Domain Model Research Articles

Optimal scheduling of energy routers based on distributed CAES systems

Abstract With the rise of renewable energy, distributed energy, and other different forms of energy, the architecture and function of the energy router are relatively perfect, for the topology of the energy router itself, different forms of energy in the energy router flow, conversion to realize the large-scale use of distributed energy equipment has also gradually become a hot spot of research. However, there are fewer studies on the energy router with a distributed CAES system to participate in the optimal scheduling of energy for integrated energy systems. Therefore, this paper is based on the operational domain model of an energy router with a distributed CAES system and analyzes the physical characteristics and coupling relationship of non-energy storage components, takes air pressure, temperature, and oil volume as state variables, takes charging, power generation, and heating power as external interfaces, and utilizes internal interface variables such as air quality and flow rate to realize energy storage, flow, and other internal interface variables to realize the communication between energy storage and non-energy storage elements, and finally determine the operation domain of the system. Secondly, the feasible domain of the energy router based on the distributed CAES system is portrayed, and the optimization of the system parameters is guided with the help of the relative distance by establishing the electric and thermal power constraints and the storage state constraints. Finally, the effectiveness of the feasible domain analysis method in the energy router of a distributed CAES system is verified by taking a northern region as an example.

Effect of crown gap thickness on axial force of high-head pump turbine

Abstract The size of the runner crown gap is one of the factors affecting the volume loss of the pump turbine, and the unreasonable gap thickness is unfavourable to the unit operation. By establishing a three-dimensional full-channel fluid domain model of a high-head pump turbine, and using the computational fluid dynamics (CFD) method to perform numerical calculations on models with different crown gap thicknesses, the effects of axial water thrust on runners with different upper crown gap thicknesses are clarified. The results of simulation indicates that the resultant force of axial water thrust on the runner is vertically upward under different gap thicknesses, which is far less than the weight of the whole rotating part. The axial water thrust is very sensitive to the change of the seal gap value, the greater the seal gap value, the greater the axial water thrust on the unit, and the axial water thrust begins to decrease after increasing to a certain value. The research results are referable for the study of the gap flow characteristics and the crown gap design.

Cross-condition bearing fault detection based on online drift detection and domain adaptation

Aiming at the problem that the data distribution of bearings across operating conditions generates offset resulting in insufficient diagnostic accuracy of the original model for new data, a cross-condition bearing fault detection method based on online drift detection and domain adaptation is proposed. First, the original one-dimensional vibration signals collected are transformed by a two-dimensional wavelet transform to convert the time-frequency image dataset. Second, the drift detection of the data across operating conditions is carried out using Random Forest (RF), and the 3σ criterion as well as the drift detection judgment criteria are set. Next, the source domain model based on Googlenet is used to extract features from the target domain data, and the Whale Optimization Algorithm to Improve Local Preserving Projection Algorithm (WOA-LPP) algorithm is combined to construct a brand-new projection space to align the features of the source and target domains. Then, the source and target domain features are reconstructed by combining the LPP optimal projection matrix to construct a fully connected network trained by the source domain features. Finally, probabilistic label-based decision fusion is proposed to integrate multiple classifiers to reduce the effects of model training randomness and strong noise interference. Validated by the publicly available Western Reserve University bearing data, the method proposed in this paper has good detection accuracy as well as robustness across operating conditions, which can effectively improve the defects of shifting data distribution and degradation of model accuracy under variable speed.

Trajectory compensation based adaptive control (TCAC) for high-precision piezoelectric actuators

