Functional Modeling Framework Research Articles

Eco-evolutionary dynamics of structured populations in periodically fluctuating environments: a G function approach.

Understanding the ecological and evolutionary dynamics of populations is critical for both basic and applied purposes in a variety of biological contexts. Although several modeling frameworks have been developed to simulate eco-evolutionary dynamics, many fewer address how to model structured populations. In a prior paper, we put forth the first modeling approach to simulate eco-evolutionary dynamics in structured populations under the G function modeling framework. However, this approach does not allow for accurate simulation under fluctuating environmental conditions. To address this limitation, we draw on the study of periodic differential equations to propose a modified approach that uses a different definition of fitness more suitable for fluctuating environments. We illustrate this method with a simple toy model of life history trade-offs. The generality of this approach allows it to be used in a variety of biological contexts.

Investigating the Structural Composition of Contemporary Sustainable Organic Agriculture Supply Chains: A Case Study from Thailand

The global demand for organic produce has surged, with notable growth among Thai consumers. However, within Thailand's organic agricultural sectors, supply chain obscurity has impeded the understanding and resolution of emerging challenges associated with meeting this rising demand. Public interest has traditionally centered on mainstream economic sectors, resulting in inadequate attention to the sustainable organic farming supply chains. This study aims to explore the complexities of current sustainable organic supply chain configurations in Thailand, addressing gaps that lead to suboptimal planning and non-sustainable practices. Utilizing the Integration Definition for Function Modeling (IDEF0) framework, this research collected data from key stakeholders through in-depth interviews. Participants included representatives from each stage of the supply chain, providing comprehensive insights into the operational dynamics. The analysis identified several critical challenges within the supply chain, including insufficient collaborative planning, reduced productivity, inaccurate demand forecasting, ineffective budgetary planning, and inadequate cold chain management. These challenges highlight the fragmented nature of the current supply chain and the need for strategic improvements. To address these shortcomings, the study suggests integrating technological advancements in demand planning, operations, and budgeting, fostering collaboration among supply chain members, and forming strategic partnerships with cold chain management service providers. These recommendations aim to enhance the efficiency and sustainability of Thailand's organic produce supply chain, ensuring it can meet increasing global and domestic demands effectively.

Brain Tumor Segmentation Pipeline Model Using U-Net Based Foundation Model

Medical professionals often rely on Magnetic Resonance Imaging (MRI) to obtain non-invasive medical images. One important use of this technology is brain tumor segmentation, where algorithms are used to identify tumors in MRI scans of the brain. The foundation model Pipeline is based on U-Net Architecture to handle medical image segmentation and has been fine-tuned in the research paper to segment brain tumors. The model will be further trained on various medical images to segment images for various bio-medical purposes and used as part of the Generative AI functional model framework. Accurate segmentation of tumors is essential for treatment planning and monitoring, and this approach can potentially improve patient outcomes and quality of life

BAYESIAN INFERENCE AND DYNAMIC PREDICTION FOR MULTIVARIATE LONGITUDINAL AND SURVIVAL DATA.

Alzheimer's disease (AD) is a complex neurological disorder impairing multiple domains such as cognition and daily functions. To better understand the disease and its progression, many AD research studies collect multiple longitudinal outcomes that are strongly predictive of the onset of AD dementia. We propose a joint model based on a multivariate functional mixed model framework (referred to as MFMM-JM) that simultaneously models the multiple longitudinal outcomes and the time to dementia onset. We develop six functional forms to fully investigate the complex association between longitudinal outcomes and dementia onset. Moreover, we use the Bayesian methods for statistical inference and develop a dynamic prediction framework that provides accurate personalized predictions of disease progressions based on new subject-specific data. We apply the proposed MFMM-JM to two large ongoing AD studies: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and National Alzheimer's Coordinating Center (NACC), and identify the functional forms with the best predictive performance. our method is also validated by extensive simulation studies with five settings.

