Functional Modeling Approach Research Articles

Sensitivity of simulated light interception and tree transpiration to the level of detail of 3D tree reconstructions

The aim of this paper is to investigate the minimal level of detail of the tree geometry reconstruction required to enable an accurate estimate of the evaporative cooling effect of an individual tree.The Functional Structural Plant Modeling approach, which links the 3D tree structure to its functioning, is used to investigate the impact of the level of detail of a tree reconstruction on its simulated transpiration. Based on terrestrial laser scanning point cloud data of a nine-meter-high silver linden tree (Tilia tomentosa Moench), several methods of reconstruction of its crown were considered. They can be divided into three groups: (i) tree branching structure reconstructions where leafy shoots are reconstructed; (ii) envelope reconstructions such as 3D convex/concave envelopes; and (iii) voxel reconstructions where leaves are uniformly distributed within the given volume. Based on these methods, several mock-ups of resolved tree crowns from low to high level of details were created. The cooling performance of each tree mock-up was compared by simulating its transpiration rate with a validated 3D ecophysiological model based on the turbid medium approach.The resulting mock-ups differ in several characteristics related to tree metrics and light interception: volume, projected leaf area, leaf area index, leaf area density, leaf clumping and silhouette-to-total area ratio. An inter-comparison of the transpiration rates provided by these mock-ups shows that tree branching structure reconstruction methods perform better than envelope computation methods and that these differences are related to the light interception property of the reconstructed trees.This study provides guidelines to determine which structural characteristics of tree crowns must be measured and taken into account in order to carry out accurate estimates of the transpiration rates of individual trees.

Comparison of GSTARIMA and GSTARIMA-X Model by using Transfer Function Model Approach to Rice Price Data

GSTARIMA-X model is a development of the GSTARIMA model involving exogenous variables. The approach of transfer function model in the GSTARIMA-X model produces a dynamic space-time model. The influence of exogenous variables in a GSTARIMA model can assist in better estimation of response values. In this study, GSTARIMA model will be compared with GSTARIMAX model by using transfer function model approach. Both models are applied to data of medium rice price at the market level in six provinces of Java Island, Indonesia. The exogenous variables in the GSTARIMA-X model use data of dried grain price at the milling level at the same location. The best model of GSTARIMA was GSTARIMA(21,1, 1,0) and GSTARIMA-X with transfer function approach was GSTARIMA-X(0, 21,1, 0,0,0). The performance of comparison model showed that GSTARIMA-X model with a transfer function approach was better model than GSTARIMA model. The RMSEP and MAPE values of GSTARIMA-X model by using transfer function model approach for training and testing data had tendency of lower value compared to GSTARIMA model.

A state–behavior–function model for functional modeling of multi-state systems

Since functional modeling can be very useful in conceptual design, it has received considerable attention from engineering design community. However, existing functional models still cannot effectively assist designers in analyzing the functionalities of multi-state systems during conceptual design. As a result, designers often have to manually carry out functionality analysis according to their experiences, which are not only tedious, but also error-prone. Therefore, this paper proposes a state–behavior–function model for functional modeling of multi-state systems, which can provide designers with automated functional analysis support. The approach involves not only a state–behavior–function model for representing components, but also a model for representing the functionalities of multi-state systems. A prototype software is then developed for demonstrating the proposed model, with the functional modeling of a peeler centrifuge as an example to illustrate the proposed functional modeling approach.

Analyzing and modeling inter-sensor relationships for strain monitoring data and missing data imputation: a copula and functional data-analytic approach

Data analysis and modeling are very important in structural health monitoring. A good model is very conducive to better understanding, interpretation, estimation or prediction of structural performance, and dependence of variables. Strain responses are one of the most common measurements in structural health monitoring. Conventional modeling approaches in structural health monitoring for strain monitoring data have limitations in flexibly capturing the complex dependence structures of stochastic components between sensors. Also, addressing the nonlinear phase components is challenging when they are employed to model the inter-sensor relationships for temperature-induced strains. This article presents a refined inter-sensor modeling approach for strain monitoring data, which enables us to better understand the dependence of strain at different locations. Specifically, nonparametric copulas are employed to flexibly describe the dependence structures and the link marginal distributions to form the joint distribution to capture the inter-sensor relationship of stochastic strain responses. As opposed to traditional approaches in structural health monitoring, temperature-induced strains are treated as functional data; the phase and amplitude components are separately modeled by warping functions and a piecewise linear mapping model based on phase–amplitude separation technique; then, phase model and amplitude model are combined to form the final sensor-to-sensor mapping model. A similar functional modeling approach is also discussed for local strain responses resulting from wheel forces. Cooperated by a kernel regression model, applications of the established models in missing data imputation (i.e. replacing missing records by substitute values) are discussed for stochastic strain responses and temperature-induced strains using field monitoring data.