Piezoelectric actuator (PEA) has been widely used in micro-systems, due to the advantages of nano-scale precision, rapid response speed, high stiffness, etc. Nevertheless, the intrinsic nonlinearities of PEA, particularly hysteresis, severely deteriorate its positioning and tracking control accuracy. In most existing control algorithms of PEA, the approximate inverse model of hysteresis is developed to compensate for motion error, which requires high modeling accuracy. To avoid the influence of complex modeling procedures and modeling errors on tracking precision, a trajectory compensation-based adaptive control method has been proposed in this paper. Firstly, a simple linear controller is used to establish the closed-loop feedback system and to guarantee the basic tracking performance of the PEA system. And then, the trajectory compensation is designed according to the frequency analysis of the closed-loop control system. Finally, nonlinear adaptive compensation is used to eliminate the residual errors of the above linear frequency domain model. Different from conventional controllers, the proposed method can achieve excellent tracking control performance without any hysteresis model of PEA. The stability and convergence of the proposed method have been theoretically proved. In addition to theoretical guarantees, the proposed method is validated experimentally and demonstrated to achieve the root-mean-square and maximum value of tracking errors to less than 8 nm and 25 nm, respectively, which has a clear accuracy advantage over other methods.

Data Assimilation Informed Model Structure Improvement (DAISI) for Robust Prediction Under Climate Change: Application to 201 Catchments in Southeastern Australia

AbstractThis paper presents a method to analyze and improve the set of equations constituting a rainfall‐runoff model structure based on a combination of a data assimilation algorithm and polynomial updates to the state equations. The method, which we have called “Data Assimilation Informed model Structure Improvement” (DAISI) is generic, modular, and demonstrated with an application to the GR2M model and 201 catchments in South‐East Australia. Our results show that the updated model generated with DAISI generally performed better for all metrics considered included Kling‐Gupta Efficiency, NSE on log transform flow and flow duration curve bias. In addition, the elasticity of modeled runoff to rainfall is higher in the updated model, which suggests that the structural changes could have a significant impact on climate change simulations. Finally, the DAISI diagnostic identified a reduced number of update configurations in the GR2M structure with distinct regional patterns in three sub‐regions of the modeling domain (Western Victoria, central region, and Northern New South Wales). These configurations correspond to specific polynomials of the state variables that could be used to improve equations in a revised model. Several potential improvements of DAISI are proposed including the use of additional observed variables such as actual evapotranspiration to better constrain internal model fluxes.

Comparing Generic Paediatric Health-Related Quality-of-Life Instruments: A Dimensionality Assessment Using Factor Analysis.

Widely used generic instruments to measure paediatric health-related quality of life (HRQoL) include the EQ-5D-Y-5L, Child Health Utility 9 Dimension (CHU-9D), Paediatric Quality of Life Inventory (PedsQL) and Health Utilities Index (HUI). There are similarities and differences in the content of these instruments, but there is little empirical evidence on how the items they contain relate to each other, and to an overarching model of HRQoL derived from their content. This study aimed to explore the dimensionality of the instruments using exploratory factor analysis (EFA). Data from the Australian Paediatric Multi-Instrument Comparison (P-MIC) Study were used. EQ-5D-Y-5L, CHU-9D, PedsQL and HUI data were collected via proxy or child self-report data. EFA was used to investigate the underlying domain structure and measurement relationship. Items from the four instruments were pooled and domain models were identified for self- and proxy-reported data. The number of factors was determined based on eigenvalues greater than1. A correlation cut-off of 0.32 was used to determine item loading on a given factor, with cross-loading also considered. Oblique rotation was used. Results suggest a six-factor structure for the proxy-reported data, including emotional functioning, pain, daily activities, physical functioning, school functioning, and senses, while the self-report data revealed a similar seven-factor structure, with social functioning emerging as an additional factor. We provide evidence of differences and similarities between paediatric HRQoL instruments and the aspects of health being measured by these instruments. The results identified slight differences between self- and proxy-reported data in the relationships among items within the resulting domains.

Unravelling Stock Market Patterns: Analysis and Predictive Modelling Using Time Series and Deep Learning

Abstract: Within the dynamic landscape of the financial markets, where fortunes can shift with the wind, the precision of future prediction holds immense value. This research embarks on a quest to enhance the accuracy of stock market forecasts, venturing into the domain of advanced time series models. We shall closely examine three prominent methodologies: the venerable ARIMA, the multifaceted VARMA, and the potent deep learning architecture known as LSTM. Through a rigorous investigation of their strengths and limitations, we shall subject these models to both univariate and multivariate data extracted from the Indian stock market. This comparative analysis aims to glean invaluable insights into their predictive capabilities, ultimately paving the path for the development of even more sophisticated forecasting tools. By wielding these instruments with confidence, investors can navigate the intricate dynamics of the market with a newfound certainty and optimize their decision-making for greater returns. Our endeavor transcends mere numerical analysis; it seeks to illuminate the complex dance of the market, unravel its hidden patterns, and ultimately empower investors to gain the upper hand in this challenging yet potentially rewarding financial sphere.

