Concurrent Model Research Articles

Functional Concurrent Regression Mixture Models Using Spiked Ewens-Pitman Attraction Priors.

Functional concurrent, or varying-coefficient, regression models are a form of functional data analysis methods in which functional covariates and outcomes are collected concurrently. Two active areas of research for this class of models are identifying influential functional covariates and clustering their relations across observations. In various applications, researchers have applied and developed methods to address these objectives separately. However, no approach currently performs both tasks simultaneously. In this paper, we propose a fully Bayesian functional concurrent regression mixture model that simultaneously performs functional variable selection and clustering for subject-specific trajectories. Our approach introduces a novel spiked Ewens-Pitman attraction prior that identifies and clusters subjects' trajectories marginally for each functional covariate while using similarities in subjects' auxiliary covariate patterns to inform clustering allocation. Using simulated data, we evaluate the clustering, variable selection, and parameter estimation performance of our approach and compare its performance with alternative spiked processes. We then apply our method to functional data collected in a novel, smartphone-based smoking cessation intervention study to investigate individual-level dynamic relations between smoking behaviors and potential risk factors.

Empowering model repair: a rule-based approach to graph repair without side effects—extended version

AbstractWorking with models can lead to inconsistencies, e.g., due to erroneous or contradictory actions during concurrent modeling processes. Modern modeling environments typically tolerate inconsistencies and support their detection. However, at a later stage of development, models are expected to be consistent, meaning their inconsistencies should be considered and resolved. The process of resolving model inconsistencies is commonly referred to as model repair. Our approach to model repair is semi-automatic in the sense that the repair tool computes appropriate repair plans and the modeler decides which path to take. The speciality of our approach is that the repair process can register any small improvement in the model. This allows the interaction with the user to be optimized, resulting in an approach with a high level of automation on the one hand and flexible configuration options on the other. The approach focuses on providing repair plans that do not have side effects, i.e., the computed repair plans do not inadvertently introduce a new inconsistency of already repaired constraints into the model. Since models often have a graph-like structure, we present our approach to model repair based on graphs. Our approach is completely formal—we use the algebraic graph transformation approach to prove its correctness. We also present a prototype implementation of our repair approach based on the Eclipse Modeling Framework and Henshin, a model transformation engine based on graph transformation, to perform the actual model repair. A first performance evaluation shows that graphs with up to 1000 nodes can be repaired in about 10 s.

Implications Of The Implementation Of Clinical Authority On Improving The Quality Of Nursing Services

Literature studies indicate that implementing clinical authority can enhance the quality of nursing care. However, many hospitals still have not fully applied clinical authority aligned with nurses' career paths. This research explores the impact of clinical authority on nurses' performance, both directly and indirectly, through situational leadership in Hospital X. Using a mixed-method approach with a parallel concurrent triangulation model, the study collected quantitative data via questionnaires from 300 nurses using total sampling. Descriptive crosstab analysis revealed no significant differences in perceptions of clinical authority, performance, and situational leadership based on individual characteristics. Structural Equation Modeling showed that clinical authority positively and significantly affects nurse performance. Qualitative data were gathered through focus group discussions with 13 nurses and semi-structured interviews with senior nursing managers. Thematic analysis identified six key themes: positive impacts on performance, the role of situational leadership styles, strategies to optimize nursing services, encountered obstacles, competitive challenges, and motivation to enhance care. The qualitative findings indicated that clinical authority fosters nurses' responsibility, adherence to standards, mutual trust, teamwork, and improved communication and caring attitudes. The corroboration of quantitative and qualitative analyses demonstrated alignment, confirming that clinical authority significantly influences nurse performance. To improve nursing care quality, hospitals are encouraged to implement structured career paths for nurses, ensuring that clinical authority is supported at every level

A Reversible Perspective on Petri Nets and Event Structures

Event structures have emerged as a foundational model for concurrent computation, explaining computational processes by outlining the events and the relationships that dictate their execution. They play a pivotal role in the study of key aspects of concurrent computation models, such as causality and independence, and have found applications across a broad range of languages and models, spanning realms like persistence, probabilities, and quantum computing. Recently, event structures have been extended to address reversibility, where computational processes can undo previous computations. In this context, reversible event structures provide abstract representations of processes capable of both forward and backward steps in a computation. Since their introduction, event structures have played a crucial role in bridging operational models, traditionally exemplified by Petri nets and process calculi, with denotational ones, i.e., algebraic domains. In this context, we revisit the standard connection between Petri nets and event structures under the lenses of reversibility. Specifically, we introduce a subset of contextual Petri nets, dubbed reversible causal nets , that precisely correspond to reversible prime event structures. The distinctive feature of reversible causal nets lies in deriving causality from inhibitor arcs, departing from the conventional dependence on the overlap between the postset and preset of transitions. In this way, we are able to operationally explain the full model of reversible prime event structures.