Integrating condition-based monitoring with process sensors information for operation and maintenance in a functional modeling framework

Safe operations and adequate maintenance are two main means to achieve reliable production and reduce downtime of a plant. While the tasks of operations and maintenance are carried out by two different groups of staff, as a result, the close relationship between the two tasks is split. In this paper, this challenge is handled by a proposed integrated functional modeling framework. In this framework, the Multilevel Flow Modeling (MFM) method with its cause-consequence reasoning rules is used. Condition-based monitoring is a well-accepted strategy for predictive maintenance and fault detection based on measurements is a well-developed technology for operation support. Information fusion including monitoring conditions of assets and process sensors information for both operation and maintenance in the same modeling framework is desired. The qualitative relationship distribution between operations and maintenance can be established based on the function states of the system. In addition, these relationships are visible for both groups of staff. As a result, the detected information in the early stage of the development of the unpleasant scenarios is used to improve their situation awareness, so that the undesired emergency shutdown from both perspectives of operation and maintenance is prevented. Consequently, it can reduce production loss. A case study of operations and maintenance of a seawater injection system is carried out and shows the industrial applicability of the proposed framework. The case study strongly reveals that there is a highly close relation between operation and maintenance for ensuring the system working properly. It demonstrates that the proposed integrated framework is not only able to support operational tasks but also for the maintenance tasks by including relevant maintenance information of the system. The results show that it can potentially help with decreasing downtime of the system.

Digital Function Modeling in Graph-Based Design Languages

The main focus of this paper is the integration of an integrated function modeling (IFM) framework in an engineering framework based on graph-based design languages (GBDLs). Over the last decade, GBDLs have received increasing attention as they offer a promising approach for addressing several important challenges in engineering, such as the frequent and time-consuming transfer of data between different computer aided engineering (CAE) tools. This absorbs significant amounts of manual labor in engineering design projects. GBDLs create digital system models at a meta level, encompassing all relevant information concerning a certain product design and feeding this into the relevant simulation tools needed for evaluating the impact of possible design variations on the performance of the resulting products/parts. It is possible to automate this process using digital compilers. Because of this, it is also possible to realize systematic design variations for a very large number of parameters and topological variants. Therefore, these kinds of graph-based languages are a powerful means for creating a large number of viable design alternatives and for permitting fast evaluation processes against the given specifications. While, thus far, such analyses tend to be based on a more or less fully defined system, this paper proposes an expansion of the applicability of GBDLs into the domain of product functions to cohesively link conceptual with embodiment design stages. This will also help with early systematic, automated generation and the validation of design alternatives through relevant simulation tools during embodiment design. Further, it will permit the automated exploration of function paths and enable extended analysis possibilities, such as the detection of functional bottlenecks, while enhancing the traceability of the design over the development process. For these extended analysis possibilities, a function analysis tool was developed that adopts core ideas of the failure mode and effects analysis (FMEA). In this, the functional distinction between function carriers and function-related processes allows the goal-directed assessment of component reliabilities and the detectability and importance of processes in a technical system. In the paper, the graph-based modeling of functions and the function analysis tools are demonstrated on the example of a multicopter.

ИНТЕГРИРОВАННЫЙ ПОДХОД К МОДЕЛИРОВАНИЮ ГРУЗОВЫХ ПОСТАВОК

The article discusses the models used to optimize freight deliveries based on tours and urban transportation logistics and an approach to integrating them. The study demonstrates a functional model framework that can address the limitations of existing freight demand forecasting models

The Use of Function Modelling Methods in Industry: Insights from a Large Scale Study with an Automotive OEM

AbstractThis paper presents an evaluation study for the System State Flow Diagram function modeling framework based on a large-scale study with an automotive OEM. Technical reports are used to evaluate the usage of the framework within the organization. The paper also introduces a list of the type of problems that engineers are interested in in practical function modelling. The findings suggest that there is a widespread uptake of the framework across the organization and it supports the usage of relevant key engineering tools within the context of a broader model-based Failure Mode Analysis methodology.