Functional Connectivity-Based Modelling Simulates Subject-Specific Network Spreading Effects of Focal Brain Stimulation

Neurostimulation remarkably alleviates the symptoms in a variety of brain disorders by modulating the brain-wide network. However, how brain-wide effects on the direct and indirect pathways evoked by focal neurostimulation elicit therapeutic effects in an individual patient is unknown. Understanding this remains crucial for advancing neural circuit-based guidance to optimize candidate patient screening, pre-surgical target selection, and post-surgical parameter tuning. To address this issue, we propose a functional brain connectome-based modeling approach that simulates the spreading effects of stimulating different brain regions and quantifies the rectification of abnormal network topology in silico. We validated these analyses by pinpointing nuclei in the basal ganglia circuits as top-ranked targets for 43 local patients with Parkinson’s disease and 90 patients from a public database. Individual connectome-based analysis demonstrated that the globus pallidus was the best choice for 21.1% and the subthalamic nucleus for 19.5% of patients. Down-regulation of functional connectivity (up to 12%) at these prioritized targets optimally maximized the therapeutic effects. Notably, the priority rank of the subthalamic nucleus significantly correlated with motor symptom severity (Unified Parkinson’s Disease Rating Scale III) in the local cohort. These findings underscore the potential of neural network modeling for advancing personalized brain stimulation therapy, and warrant future experimental investigation to validate its clinical utility.

Effective inpainting method for natural textures with gradually changed illumination

Natural textures with gradually changed illumination bring great troubles to the field of image inpainting. Using the improved dynamical modeling approach, an energy function model gradually changed from the center to the boundary of data missing region of image is established. On this basis, a gradually changed directional priority function is proposed to ensure the gradual propagation of texture synthesis. In addition, to achieve the feasible propagation of the boundary between different textural regions, the relative test point selection, as well as energy function modeling approach, is also discussed for different cases. Besides, the established energy function models are theoretically evaluated to demonstrate their accuracy in images. Experimental results show the effectiveness of the proposed approach in the natural images with gradually changed illumination.

Studying the interactions in a mammalian nerve fiber: a functional modeling approach.

Modern therapeutic interventions are increasingly favoring electrical stimulation to treat neurophysiological dis-orders. These therapies are associated with suboptimal efficacy since most neurostimulation devices operate in an open-loop manner $(i.e.$, stimulation settings like frequency, amplitude are preprogrammed). A closed-loop system can dynamically adjust stimulation parameters and may provide efficient therapies. Computational models used to design these systems vary in complexity which can adversely affect their real-time performance. In this study, we compare two models of varying degrees of complexity. We constructed two computational models of a myelinated nerve fiber (functional versus mechanistic) each receiving two inputs: the underlying physiological activity at one end of the fiber, and the external stimulus applied to the middle of the fiber. We then defined relay reliability as the percentage of physiological action potentials that make it to the other end of the nerve fiber. We applied the two inputs to the fiber at various frequencies and analyze reliability. We found that the functional model and the mechanistic model have similar reliability properties, but the functional model significantly decreases the computational complexity and simulation run time. This modeling effort is the first step towards understanding and designing closed loop, real-time neurostimulation devices.

