Graphical Approach Research Articles

Observability analysis and state reconstruction for networks of nonlinear systems

We address the problem of retrieving the full state of a network of Rössler systems from the knowledge of the actual state of a limited set of nodes. The selection of nodes where sensors are placed is carried out in a hierarchical way through a procedure based on graphical and symbolic observability approaches applied to pairs of coupled dynamical systems. By using a map directly obtained from governing equations, we design a nonlinear network reconstructor that is able to unfold the state of non-measured nodes with working accuracy. For sparse networks, the number of sensor scales with half the network size and node reconstruction errors are lower in networks with heterogeneous degree distributions. The method performs well even in the presence of parameter mismatch and non-coherent dynamics and for dynamical systems with completely different algebraic structures like the Hindmarsch–Rose; therefore, we expect it to be useful for designing robust network control laws.

Mixed convection MHD boundary layer flow, heat, and mass transfer past an exponential stretching sheet in porous medium with temperature-dependent fluid properties

The mixed convection MHD boundary layer flow, heat and mass transfer of a Newtonian fluid mixture across an exponentially stretched permeable sheet in porous medium with Soret and Dufour effects subject to temperature-dependent fluid properties have been investigated numerically. The distribution of temperature and concentration at the surface is supposed to grow exponentially. Temperature is considered to affect fluid viscosity and thermal conductivity. The guiding partial differential equations with appropriate boundary conditions are converted into coupled, nonlinear ordinary differential equations with variable coefficients via a similarity transformation. Matlab's built in solver bvp4c is used to find numerical solutions. Using a graphical approach, the effects of several emerging physical parameters on velocity, temperature and concentration distributions on a flow field of a chemically nonreacting fluid mixture are examined and discussed. Results are compared to earlier literature available and found to be in good agreement. Furthermore, this research demonstrates that flow, heat and mass transmission are greatly influenced by mixed convection parameters, magnetic field, Prandtl number, viscosity, conductivity, permeability, and Schmidt number. Skin friction grows for mixed convection parameter, magnetic, viscosity. Highest growths of wall temperature and wall concentration gradients are observed for Prandtl number and Schmidt number, respectively. HIGHLIGHTS Heat and mass transfer rates increase with higher values of mixed convection parameters and Darcy parameter. To reduce heat transfer rate magnetic field may be used. Fluid with lower thermal conductivity and with higher Prandtl number may be employed to boost the cooling rate in conducting flows. Higher Schmidt enhances mass transfer rates. Suction stabilizes boundary layer growth. Soret effect can be used to separate light and medium molecular weight components from a fluid mixture and hence can be used for control of air pollution.

ANISE: an application to design mechanobiology simulations of planar epithelia.

TiFoSi (Tissues Forces & Signaling) is an efficient computational tool for performing mechanobiology simulations of planar epithelia. A drawback of this tool is that it relies on an XML configuration file (input data) that can be cumbersome to set up and/or decode due to the endless possibilities of the software. Moreover, some modeling know-how is needed in order to provide equations that describe gene regulatory interactions. These factors limit the usability of this tool for users with a weak computational and/or mathematical background. Here, we introduce ANISE (grAphical coNfigurator of TiFoSi In Silico Experiments), a web-app that allows to easily setup the configuration of mechanobiology simulations using TiFoSi. The application covers all the configuration modules in TiFoSi comprehensively (from basic to advanced editing options) and uses a graphical approach (e.g. to build the modeling equations of gene regulatory networks). http://github.com/lsym-uveg/anise (server: http://lsymserver.uv.es/lsym/ANISE).

Exploring Relationships between Months and Different Crash Types on Mountainous Freeways Using a Combined Modeling Approach

Investigating the relationship between the months and traffic crashes is a foremost task for the safety improvement of mountainous freeways. Taking a mountainous freeway located in China as an example, this paper proposed a combined modeling framework to identify the relationships between months and different crash types. K-means and Apriori were initially used to extract the monthly distribution patterns of different types of crashes. A graphical approach and a risk calculation equation were developed to assess the output of K-means and Apriori. Then, using the assessment results as the input, a logistic regression model was constructed to quantify the effects of each month on crashes. The results indicate that the monthly distribution patterns of different crash types are inconsistent, i.e., for a specific month, the high risk of a certain crash type may be covered up if experts only focus on the total number of crashes. Moreover, when identified as high-risk months by K-means and Apriori, the crash-proneness will significantly increase several times than months identified as high-risk by only one of K-means and Apriori, thereby illustrating the superior performance of the mix-method. The conclusions can assist local relevant organizations in formulating strategies for preventing different types of traffic crashes in different months (e.g., the risk of rear-end crashes in August, the risk of fixed-object hitting crashes in February, and the risk of overturning crashes in October) and provide a methodological reference for relevant studies in other regions.