Uncovering Human Errors Associated With System-User Interactions Using Functional Modeling

Abstract Designers should adequately develop user considerations such as usability, safety, and comfort during the design process of new systems. Nevertheless, incorporating human factors engineering principles during early design phases is not simple. The objective of this work is to assist designers in implementing human factors engineering principles during early design phases using a functional model framework. This effort expands our previous work on automating the function-human error design method (FHEDM) implementation. In this work, we use data mining techniques in a design repository to explore the construction of association rules between components, functions, flows, and user interactions. Such association rules can support designers assessing user-system interactions during the early design stages. To validate this approach, we compare the associations generated by expert designers using the FHEDM while designing a new product to those generated by an algorithm using the repository data. The results show notable similarities between the associations extracted by the algorithm and the associations identified by designers. Thus, the overall results show that association rules extracted from a rich dataset can be used to distinguish user-product interactions, demonstrating the potential of automating the identification of user-product interactions from a functional model.

A fast wavelet-based functional association analysis replicates several susceptibility loci for birth weight in a Norwegian population

BackgroundBirth weight (BW) is one of the most widely studied anthropometric traits in humans because of its role in various adult-onset diseases. The number of loci associated with BW has increased dramatically since the advent of whole-genome screening approaches such as genome-wide association studies (GWASes) and meta-analyses of GWASes (GWAMAs). To further contribute to elucidating the genetic architecture of BW, we analyzed a genotyped Norwegian dataset with information on child’s BW (N=9,063) using a slightly modified version of a wavelet-based method by Shim and Stephens (2015) called WaveQTL.ResultsWaveQTL uses wavelet regression for regional testing and offers a more flexible functional modeling framework compared to conventional GWAS methods. To further improve WaveQTL, we added a novel feature termed “zooming strategy” to enhance the detection of associations in typically small regions. The modified WaveQTL replicated five out of the 133 loci previously identified by the largest GWAMA of BW to date by Warrington et al. (2019), even though our sample size was 26 times smaller than that study and 18 times smaller than the second largest GWAMA of BW by Horikoshi et al. (2016). In addition, the modified WaveQTL performed better in regions of high LD between SNPs.ConclusionsThis study is the first adaptation of the original WaveQTL method to the analysis of genome-wide genotypic data. Our results highlight the utility of the modified WaveQTL as a complementary tool for identifying loci that might escape detection by conventional genome-wide screening methods due to power issues. An attractive application of the modified WaveQTL would be to select traits from various public GWAS repositories to investigate whether they might benefit from a second analysis.

A framework for modeling the knowledge of workplace relocation

PurposeThe purpose of this study is to propose a framework model for workplace relocation.Design/methodology/approachPublished literature was reviewed to comprehend the broad aspects of workplace relocation. The current practices of workplace relocation were investigated by corporate real estate (CRE) professionals. A generic framework model was developed to guide organizations upon the process of workplace relocation, over its lifecycle. The framework is validated by semi-structured interviews, conducted to ascertain its importance and applicability.FindingsA validated integration definition for function modeling framework for the process of workplace relocation was developed. It consisted of three systematic processes, aligned with its lifecycle, namely, initiate the pre-relocation activities; implement the relocation activities; and conduct the post-relocation activities.Originality/valueThe workplace is a linkage between people, business processes and information technology, that provides a physical medium for interactions and provision of services. Workplace relocation is an opportunity to introduce elements of change. Yet the process of workplace relocation has been under-valued by practitioners in the CRE industry. The developed framework can be adopted as a standardized process for the relocation of workplace facilities. In terms of clearly structuring the workplace relocation processes, the study contributes to making these processes more transparent and aids accountability for decision-making. It is a time-saver and guiding model for organizations endeavoring on relocation.

Identifying and addressing latent causes of construction waste in infrastructure projects

Infrastructure projects often generate significant amounts of waste. However, research has focused so far mostly on the reactive treatment of waste, rather than its proactive prevention. Moreover, few previous studies have dealt with waste from infrastructure projects. This paper presents a method that can be used by project teams to identify both the causes of major types of waste in infrastructure projects, and the means to prevent its generation. The method is based on function modeling to support a detailed analysis of project activities that are critical in terms of their economic and environmental impact. The existing function modeling framework is expanded, to include a quantitative representation of material flows in the project’s supply chain. The proposed method was applied in a real project to verify its feasibility.