Electrochemical Properties of Quinone Derivatives: First-Principles Density Functional Theory Modeling and Machine Learning Approach

Lithium-ion battery has received intensive attention as a candidate for efficient and portable electrochemical energy storage systems (EES) to be used in many applications ranging from cell phones to electric vehicles. Lithium-ion batteries not only have the high performance but also cyclic stability making them the optimal choice for recharging-discharging process. Despite the high potential, the slow diffusivity of lithium ions, which is mainly due to the intrinsic properties of the conventional cathode materials, which are mainly transition metal oxides, resulting in poor power density. Organic electrode materials have been considered as promising substitute for metallic oxide cathodes due to safety, low cost, and low density. Among various organic materials, the ones with the carbonyl functional group have been recognized to achieve high-performance redox couples with stable and reversible redox reactions. Finding cathode materials with high redox potentials is critical for organic molecules as they exhibit lower potential compared to the conventional electrode materials. The lower potential exhibited by the organic molecules compared to the metallic oxides are believed to be due to the low ionic conductivity of organic materials, despite having good electron conductivity. In this study, we characterize the electrochemical potentials of quinone-based structures in as a function of molecular structures. Quinone-based organic materials have well-defined active sites and fast electron and ionic transfer kinetics compared to other organic molecules, which is suitable for battery application. We also investigate what factors may contribute to the electrochemical properties using machine learning technique, especially artificial neural network.

Modeling water quality and hydrological variables using ARIMA: a case study of Johor River, Malaysia

Long-term trends in water quality and hydrological variables of natural systems reveal information about physical, chemical and biological changes and variations due to manmade and seasonal interventions. The objective of this study was to develop suitable stochastic models for predicting river water quality and hydrological variables through the establishment of dynamic relationship among the variables using transfer function modeling approaches. Autoregressive integrated moving average (ARIMA) model containing autoregressive (AR), integrated (I) and moving average (MA) was used for this purpose. The water quality variables, namely pH, color (TCU), turbidity (ppm), Al3+ (ppm), Fe2+ (ppm), NH4 + (ppm) and Mn2+ (ppm), and hydrological variables, namely rainfall and river discharge for Johor River, Malaysia, recorded for the period 2004–2007 were used in the study. Results showed that except Al3+, Fe2+, NH4 + and rainfall, all other variables are stationary. The non-stationary time series can be fitted with ARIMA (p, 1, q), while the stationary time series can be fitted with AR model with 1–5 time lags. The autocorrelations of all the samples were found within the 95% confidence bounds and the model residuals were found to follow normal probability distribution, which indicate the suitability of the models in forecasting water quality and hydrological variables. It is expected that the modeling approach developed in this paper can be replicated in other river basins for reliable prediction of river water quality due to changes in rainfall–runoff processes.

Three-part joint modeling methods for complex functional data mixed with zero-and-one-inflated proportions and zero-inflated continuous outcomes with skewness.

We take a functional data approach to longitudinal studies with complex bivariate outcomes. This work is motivated by data from a physical activity study that measured 2 responses over time in 5-minute intervals. One response is the proportion of time active in each interval, a continuous proportions with excess zeros and ones. The other response, energy expenditure rate in the interval, is a continuous variable with excess zeros and skewness. This outcome is complex because there are 3 possible activity patterns in each interval (inactive, partially active, and completely active), and those patterns, which are observed, induce both nonrandom and random associations between the responses. More specifically, the inactive pattern requires a zero value in both the proportion for active behavior and the energy expenditure rate; a partially active pattern means that the proportion of activity is strictly between zero and one and that the energy expenditure rate is greater than zero and likely to be moderate, and the completely active pattern means that the proportion of activity is exactly one, and the energy expenditure rate is greater than zero and likely to be higher. To address these challenges, we propose a 3-part functional data joint modeling approach. The first part is a continuation-ratio model to reorder the ordinal valued 3 activity patterns. The second part models the proportions when they are in interval (0,1). The last component specifies the skewed continuous energy expenditure rate with Box-Cox transformations when they are greater than zero. In this 3-part model, the regression structures are specified as smooth curves measured at various time points with random effects that have a correlation structure. The smoothed random curves for each variable are summarized using a few important principal components, and the association of the 3 longitudinal components is modeled through the association of the principal component scores. The difficulties in handling the ordinal and proportional variables are addressed using a quasi-likelihood type approximation. We develop an efficient algorithm to fit the model that also involves the selection of the number of principal components. The method is applied to physical activity data and is evaluated empirically by a simulation study.