Graphical Specification of Sensitive Business Processes

This paper aims at developing a new BPMN extension, called “BPMN4SBP”, supporting the multi-dimensional modeling of Sensitive Business Processes (SBPs) (i.e., the knowledge, functional, organizational, behavioral, informational and intentional dimensions). It aims to explicitly integrate all the relevant issues and aspects relevant at the coupling of the business process modeling (BPM) domain and the knowledge management (KM) domain for improving the identification and management of crucial knowledge which are mobilized and created by these processes. This paper provides the analysis of requirements and relevant concepts for modeling SBP. Based on a core domain ontology, need for extension is identified and the valid BPMN4SBP extension is designed (according to the BPMN extension mechanism) by the construction of a conceptional domain model and the corresponding BPMN extension model. This extended BPMN4SBP meta-model is derived by applying model transformation rules and by adapting, also, the UML profile mechanism to BPMN. The applicability of the proposed BPMN extension is demonstrated by modeling an SBP in a real case study in the healthcare domain.

Efficiently approaching vertical federated learning by combining data reduction and conditional computation techniques

In this paper, a framework based on a sparse Mixture of Experts (MoE) architecture is proposed for the federated learning and application of a distributed classification model in domains (like cybersecurity and healthcare) where different parties of the federation store different subsets of features for a number of data instances. The framework is designed to limit the risk of information leakage and computation/communication costs in both model training (through data sampling) and application (leveraging the conditional-computation abilities of sparse MoEs). Experiments on real data have shown the proposed approach to ensure a better balance between efficiency and model accuracy, compared to other VFL-based solutions. Notably, in a real-life cybersecurity case study focused on malware classification (the KronoDroid dataset), the proposed method surpasses competitors even though it utilizes only 50% and 75% of the training set, which is fully utilized by the other approaches in the competition. This method achieves reductions in the rate of false positives by 16.9% and 18.2%, respectively, and also delivers satisfactory results on the other evaluation metrics. These results showcase our framework’s potential to significantly enhance cybersecurity threat detection and prevention in a collaborative yet secure manner.

Bulk resistance and contact impedance: Particular solution for annulus, homogeneous domain and dimensional analysis of the complete electrode model

Viewing the complexity of multiphase flows, in which multi-dimensional and time-changing fluid structures, known as flow patterns, must be captured, electrical impedance tomography (EIT) stands out as an alternative for fluid flow visualization. Using an EIT system, this study presents an enhanced approach for measuring film thickness in two-phase flows. Incorporating bulk resistance and contact impedance contributions, the proposed ohmic model significantly improves accuracy, particularly in low-conductivity scenarios, yielding a robust, accurate, and rapid technique for obtaining images from an EIT-based low-cost acquisition system. Tomographic experiments validate the model’s efficacy using cylindrical sensors and diverse conductivity mediums, including two distinct phantoms. The dimensional analysis of the model yields five dimensionless numbers, revealing interrelations between electrical parameters and sensor construction. A correlation based on a dimensionless number for the contact impedance is obtained. Additionally, the experiments demonstrate a strong interplay with the polarization phenomenon.

EXPLORING BACKDOOR ATTACKS IN OFF-THE-SHELF UNSUPERVISED DOMAIN ADAPTATION FOR SECURING CARDIAC MRI-BASED DIAGNOSIS.