Intelligent prediction for remaining life of aero-engine --A Study on CNN-BiLSTM Model Based on Scores Loss Function

With the advent of the era of big data and artificial intelligence, predicting and managing the remaining service life of aero-engines has become a key challenge, which has a direct impact on the enhancement of autonomous control capabilities. In this paper, a new deep learning model combining a one-dimensional convolutional neural network (1D-CNN) and a bidirectional long and short-term memory network (BiLSTM) is proposed to efficiently handle dynamic and uncertain engine health state data. The concurrent model takes full advantage of 1D-CNN in extracting local features and BiLSTM in capturing time-dependence, avoiding the loss in the information transfer process in conventional models. Empirical studies on NASA's C-MAPSS dataset show that the model significantly improves the accuracy and robustness in predicting the remaining service life, reducing the RMSE by up to 5.05% and the Scores by up to 54.89% compared to the conventional model. Especially in the short-term prediction task, the model shows higher stability and accuracy, which provides strong support for advance warning in the field of aviation safety.

Comparing inflationary models in extended Metric-Affine theories of gravity

We study slow-roll inflation as a common feature in Metric-affine Theories of Gravity. In particular, we take into account extended metric, teleparallel and symmetric-teleparallel theories of gravity, based on different geometric invariants, discussing analogies and differences. The analysis for each model is performed in two approaches. First, we focus on the potential-slow-roll approach by studying the reconstructed potentials for different forms of the extended models related to the considered gravitational theory in the Einstein frame. Secondly, we investigate the Hubble-slow-roll approach for some conventional inflationary potentials related to the specific extended model in the Jordan frame. We compare all results with cosmic microwave background anisotropy observations coming from Planck 2018 and BICEP2/Keck array satellites in order to find the observational constraints on the parameters space of the models as well as their prediction from the spectral parameters. Eventually, we attempt to present a qualitative comparison between three classes of considered modified gravities using the obtained inflationary results. The aim is to select, in principle, cosmological signatures capable of discriminating among concurrent models in view to point out the representation of gravity, according with geometric invariants, which better addresses the early universe dynamics.

An ANN-based concurrent multiscale damage evolution model for hierarchical fiber-reinforced composites

In this paper, an ANN-based concurrent multiscale damage evolution model is proposed, which is able to investigate the complex failure behaviors of hierarchical fiber-reinforced composites. In the framework of the proposed model, yarn damage evolution laws at the mesoscale are indirectly derived from the microscale representative volume element (RVE), using artificial neural networks (ANNs) as a surrogate model. A homogenized characterization method is proposed to derive the homogenized damage variables. The homogenized strain and damage variables of the microscale RVE are taken as inputs and outputs in ANNs, respectively. The dataset is generated by combining clustering with the finite element simulation. A typical kind of plain-woven composite is adopted as a benchmark material for numerical implementation and experimental verification. The numerical predictions, including the tensile properties and damage evolution, are consistent with the results from quasi-static tension experiments.

CoMPers: A configurable conflict management framework for personalized collaborative modeling

Context:Modeling is an activity in the software development life cycle where experts and stakeholders collaborate as a team. In collaborative modeling, adhering to the optimistic versioning paradigm allows users to make concurrent changes to the same model, but conflicts may arise. To achieve an integrated and consistent merged model, conflicts must be resolved. Objective:The primary objective of this study was to provide a customizable and extensible framework for conflict management in personalized change propagation during collaborative modeling. Methods:We propose CoMPers, a customizable and extensible conflict management framework designed to address various conflicts encountered in collaborative modeling. We present the duel algorithm for automatically detecting and resolving conflicts according to user preferences. The framework utilizes personalized change propagation to customize collaboration and supports the conflict management process by executing the duel algorithm based on user preferences. As a proof-of-concept, we have implemented the CoMPers framework and extended the EMF.cloud modeling framework to demonstrate its applicability. Results:We have constructed a proof-of-concept implementation and conducted a real-world case study, a benchmark experiment, and a user experience evaluation. Our findings demonstrate that: (1) CoMPers enables collaborators to configure propagation strategies according to their habits; (2) CoMPers successfully identifies all anticipated conflicts and achieves a 100% accuracy in conflict handling; (3) The majority of participants agreed that CoMPers is user-friendly for collaborative modeling. Conclusion:This paper presents the CoMPers framework, which is based on personalized change propagation, and helps collaborators customize conflict management activities. The results confirm the feasibility and advantages of consistent and concurrent modeling within the collaborative CoMPers platform, with an acceptable functionality for approximately ten collaborators.