Comparing Two Samples Through Stochastic Dominance: A Graphical Approach

Nondeterministic measurements are common in real-world scenarios: the performance of a stochastic optimization algorithm or the total reward of a reinforcement learning agent in a chaotic environment are just two examples in which unpredictable outcomes are common. These measures can be modeled as random variables and compared among each other via their expected values or more sophisticated tools such as null hypothesis statistical tests. In this article, we propose an alternative framework to visually compare two samples according to their estimated cumulative distribution functions. First, we introduce a dominance measure for two random variables that quantifies the proportion in which the cumulative distribution function of one of the random variables stochastically dominates the other one. Then, we present a graphical method that decomposes in quantiles (i) the proposed dominance measure and (ii) the probability that one of the random variables takes lower values than the other. With illustrative purposes, we reevaluate the experimentation of an already published work with the proposed methodology and we show that additional conclusions—missed by the rest of the methods—can be inferred. Additionally, the software package RVCompare was created as a convenient way of applying and experimenting with the proposed framework.

Family-wise type I error rate control for an extended 2-in-1 design with graphical approach in oncology drug development

Nowadays, in oncology drug development, when an experimental treatment shows a promising anti-tumor effect in Phase I efficacy expansion, a Phase III pivotal trial may be launched directly. To mitigate the risk of skipping the traditional randomized Phase II proof of concept (POC) study, the 2-in-1 design was proposed by Chen et al. (2018). This design has gained great research and application interest since its publication and been extended in many ways. The original 2-in-1 design controls family-wise type I error rate (FWER) for one hypothesis in Phase II part and one hypothesis in Phase III part. However, in practice, for a stand-alone Phase III study usually there are multiple hypotheses with group sequential interim analyses and the multiplicity is controlled by the graphical approach. It is desirable that these features of the Phase III design are retained when 2-in-1 design is considered. The multiplicity control for a 2-in-1 design with multiple hypotheses in Phase III has been addressed mainly by the Bonferroni approach in the literature. For the more powerful graphical approach, while Jin and Zhang (2021) discussed the FWER control for a special 2-in-1 design, in which Phase II and Phase III have exactly the same hypotheses, the FWER control for a more common 2-in-1 design (i.e., one hypothesis in Phase II and multiple hypotheses in Phase III) is yet investigated. This paper provides the analytical conditions under which FWER is controlled with the graphical approach in such a 2-in-1 design. It also provides the numeric explorations of FWER control for such design with group sequential interim analyses in Phase III, as a direct Phase III design normally would have. As a result, our work helps lower the hurdle of the application of the 2-in-1 design and pave the way for its wider application.

Rationale for the update algorithm of the graphical approach to sequentially rejective multiple test procedures.

The graphical approach by Bretz et al. is a convenient tool to construct, visualize and perform multiple test procedures that are tailored to structured families of hypotheses while controlling the familywise error rate. A critical step is to update the transition weights following a pre-specified algorithm. In their original publication, however, the authors did not provide a detailed rationale for the update formula. This paper closes the gap and provides three alternative arguments for the update of the transition weights of the graphical approach. It is a legacy of the first author, based on an unpublished technical report from 2014, and after his untimely death reconstructed by the other two authors as a tribute to Willi Maurer's collaboration with Andy Grieve and contributions to biostatistics over many years.

Examining export advantages in Indian horticulture: an approach based on product mapping and seasonality

PurposeThe present study provides evidence on export advantages of horticultural commodities based on competitiveness, trade balance and seasonality dimensions.Design/methodology/approachThe study delineated horticultural commodities in terms of comparative advantage, examined temporal shifts in export advantages (mapping) and estimated seasonality. Product mapping was carried out using the Revealed Symmetric Comparative Advantage (RSCA) and Trade Balance Index (TBI). Seasonal advantages were examined through a graphical approach along with the objective tests, namely, modified QS-test (QS), Friedman-test (FT) and using a seasonal dummy.FindingsCucumbers/gherkins, onions, preserved vegetables, fresh grapes, shelled cashew nuts, guavas, mangoes, and spices emerged as the most favorable horticultural products. India has a strong seasonal advantage in dried onions, cucumber/gherkins, shelled cashew nut, dried capsicum, coriander, cumin, and turmeric. The untapped potential in horticulture can be addressed by handling the trade barriers effectively, particularly the sanitary and phytosanitary issues, affecting the exports. Proper policies must be enacted to facilitate the investment in advanced agricultural technologies and logistics to ensure the desired quality and cost effectiveness.Research limitations/implicationsCommodity-specific studies on value chain analysis would provide valuable insights into the issues hindering exports and realizing the untapped export potential.Originality/valueThere is no holistic and recent study illustrating the horticulture export advantages covering a large number of commodities in the Indian context. The study would be helpful to the stakeholders for drawing useful policy implications.