The off-the-shelf model for unsupervised domain adaptation (OSUDA) has been introduced to protect patient data privacy and intellectual property of the source domain without access to the labeled source domain data. Yet, an off-the-shelf diagnosis model, deliberately compromised by backdoor attacks during the source domain training phase, can function as a parasite-host, disseminating the backdoor to the target domain model during the OSUDA stage. Because of limitations in accessing or controlling the source domain training data, OSUDA can make the target domain model highly vulnerable and susceptible to prominent attacks. To sidestep this issue, we propose to quantify the channel-wise backdoor sensitivity via a Lipschitz constant and, explicitly, eliminate the backdoor infection by overwriting the backdoor-related channel kernels with random initialization. Furthermore, we propose to employ an auxiliary model with a full source model to ensure accurate pseudo-labeling, taking into account the controllable, clean target training data in OSUDA. We validate our framework using a multi-center, multi-vendor, and multi-disease (M&M) cardiac dataset. Our findings suggest that the target model is susceptible to backdoor attacks during OSUDA, and our defense mechanism effectively mitigates the infection of target domain victims.

A model-based study of the dynamics of Arctic low-level jet events for the MOSAiC drift

Low-level jets (LLJs) are studied for the period of the ship-based experiment MOSAiC 2019/2020 using the regional climate model Consortium for Small-scale Model—Climate Limited area Mode (CCLM). The model domain covers the whole Arctic with 14 km resolution. CCLM is run in a forecast mode (nested in ERA5) and with different configurations of sea ice data for the winter. The focus is on the study of LLJs for the MOSAiC site. LLJs are detected using model output every 1 h. We define LLJ events as LLJs that last at least 6 h. Case studies of LLJ events are shown using wind lidar and radiosonde data as well as CCLM simulations. LLJs are not local events but are embedded in large jet structures extending for hundreds of kilometers that are advected toward the MOSAiC site. CCLM simulations are used to study the statistics of LLJs of all profiles and of LLJ events as well as the dynamics. LLJs are found in about 40% of the hourly profiles, but only 26% of the hourly profiles are associated with LLJ events. Strong LLJs (≥15 m/s) are detected in 13% of the hourly profiles, which is about the same fraction as for strong LLJ events. The mean duration of events is about 12 h. The LLJ events are characterized using dynamical criteria for the wind speed profile and the evolution of the jet core. A fraction of 35% of the LLJ events are baroclinic, but more than 40% of the LLJ events show a large contribution of advection to the initial generation as well as for the evolution of the jet core. Only very few events fulfill the criteria of inertial oscillations. LLJ events occur for all months, but strong events have a higher frequency during winter. The turbulent kinetic energy in the lower atmospheric boundary layer (ABL) is twice (4 times) as large for LLJs (strong LLJs) than for situations without LLJs, which underlines the impact of LLJs on turbulent processes in the ABL.

Estimating hydraulic diffusivity in coastal confined aquifer under tidal fluctuation using a frequency domain model

Oceanic tidal propagation into the coastal aquifers is a cost-effective method for estimating hydraulic parameters in coastal aquifers, and identifying the most suitable parameter estimation method for coastal aquifers based on the observations has become an intensively studied subject. In this study, the frequency domain model, LPMLE3 (combination of a local polynomial (LP) signal processing technique with a maximum likelihood estimator (MLE)), was applied to estimate the hydraulic diffusivity (λ) of the coastal aquifer by adjusting the mathematical model in LPMLE3. The mathematical model in LPMLE3 has been adjusted by removing the convective term from the convective diffusion equation within LPMLE3, and a newly developed frequency-domain analytical solution that describes the head changes due to tidal fluctuations in coastal confined aquifers was used to replace the diffusion term. For comparison, an analytical solution in the time domain was also derived by Green’s functions method. After that, the particle swarm optimization algorithm and Markov Chain Monte Carlo method were used to estimate λ. Results show that LPMLE3 performed well in estimating homogeneous or heterogeneous aquifers with complicated boundary conditions. The estimated error of λ is within 10 % in a heterogeneous aquifer when the variance of the natural logarithm of the aquifer hydraulic conductivity is less than 0.05 (σlnK2≤0.05). If the coastal aquifer has a sloping beach, the estimated λ by LPMLE3 method accurately when the sloping beach angle is greater than 65°. As for an inclined coastal aquifer, the dip angle has a greater influence on the offshore areas than the nearshore areas and the estimation errors are relatively small (less than 15 %) when the dip angle is within a range of 0- 15°.