Large‐scale movement behavior in a reintroduced predator population

Understanding movement behavior and identifying areas of landscape connectivity is critical for the conservation of many species. However, collecting fine‐scale movement data can be prohibitively time consuming and costly, especially for rare or endangered species, whereas existing data sets may provide the best available information on animal movement. Contemporary movement models may not be an option for modeling existing data due to low temporal resolution and large or unusual error structures, but inference can still be obtained using a functional movement modeling approach. We use a functional movement model to perform a population‐level analysis of telemetry data collected during the reintroduction of Canada lynx to Colorado. Little is known about southern lynx populations compared to those in Canada and Alaska, and inference is often limited to a few individuals due to their low densities. Our analysis of a population of Canada lynx fills significant gaps in the knowledge of Canada lynx behavior at the southern edge of its historical range. We analyzed functions of individual‐level movement paths, such as speed, residence time, and tortuosity, and identified a region of connectivity that extended north from the San Juan Mountains, along the continental divide, and terminated in Wyoming at the northern edge of the Southern Rocky Mountains. Individuals were able to traverse large distances across non‐boreal habitat, including exploratory movements to the Greater Yellowstone area and beyond. We found evidence for an effect of seasonality and breeding status on many of the movement quantities and documented a potential reintroduction effect. Our findings provide the first analysis of Canada lynx movement in Colorado and substantially augment the information available for conservation and management decisions. The functional movement framework can be extended to other species and demonstrates that information on movement behavior can be obtained using existing data sets.

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.

Comparing function structures and pruned function structures for market price prediction: An approach to benchmarking representation inferencing value

AbstractBenchmarking function modeling and representation approaches requires a direct comparison, including the inferencing support by the different approaches. To this end, this paper explores the value of a representation by comparing the ability of a representation to support reasoning based on varying amounts of information stored in the representational components of a function structure: vocabulary, grammar, and topology. This is done by classifying the previously developed functional pruning rules into vocabulary, grammatical, and topological classes and applying them to function structures available from an external design repository. The original and pruned function structures of electromechanical devices are then evaluated for how accurately market values can be predicted using the graph complexity connectivity method. The accuracy is found to be inversely related to the amount of information and level of detail. Applying the topological rule does not significantly impact the predictive power of the models, while applying the vocabulary rules and the grammar rules reduces the accuracy of the predictions. Finally, the least predictive model set is that which had all rules applied. In this manner, the value of a representation to predict or answer questions is quantified.

Function modeling combined with physics-based reasoning for assessing design options and supporting innovative ideation

AbstractFunctional modeling is an analytical approach to design problems that is widely taught in certain academic communities but not often used by practitioners. This approach can be applied in multiple ways to formalize the understanding of the systems, to support the synthesis of the design in the development of a new product, or to support the analysis and improvement of existing systems incrementally. The type of usage depends on the objectives that are targeted. The objectives can be categorized into two key groups: discovering a totally new solution, or improving an existing one. This article proposes to use the functional modeling approach to achieve three goals: to support the representation of physics-based reasoning, to use this physics-based reasoning to assess design options, and finally to support innovative ideation. The exemplification of the function-based approach is presented via a case study of a glue gun proposed for this Special Issue. A reverse engineering approach is applied, and the authors seek an incremental improvement of the solution. As the physics-based reasoning model presented in this article is heavily dependent on the quality of the functional model, the authors propose a general approach to limit the interpretability of the functional representations by mapping the functional vocabulary with elementary structural blocks derived from bond graph theory. The physics-based reasoning approach is supported by a mathematical framework that is summarized in the article. The physics-based reasoning model is used for discovering the limitations of solutions in the form of internal contradictions and guiding the design ideation effort.

A Comprehensive Image-based Phenomic Analysis Reveals the Complex Genetic Architecture of Shoot Growth Dynamics in Rice (Oryza sativa).