Disentangling Parallelism and Interference in Game Semantics

Game semantics is a denotational semantics presenting compositionally the computational behaviour of various kinds of effectful programs. One of its celebrated achievement is to have obtained full abstraction results for programming languages with a variety of computational effects, in a single framework. This is known as the semantic cube or Abramsky's cube, which for sequential deterministic programs establishes a correspondence between certain conditions on strategies (''innocence'', ''well-bracketing'', ''visibility'') and the absence of matching computational effects. Outside of the sequential deterministic realm, there are still a wealth of game semantics-based full abstraction results; but they no longer fit in a unified canvas. In particular, Ghica and Murawski's fully abstract model for shared state concurrency (IA) does not have a matching notion of pure parallel program-we say that parallelism and interference (i.e. state plus semaphores) are entangled. In this paper we construct a causal version of Ghica and Murawski's model, also fully abstract for IA. We provide compositional conditions parallel innocence and sequentiality, respectively banning interference and parallelism, and leading to four full abstraction results. To our knowledge, this is the first extension of Abramsky's semantic cube programme beyond the sequential deterministic world.

Proving Correctness of Parallel Implementations of Transition System Models

This article addresses the long-standing problem of program correctness for programs that describe systems of parallel executing processes. We propose a new method for proving correctness of parallel implementations of high-level models expressed as transition systems. The implementation language underlying the method is based on the concurrency model of actors and active objects. The method defines program correctness in terms of a simulation relation between the transition system that specifies the program semantics of the parallel program and the transition system that is described by the correctness specification. The simulation relation itself abstracts from the fine-grained interleaving of parallel processes by exploiting a global confluence property of the concurrency model of the implementation language considered in this article. As a proof of concept, we apply our method to the correctness of a parallel simulator of multicore memory systems.

Lagged effects of childhood depressive symptoms on adult epigenetic aging.

Cross-sectional studies have identified health risks associated with epigenetic aging. However, it is unclear whether these risks make epigenetic clocks 'tick faster' (i.e. accelerate biological aging). The current study examines concurrent and lagged within-person changes of a variety of health risks associated with epigenetic aging. Individuals from the Great Smoky Mountains Study were followed from age 9 to 35 years. DNA methylation profiles were assessed from blood, at multiple timepoints (i.e. waves) for each individual. Health risks were psychiatric, lifestyle, and adversity factors. Concurrent (N = 539 individuals; 1029 assessments) and lagged (N = 380 individuals; 760 assessments) analyses were used to determine the link between health risks and epigenetic aging. Concurrent models showed that BMI (r = 0.15, PFDR < 0.01) was significantly correlated to epigenetic aging at the subject-level but not wave-level. Lagged models demonstrated that depressive symptoms (b = 1.67 months per symptom, PFDR = 0.02) in adolescence accelerated epigenetic aging in adulthood, also when models were fully adjusted for BMI, smoking, and cannabis and alcohol use. Within-persons, changes in health risks were unaccompanied by concurrent changes in epigenetic aging, suggesting that it is unlikely for risks to immediately 'accelerate' epigenetic aging. However, time lagged analyses indicated that depressive symptoms in childhood/adolescence predicted epigenetic aging in adulthood. Together, findings suggest that age-related biological embedding of depressive symptoms is not instant but provides prognostic opportunities. Repeated measurements and longer follow-up times are needed to examine stable and dynamic contributions of childhood experiences to epigenetic aging across the lifespan.