A Model of Core Emotional Needs and Toxic Experiences: Their Links with Schema Domains, Well-Being, and Ill-Being.

This study examined the second-order schema domains of Early Maladaptive and Adaptive Schemas based on recent trends and compared them with the five theoretical second-order schema domains commonly used in schema therapy. Using six international Eastern and Western community samples-Singapore (n = 628), Malaysia (n = 229), USA (n = 396), South Africa (n = 390), Nigeria (n = 364), India (n = 306)-confirmatory factor analysis showed that the four second-order domains of EMSs and EASs, which ran almost parallel with each other, were the most robust models calling into question the validity of the five domain model. Given the hypothesized links between schemas and needs, these four categories of EMSs and EASs represent four categories of toxic experiences and core emotional needs, respectively. These categories were supported empirically and are useful to parents as well as to clinicians as they approach child rearing and the treatment of clients in schema therapy from the vantage point of needs. These four categories of psychological core emotional needs, as well as toxic experiences, were found, as expected, to be linked with various measures of well-being and ill-being.

Impact of high-quality, mixed-domain data on the performance of medical language models.

To optimize the training strategy of large language models for medical applications, focusing on creating clinically relevant systems that efficiently integrate into healthcare settings, while ensuring high standards of accuracy and reliability. We curated a comprehensive collection of high-quality, domain-specific data and used it to train several models, each with different subsets of this data. These models were rigorously evaluated against standard medical benchmarks, such as the USMLE, to measure their performance. Furthermore, for a thorough effectiveness assessment, they were compared with other state-of-the-art medical models of comparable size. The models trained with a mix of high-quality, domain-specific, and general data showed superior performance over those trained on larger, less clinically relevant datasets (P < .001). Our 7-billion-parameter model Med5 scores 60.5% on MedQA, outperforming the previous best of 49.3% from comparable models, and becomes the first of its size to achieve a passing score on the USMLE. Additionally, this model retained its proficiency in general domain tasks, comparable to state-of-the-art general domain models of similar size. Our findings underscore the importance of integrating high-quality, domain-specific data in training large language models for medical purposes. The balanced approach between specialized and general data significantly enhances the model's clinical relevance and performance. This study sets a new standard in medical language models, proving that a strategically trained, smaller model can outperform larger ones in clinical relevance and general proficiency, highlighting the importance of data quality and expert curation in generative artificial intelligence for healthcare applications.

Theorising in the business model domain: some pathways to pursue

Theorising in the business model domain: some pathways to pursue

Unravelling the influence of heterogeneities and abstraction on groundwater flow and solute transport in a fractured carbonate aquifer, Sicily, Italy

The migration of a groundwater contaminant plume consisting of light nonaqueous phase liquids (LNAPLs) along the Ionian coastline of Sicily, Italy, has been reported to follow a trajectory that is inconsistent with the regional hydraulic gradient. The influence of several faults affecting the fractured carbonate bedrock aquifer, and groundwater abstraction from a well, were hypothesized to be responsible for the anomalous trajectory of the contaminant plume. A conceptual hydrogeological model was developed for the study area that incorporated structural information derived from geophysical surveys and the mapping of fractures in bedrock outcrops. This conceptual model was incorporated into numerical groundwater flow and solute transport models to simulate the groundwater transport of the light nonaqueous phase liquids. Four model scenarios representing different levels of complexity were tested to assess the relative influence of geological heterogeneity and groundwater abstraction on the migration of the contaminant plume. Results show that underground major discontinuity systems, invoking the presence of the faults in the model domain accounted for the observed migration of the contaminant plume, act as conduits for groundwater flow. Conversely, groundwater abstraction from a well was found to result in relatively minor, localized impacts to the migration of the contamination plume. This study demonstrates the importance of incorporating geological heterogeneity into groundwater modelling and environmental risk assessments associated with the storage of LNAPLs.