Early vigor is an important trait for many rice ( L.)-growing environments. However, genetic characterization and improvement for early vigor is hindered by the temporal nature of the trait and strong genotype × environment effects. We explored the genetic architecture of shoot growth dynamics during the early and active tillering stages by applying a functional modeling and genomewide association (GWAS) mapping approach on a diversity panel of ∼360 rice accessions. Multiple loci with small effects on shoot growth trajectory were identified, indicating a complex polygenic architecture. Natural variation for shoot growth dynamics was assessed in a subset of 31 accessions using RNA sequencing and hormone quantification. These analyses yielded a gibberellic acid (GA) catabolic gene, , which could influence GA levels to regulate vigor in the early tillering stage. Given the complex genetic architecture of shoot growth dynamics, the potential of genomic selection (GS) for improving early vigor was explored using all 36,901 single-nucleotide polymorphisms (SNPs) as well as several subsets of the most significant SNPs from GWAS. Shoot growth trajectories could be predicted with reasonable accuracy using the 50 most significant SNPs from GWAS (0.37-0.53); however, the accuracy of prediction was improved by including more markers, which indicates that GS may be an effective strategy for improving shoot growth dynamics during the vegetative growth stage. This study provides insights into the complex genetic architecture and molecular mechanisms underlying early shoot growth dynamics and provides a foundation for improving this complex trait in rice.

Estimation of Peak Factor of Non-Gaussian Wind Pressures by Improved Moment-Based Hermite Model

AbstractThe moment-based Hermite polynomial function model approach is often used to estimate the extreme value distribution and peak factor of a non-Gaussian process through those of the underlyin...

An approach to support entries for a design failure modes and effects analysis

The design of a fixture device is very laborious, requiring consideration of many issues from series production, information on the raw part material, required precision surfaces, ensuring an appropriate orientation scheme, continue with requirements related device itself as number of parts, parts type, complexity, manageability, easy operation and maintenance. That is why realising a proper analyse regarding the possible failures associated to these issues is overwhelming. This paper’s aim is to propose an approach for collection of items necessary for starting a Design FMEA, using Functional Analysis, TRIZ functional modelling and axiomatic design approaches. The proposed approach – to extract items for a FMEA analyse – is applied to an example of an expanding mandrel regarding its internal components and theirs relations in order to accomplish the main requirement: the accurate locating and clamping of a part.

Development of space time model with exogenous variable by using transfer function model approach on the rice price data

Development of space time model with exogenous variable by using transfer function model approach on the rice price data

A product functional modelling approach based on the energy flow by using characteristics-properties modelling

ABSTRACTThe main objective in designing a product is to satisfy the required needs. So, the designer aims to design the product’s structure based on the required function of the product. Therefore, in the design process, the link between the functional requirement and the physical structure has to be assured. This paper proposes an approach to help the designer in transition from function to structure to assure this link. This approach uses energy flow modelling to model the system’s behaviour in the framework of characteristics-properties modelling (CPM). It is composed of a multi-level modelling approach with two concurrent, interlinked modelling domains: functional and structural. By zigzagging between these two domains in each level of the decomposition, a quantitative model of the product is determined using CPM. The result shows the effective modifiable parameters (or required structure) of the product to satisfy the required function. So, it can be used to design a new product as well as analysing and optimising an existing product.

A framework for complex product architecture analysis using an integrated approach

Contemporary design decomposition and synthesis analytical tasks at the conceptual design stage rely on functional and structural modelling approaches. There is a wide diversity of elements used by various modelling approaches for information and representation of product architecture, which incurs difficulties for multidisciplinary engineers working across different phases of design in capturing, visualising, sharing and tracing consistent yet common knowledge and elements across the function and structure domains. This prompts for fixation of detail and common modelling knowledge across both functional and structural analytical approaches which are also critical from automatised software perspective. A limitation of the existing approaches is that they tend to focus more on ‘what’ and less on ‘how’ (and vice versa). This article proposes an integrated conceptual product architecting approach that combines and expands the functional and structural modelling approaches, enabling capturing and tracing knowledge coherently through a common binding domain. This is underpinned by the view that most interaction requirements among the physical components during structural modelling can be derived from functional modelling. The proposed integrated approach is underpinned by the critical analysis and synthesis of the existing approaches in the literature dealing with functional and structural architecture analyses, integrated within a multiple domain matrix to fuse the knowledge of both solution-independent (functional) and solution-dependent (structural) analyses. The proposed framework is illustrated with a case study of solar robot toy, followed by discussion and suggestions for future work.