Exploring medical and dental practitioner perspectives and developing a knowledge attitude and practice (KAP) evaluation tool for the common risk factor approach in managing non-communicable and periodontal diseases

BackgroundThe Common Risk Factor Approach (CRFA) is one of the methods to achieve medical-dental integration. CRFA addresses shared risk factors among major Non-communicable Diseases (NCDs). This study aimed to explore the perspectives of dental and medical practitioners concerning CRFA for managing NCDs and periodontal diseases and to create and validate a tool to evaluate the Knowledge, Attitude, and Practice (KAP) of medical and dental practitioners in relation to utilization of CRFA for management of NCDs and Periodontal diseases.MethodsThis research employed a concurrent mixed-method model and was carried out from January 2021 to February 2022, focusing on medical and dental practitioners in South India. In the qualitative phase, online interviews were conducted with dental and medical practitioners, recorded, and transcribed. Thematic analysis was applied after achieving data saturation. In the quantitative phase, a KAP questionnaire was developed. The sample size was determined by using the G power statistical power analysis program. A sample size of 220 in each group (dentists and medical practitioners) was estimated. Systematic random sampling was used to recruit the potential participants. The data obtained through the online dissemination of KAP tool was analysed and scores were standardized to categorize the KAP.ResultsQualitative thematic analysis identified four major themes: understanding of common risk factors, risk factor reduction and disease burden, integrating CRFA into clinical practice, and barriers to CRFA. In addition, thematic analysis revealed seventeen subthemes. For the quantitative phase, standardization was applied to a 14-item KAP questionnaire for medical practitioners and a 19-item KAP questionnaire for dental practitioners. The total KAP score for medical practitioners in the study was 21.84 ± 2.87, while dental practitioners scored 22.82 ± 3.21, which indicated a high level of KAP regarding CRFA. Meta integration of qualitative and quantitative data identified eight overarching themes: four were concordant, three were discordant, and one theme provided the explanatory component.ConclusionThe study’s structured, validated questionnaire showed that both medical and dental professionals had a high knowledge of CRFA. However, they were not appreciably aware of the risk factors that are shared between NCDs and periodontal disease. Both groups were interested in the idea of using CRFA in integrated medical and dental care.

Investigation of dynamic responses of skin simulant against fragment impact through experiments and concurrent computational modeling.

Perforation of the skin by fragment impact is a key determinant of the severity of an injury and incapacitation during modern asymmetric warfare. Computational models validated against experimental data are thus desired for simulating the responses of a skin simulant against fragment impact. Toward this end, experiments and concurrent computational modeling were used to investigate the dynamic responses of the skin simulant against fragment impact. Fragment simulating projectiles (FSPs) of masses 1.10 g and 2.79 g were considered herein, and the responses of the skin simulant were investigated in terms of the threshold velocity, energy density, peak displacement, and failure mechanisms. The results illustrate numerous salient aspects. The skin simulant failure involved cavity shearing followed by elastic hole enlargement, and these results were sensitive to the strain rate. The best agreement between the simulated and experimental results was achieved when the input stress-strain curves to the simulation were based on the full spectrum of strain rates. When a single stress-strain curve corresponding to a specific strain rate was used as the input, the threshold velocity and peak displacement of the skin simulant were either underpredicted or overpredicted depending on the strain rate considered. The threshold velocity was also sensitive to the input failure strain; here, the best agreement was obtained when the failure strain was based on the theoretical limiting strain. When the FSP materials were changed to plastics, the threshold velocities increased by up to 33%; however, the energy densities and generated stresses exceeded the contusion and laceration thresholds of the skin.

Multitasking optimization for the imaging problem in electrical capacitance tomography

Electrical capacitance tomography excels in measuring multiphase flows, but it faces challenges in reconstructing high-accuracy images. To tap into the potential of this measurement technique, we model the image reconstruction problem as a bilevel multitasking optimization problem, aiming to enhance imaging quality via effective knowledge transfer and integration across various tasks. This new imaging model consists of a target optimization problem and an auxiliary optimization problem, facilitating knowledge transfer and fusion between tasks. We design a new algorithm to solve the target optimization problem and integrate it with an autoencoder used for the cross-task knowledge transfer, forming a new multitasking optimizer to solve this proposed bilevel multitasking optimization imaging model. The extreme learning machine is developed for the prediction of learnable prior images by introducing a novel bilevel optimization framework that enables concurrent parameter selection and model training. A novel nested algorithm, designed to efficiently address optimization problems structured hierarchically, is developed to solve the training model. Evaluation results show that the new imaging algorithm outperforms popular imaging methods in terms of reconstruction accuracy and robustness, while also demonstrating stable performance. This proposed imaging algorithm achieves cross-task knowledge transfer and fusion, integrates measurement physics and machine learning, mines and integrates effective image priors, adaptively learns model parameters, alleviates the ill-posed property of the image reconstruction problem, increase the automation of the model, and improves imaging quality, robustness and performance stability, which provides a comprehensive solution to mitigate challenges in imaging.