Partial least squares for functional joint models with applications to the Alzheimer's disease neuroimaging initiative study.

Many biomedical studies have identified important imaging biomarkers that are associated with both repeated clinical measures and a survival outcome. The functional joint model (FJM) framework, proposed by Li and Luo in 2017, investigates the association between repeated clinical measures and survival data, while adjusting for both high-dimensional images and low-dimensional covariates based on the functional principal component analysis (FPCA). In this paper, we propose a novel algorithm for the estimation of FJM based on the functional partial least squares (FPLS). Our numerical studies demonstrate that, compared to FPCA, the proposed FPLS algorithm can yield more accurate and robust estimation and prediction performance in many important scenarios. We apply the proposed FPLS algorithm to a neuroimaging study. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI)database.

Locally efficient estimation in generalized partially linear model with measurement error in nonlinear function

We investigate the errors in covariates issues in a generalized partially linear model. Different from the usual literature (Ma and Carroll in J Am Stat Assoc 101:1465–1474, 2006), we consider the case where the measurement error occurs to the covariate that enters the model nonparametrically, while the covariates precisely observed enter the model parametrically. To avoid the deconvolution type operations, which can suffer from very low convergence rate, we use the B-splines representation to approximate the nonparametric function and convert the problem into a parametric form for operational purpose. We then use a parametric working model to replace the distribution of the unobservable variable, and devise an estimating equation to estimate both the model parameters and the functional dependence of the response on the latent variable. The estimation procedure is devised under the functional model framework without assuming any distribution structure of the latent variable. We further derive theories on the large sample properties of our estimator. Numerical simulation studies are carried out to evaluate the finite sample performance, and the practical performance of the method is illustrated through a data example.

Quantifying prediction uncertainty for functional-and-scalar to functional autoregressive models under shape constraints

Motivated by demand and supply curve forecasting in energy markets, we discuss an autoregressive functional modeling framework that preserves curve constraints, includes exogenous scalar information, and provides prediction uncertainty quantification. The model is a functional autoregressive model that relies on a non-concurrent functional autoregressive model in a non-standard pre-Hilbert space in order to satisfy the curve constraints. Prediction uncertainty is quantified by means of a novel bootstrap approach for dependent functional data where the predictive bootstrap trajectories are represented alongside the prediction to show how forecasting confidence varies in the domain. Computational and numerical details are discussed in order to replicate the model estimation process an adequate number of times during the bootstrap phase. The method is applied to Italian natural gas market data.

Bayesian functional joint models for multivariate longitudinal and time-to-event data

A multivariate functional joint model framework is proposed which enables the repeatedly measured functional outcomes, scalar outcomes, and survival process to be modeled simultaneously while accounting for association among the multiple (functional and scalar) longitudinal and survival processes. This data structure is increasingly common across medical studies of neurodegenerative diseases and is exemplified by the motivating Alzheimer’s Disease Neuroimaging Initiative (ADNI) study, in which serial brain imaging, clinical and neuropsychological assessments are collected to measure the progression of Alzheimer’s disease (AD). The proposed functional joint model consists of a longitudinal function-on-scalar submodel, a regular longitudinal submodel, and a survival submodel which allows time-dependent functional and scalar covariates. A Bayesian approach is adopted for parameter estimation and a dynamic prediction framework is introduced for predicting the subjects’ future health outcomes and risk of AD conversion. The proposed model is evaluated by a simulation study and is applied to the motivating ADNI study.

The integrated function modeling framework and its relation to function structures

AbstractResearch and industrial practice have produced a host of function models and modeling approaches over the last decades. Each of these is meant to support designers in their design endeavors. Industrial practice is excessively diversified in terms of contextual requirements, aims, and adopted processes; this automatically begs the question which of the existing models should be selected for application in a specific situation. This paper sets out to contribute to this discourse. It strives to benchmark the fairly novel integrated function modeling (IFM) framework against the well-established function structures modeling approach. The paper comparatively investigates the respective capabilities of the approaches, following the benchmarking protocol used earlier in relation to this Special Issue. Function structures are used as reference as they represent one of the most widespread function modeling approaches in research and practice. Both function structures and the IFM framework are exemplarily applied for modeling a glue gun. The gradual generation and refinement of the models is used to showcase their respective benefits and shortcomings. Eventually, the IFM framework is found to excel over function structures in terms of comprehensiveness and support for different types of function analyses. Finally, future research directions are proposed.