Parallel SnowModel (v1.0): a parallel implementation of a distributed snow-evolution modeling system (SnowModel)

Abstract. SnowModel, a spatially distributed snow-evolution modeling system, was parallelized using Coarray Fortran for high-performance computing architectures to allow high-resolution (1 m to hundreds of meters) simulations over large regional- to continental-scale domains. In the parallel algorithm, the model domain was split into smaller rectangular sub-domains that are distributed over multiple processor cores using one-dimensional decomposition. All the memory allocations from the original code were reduced to the size of the local sub-domains, allowing each core to perform fewer computations and requiring less memory for each process. Most of the subroutines in SnowModel were simple to parallelize; however, there were certain physical processes, including blowing snow redistribution and components within the solar radiation and wind models, that required non-trivial parallelization using halo-exchange patterns. To validate the parallel algorithm and assess parallel scaling characteristics, high-resolution (100 m grid) simulations were performed over several western United States domains and over the contiguous United States (CONUS) for a year. The CONUS scaling experiment had approximately 70 % parallel efficiency; runtime decreased by a factor of 1.9 running on 1800 cores relative to 648 cores (the minimum number of cores that could be used to run such a large domain because of memory and time limitations). CONUS 100 m simulations were performed for 21 years (2000–2021) using 46 238 and 28 260 grid cells in the x and y dimensions, respectively. Each year was simulated using 1800 cores and took approximately 5 h to run.

Deriving requirements for integrated and standardised cadastre profile from the legacy Board of Revenue and the contemporary land administration systems

Modernising the paper-based manual Land Administration System (LAS) benefits society at large. This study examines the integration and standardisation requirements of LASs in Islamabad, Pakistan, using the Land Administration Domain Model (LADM). The exploratory case study approach assesses land tenure, value, use and development aspects. The existing LASs satisfy LADM requirements for a unified LAS. Systematic transformations are required to upgrade revenue sketch maps and drawings into accurate geospatial data in 2D and above and below-ground infrastructure into 3D. Developing a LADM edition I & II country profile is recommended for land tenure security, socioeconomic development and sustainable urban growth.

Perception-Dominant Control Types for Human/Machine Systems

We explore a novel approach to complex domain modelling by emphasising primitives based on perception. The usual approach either focuses on actors or cognition associated with tokens that convey information. In related research, we have examined using effects and/or outcomes as primitives, and influences as the generator of those outcomes via categoric functors. That approach (influences, effects) has advantages: it leverages what is known and supports the expanded logics we use, where we want to anticipate and engineer possible futures. But it has weaknesses when placed in a dynamic human-machine system where what is perceived or assumed matters more than what is known. The work reported here builds on previous advances in type specification and reasoning to ‘move the primitives forward’ more toward situation encounter and away from situation understanding. The goal is in the context of shared human-machine systems where: • reaction times are shorter than the traditional ingestion/comprehension/response loop can support; • situations that are too complex or dynamic for current comprehension by any means; • there simply is insufficient knowledge about governing situations for the comprehension model to support action; and/or, • the many machine/human and system/system interfaces that are incapable of conveying the needed insights; that is, the communication channels choke the information or influence flows. While the approach is motivated by the above unfriendly conditions, we expect significant benefits. We will explore these but engineer toward a federated decision paradigm where decisions by local human, machine or synthesis are not whole-situation-aware, but that collectively ‘swarm’ locally across the larger system to be more effective, ‘wiser’ than a convention paradigm may produce. The supposed implementation strategy will be through extending an existing ‘playbooks as code’ project whose goals are to advise on local action by modelling and gaming complex system dynamics. A sponsoring context is ‘grey zone’ competition that avoids armed conflict, but that can segue to a mixed system course of action advisory. The general context is a costly ‘blue swan’ risk in large commercial and government enterprises. The method will focus on patterns and relationships in synthetic categories used to model type transitions within topological models of system influence. One may say this is applied intuitionistic type theory, following mechanisms generally described by synthetic differential geometry. In this context, the motivating supposition of this study is that information-carrying influence channels are best modelled in our challenging domain as perceived types rather than understood types.