An efficient multi-scale method for failure mechanism analysis of SiCf/Ti composites with experimental validation

This study focuses on developing and validating a high-precision concurrent multi-scale finite element model considering the progressive damage model (PDM) and the cohesive zone model (CZM) for predicting the damage evolution of SiCf/Ti composites. At the macro-scale, a constitutive model describing the anisotropic damage criterion of SiCf/Ti composites was implemented by means of a user-defined subroutine (VUMAT). Meanwhile, based on the submodelling, a representative volume element (RVE) model was developed to systematically analyze the failure modes of the SiC fibers, the matrix, and the interface, respectively. Specifically, the crack initiation and propagation in the stress concentration zones were further discussed. Simultaneously, experimental methods were employed to verify the feasibility of the current model. During the uniaxial compression, the brittle fracture of the SiC fibers and ductile fracture of the matrix are the predominant failure modes of SiCf/Ti composites, with fiber fracture being the dominant factor. The crack initiates at the fibers, propagates rapidly to the fiber-matrix interface, and then extends into the matrix. Moreover, the fracture locations are suscepticle to stress concentrations, leading to the crack initiation and propagation. The predicted results show that the local damage has a significant effect on the failure mechanism of SiCf/Ti composites and this multi-scale model provides a scientific reference for the design and optimization of composites.

Abstract Tu147: Serial measurement of circulating cardiovascular enriched miR-1 and -34a reflects the changes in cardiac function in patients with ischemic heart disease – a five year follow-up study

Background: Patients with ischemic heart disease (IHD) require frequent monitoring, as the transition from stable disease to acute life-threatening events remains largely unpredictable. Currently available tests are either not sensitive enough to detect early changes or not easily incorporated into routine clinical practice. MicroRNAs (miRNAs), small non-coding RNAs involved in both physiological and pathological processes, are released into circulation and remain stable. Research Question: In this study, we aimed to determine if the serial measurement of cardiovascular-enriched circulating miRNAs could reflect changes in cardiac function in patients with IHD. Methods: Fifty-four new IHD patients joined a 5-year study, providing informed consent for regular echocardiography, blood tests and follow-ups at 12-month intervals. Plasma RNA was extracted and RT-PCR analysis used to assess expression of cardiovascular-enriched miR-1, miR-126, miR-132, and miR-34a. Results: Serial echocardiography analysis showed the development of diastolic dysfunction in the participants starting from 48 months (E/e’-10.7±4.3 vs 9.7±3.6 at baseline, P&lt;0.04). RT-PCR analysis revealed significant downregulation of miR-1 and miR-126 starting from 24 months of follow-up (P&lt;0.03 vs. baseline). Associations between each miRNA and each cardiac function measure were examined using linear mixed models without and with adjustment for changes in BMI and HbA1c during the same period. None of the lagged models found statistically significant associations (all p ≥ 0.126). From the concurrent models, two miRNA were associated with cardiac function: miR-1 and MV E Vel in the unadjusted (p = 0.045) and adjusted models (slope = -0.004, 95% CI -0.007 to -0.000, p = 0.026) models and miR-34a and FS(%) in the adjusted model only (slope = -0.151, 95% CI -0.301 to -0.001, p = 0.048). Conclusion: Results from this first-ever five-year follow-up study have identified a link between cardiovascular enriched miRNAs, miR-1 and -34a and cardiac function in patients with IHD, providing a knowledge platform for prognostic tests for IHD and laying the foundation for further studies in larger population size.

A national evaluation of Project Cautioning And Relationship Abuse ('CARA') awareness raising workshops for first time offenders of domestic violence and abuse: protocol for a concurrent mixed-methods evaluation design.

