming. To deal with these problems, a novel automatic disassembly equipment design method for small EOL electromechanical products based on axiomatic design and modular design is presented. To realize the mapping from disassembly knowledge to the functional requirements of automatic disassembly equipment, a transfer mapping model from the structure to disassembly methods of small EOL electromechanical products is constructed based on polychromatic sets, which are regarded as the key information of the customer domain. Furthermore, a multi-domain mapping model of the automatic disassembly equipment is constructed through axiomatic design, and the mapping from the customer domain to the functional domain and then to the structure domain in the design process of automatic disassembly equipment is gradually realized. Based on the information in the structure domain, the related modules of the automatic disassembly equipment are divided and designed. Finally, taking a power strip and a set-top box as the experimental objects, the proposed method is applied to the design of automatic disassembly equipment. The results prove that the proposed method improves the design efficiency of automatic disassembly equipment and has strong universality.