Function modeling using the system state flow diagram

AbstractThis paper introduces a rigorous framework for function modeling of complex multidisciplinary systems based on the system state flow diagram (SSFD). The work addresses the need for a consistent methodology to support solution-neutral function-based system decomposition analysis, facilitating the design, modeling, and analysis of complex systems architectures. A rigorous basis for the SSFD is established by defining conventions for states and function definitions and a representation scheme, underpinned by a critical review of existing literature. A set of heuristics are introduced to support the function decomposition analysis and to facilitate the deployment of the methodology with strong practitioner guidelines. The SSFD heuristics extend the existing framework of Otto and Wood (2001) by introducing a conditional fork node heuristic, to facilitate analysis and aggregation of function models across multiple modes of operation of the system. The empirical validation of the SSFD function modeling framework is discussed in relation to its application to two case studies: a benchmark problem (glue gun) set for the engineering design community; and an industrial case study of an electric vehicle powertrain. Based on the evidence from the two case studies presented in the paper, a critical evaluation of the SSFD function modeling methodology is discussed based on the function benchmarking framework established by Summers et al. (2013), considering the representation, modeling, cognitive, and reasoning characteristics. The significance of this paper is that it establishes a rigorous reference framework for the SSFD function representation and a consistent methodology to guide the practitioner with its deployment, facilitating its impact to industrial practice.

Connectivity strength-weighted sparse group representation-based brain network construction for MCI classification.

Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l1 -norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a "connectivity strength-weighted sparse group constraint." In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting-state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method. Hum Brain Mapp 38:2370-2383, 2017. © 2017 Wiley Periodicals, Inc.

A longevity basis risk analysis in a joint FDM framework

PurposeThe improvements of longevity are intensifying the need for capital markets to be used to manage and transfer the risk through longevity-linked securities. Nevertheless, the difference between the reference population of the hedging instrument and the population of members of a pension plan, or the beneficiaries of an annuity portfolio, determines a significant heterogeneity causing the so-called basis risk. In particular, it is shown that if insurers use financial instruments based on national indices to hedge longevity risk, this hedge can become imperfect. For this reason, it is fundamental to arrange a model allowing to quantify the basis risk for minimising it through a correct calibration of the hedging instrument.Design/methodology/approachThe paper provides a framework for measuring the basis risk impact on the. To this aim, we propose a model that measures the population basis risk involved in a longevity hedge, in the functional data model setting. hedging strategies.FindingsThe innovative contribution of the paper occurs in two key points: the modelling of mortality and the hedging strategy. Regarding the first point, the paper proposes a functional demographic model framework (FDMF) for capturing the basis risk. The FDMF model generally designed for single population combines functional data analysis, nonparametric smoothing and robust statistics. It allows to capture the variability of the mortality trend, by separating out the effects of several orthogonal components. The novelty is to set the FDMF for modelling the mortality of the two populations, the hedging and the exposed one. Regarding the second point, the basic idea is to calibrate the hedging strategy determining a suitable mixture of q-forwards linked to mortality rates to maximise the degree of longevity risk reduction. This calibration is based on the key q-duration intended as a measure allowing to estimate the price sensitivity of the annuity portfolio to the changes in the underlying mortality curve.Originality/valueThe novelty lies in linking the shift in the mortality curve to the standard deviation of the historical mortality rates of the exposed population. This choice has been determined by the observation that the shock in a mortality rate is age dependent. The main advantage of the presented framework is its strong versatility, being the functional demographic setting a generalisation of the Lee-Carter model commonly used in mortality forecasting, it allows to adapt to different demographic scenarios. In the next developments, we set out to compare other common factor models to assess the most effective longevity hedge. Moreover, the parsimony for considering together two trajectories of the populations under consideration and the convergence of long-term forecast are important aspects of our approach.