Interventions related to the perpetration of Domestic Violence and Abuse (DVA) have gained traction over the past several years, in response to dissatisfaction by victims, an inadequate response from the criminal justice system, increased demand on police time and a lack of rehabilitative responses to the perpetration of domestic abuse. The CARA model is a conditional diversionary caution, offered by police for first time offenders of 'standard' or 'medium risk' domestic abuse, that engages perpetrators in awareness raising workshops and signposts them onto further services. Although quasi-experimental studies have indicated that CARA showed promise at reducing reoffending, the CARA model has yet to be evaluated nationally and there is no qualitative evidence related to understanding or learning about the lived experience of perpetrators and victims as they engage with the intervention. Using a concurrent pragmatic mixed methods design model we will undertake a national evaluation of CARA by triangulating quantitative data from up to nine police forces, and routine data from service providers, with qualitative data from workshop participants, victims and professional stakeholders to: (1) understand the long-term impact of CARA implementation on DVA reoffending and engagement with services and (2) explore perceptions and experiences of both delivery and receipt of CARA. We will use qualitative methodologies that draw on interpretivist and phenomenological perspectives, as well as quantitative methodologies using interrupted time series models, Poisson regression models, Geo mapping and a cost benefits analysis. Where currently the CARA model is being introduced as a national option for standard risk first-time offending, we will engage with policymakers and academics nationally in the live debate on its effectiveness and suitability during its roll-out. Ethical approval was approved by the University of Southampton on the 1 st June 2022 (Ref: ERGO ID: 71818.A1).

CRITICAL THINKING SKILLS OF GRADE 5 STUDENTS ON SCIENCE LESSONS IN THE INDEPENDENT CURRICULUM

This research is motivated by the fact that in the 21st century, the learning process is no longer centered on educators (teacher-centered learning) but is centered on students (student-centered learning), learning consists of skills known as "The 4C Skills". Indonesia's low ranking in the PISA results designed by the OECD in 2018, was ranked 71st in the science category. Students' critical thinking abilities are needed in the learning process, especially in science learning in the independent curriculum, but each student has different essential thinking abilities. In the independent curriculum, the learning process is to meet the needs of the students, both in terms of planning learning and conditioning the classroom atmosphere and giving freedom to students to be active in learning. This research aims to determine the critical thinking abilities of grade 5 students in science lessons in the independent curriculum at SDI Al Azhar 27. The research method uses a combination research method with a concurrent triangulation design model. The instruments used in this research were tests, observations, and interviews. The results of the research show that students' critical thinking abilities before using the independent curriculum and students' critical thinking abilities after using the independent curriculum have increased. Quantitative analysis using the Paired Sample T-Test using SPSS 26 for Windows shows that the average value ranges from 85.00 to 90.90. Thus, it can be concluded that there is a significant increase in students' critical thinking abilities.

Predicting fatigue life with the IMWCW under complex loading

The Integration Modified Wöhler Curve Method (IMWCW), a novel approach for predicting fatigue life under complex strain-based non-proportional loading paths is introduced in this research. This method integrates concepts from the Modified Wöhler Curve Method (MWCW) with the principle of damage incremental integration to provide a more nuanced prediction of material fatigue. Besides two established E-N curves (strain-number of cycles) for uniaxial tension and torsion, a new fundamental E-N curve that characterizes the strain-life relationship under complete non-proportional loading paths is introduced. A novel strain path decomposition technique is presented, which dissects an infinite number of instantaneous micro-elements along any loading path into components parallel and perpendicular to the proportional loading direction. It is assumed that each instant will cause fatigue damage to the material. A concurrent model for the incremental integration of damage is formulated, allowing for the calculation of cumulative damage. The model is further expanded to provide analytical expressions for proportional loading and non-proportional loading, linking them to three fundamental E-N curve, so as to calibrate the parameters required for the integration model. The empirical validity of the model is confirmed through experimental testing on 7 different metallic materials under 20 loading conditions, with 391 data points analyzed. The results demonstrate that 91% of the model's predictions fall within a scatter band of 2, confirming its robustness and reliability in forecasting fatigue life in complex, real-world scenarios.

FFT-based homogenisation for efficient concurrent multiscale modelling of thin plate structures

AbstractThis paper presents a novel approach to concurrent multiscale analysis, where structures are formulated at both microscopic and macro levels for simulation purposes. The proposed method employs a plate model to formulate structures at both scales, and homogenisation is performed using the FFT-based approach, offering higher efficiency compared to conventional methods. Additionally, the macroscopic tangent operator of the microscopic model is derived through an algorithmically consistent process within the FFT-based framework, incorporating the application of Lippman–Schwinger equations as outlined in this work. The effectiveness of the proposed method is demonstrated through case studies in real simulations, revealing comparable results to traditional multiscale schemes in addressing multiscale thin plate structures. Importantly, the method significantly reduces computing time and memory usage, attributed to the efficiency of plate modelling and the FFT-based homogenisation strategy.