Multi-attribute decision-making based on aggregations and similarity measures of neutrosophic hypersoft sets with possibility setting

ABSTRACT Possibility neutrosophic hypersoft set is the generalisation of possibility neutrosophic soft set, and it is the blend of possibility neutrosophic set and hypersoft set. It tackles the insufficiencies of possibility intuitionistic fuzzy set and soft set for the entitlement of degree of indeterminacy and multi-argument approximate function, respectively. This kind of function maps the sub-parametric tuples to the power set of universe. It emphasises the partitioning of each attribute into its attribute-valued set that is missing in the existing fuzzy soft set-like structures. These features make it a completely new mathematical tool for solving problems dealing with uncertainties. This makes the decision-making process more flexible and reliable. This research aims to initiate a novel structure that hybridises the possibility neutrosophic set with the hypersoft set for dealing with uncertainties efficiently. Therefore, the possibility neutrosophic hypersoft set is developed by employing theoretic, axiomatic, graphical and algorithmic approaches. After conceptual characterisation of its essential elementary notions, decision-support systems are presented based on AND and OR operations with the proposal of algorithms to assist the decision-making process. Similarity measures between possibility neutrosophic hypersoft sets are elaborated with the support of daily life applications. The comparison of the proposed work is discussed with the existing relevant models under certain evaluating features.

Semantic Model‐based Systems Engineering based on KARMA: A Research and Practice Roadmap 2025

AbstractModel‐based Systems Engineering is proposed as a graphical approach to support for‐malization of system artifacts across system lifecycle based on models since 1993. The previous motivation of graphical specification is to provide a unified graphical description on the perspective of systems engineering in order to formalize system architectural views, to promote communications among stakeholders and to support system analysis and verification. However, when different modeling tools are developed based on such graphical specifications, model and data interoperability across modeling tools is the biggest challenge faced by the tool venders and MBSE practitioners. Thus, semantic specification is proposed again to enhance data interoperability, such as SysML 2.0 as a possible solution. In this paper, we propose a new semantic MBSE language and framework aiming to support complex system development using a semantic two‐core mechanism: 1) KARMA language and 2) Industrial Ontologies Foundry (IOF) SE and MBSE ontology. Then, we introduce the KARMA Roadmap 2025 including technical vision, organizational views and standardization. A new KARMA open‐source modeling environment is planned to being created in order to provide MBSE application for more industrial practitioners.

COMPARISON OF PREDICTIVE MODELS IN DATA MINING AND IMPACTS OF AIR POLLUTION IN METROPOLITAN CITIES

In the past decades, as a result of the enormous environmental load, the air quality in the city has worsened in India. The remarkable increase in vehicle population and industries has led to the concentration of air pollutants in major metropolitan cities. The behavior of the air pollutants has a severs mash on human health and environment. To safeguard human health, the health risks posed by the increasing rate of air pollution on a wide scale must be projected and predicted. Monitoring and forecasting the vast amounts of data generated from numerous monitoring stations across the city has been a topic of debate. This led to the scientist to look for several predicting data mining techniques and big data analytics to monitor and predict the urban air quality. Data mining combines statistical, machine learning, and graphical approaches to extract information into a format that can be used in a variety of real-world applications. This study applies data mining to uncover the hidden knowledge of air pollution distribution in the voluminous data retrieved from monitoring stations National Air Monitoring Program (NAMP) station , National Ambient Air Quality (NAAQ) standards, Central Pollution Control Board. This article covers several ways to predicting urban air quality using data mining techniques such as linear regression, back propagation, and big data analytics such as Map reduction and Geostatistical algorithms

Impedance matching via ultrathin metatronic layer assisted by Smith Chart.

Metatronics, as a feasible paradigm of nanocircuits using effective electronic elements (e.g., nanocapacitors, nanoinductors, and nanoresistors), provides the possibility for light manipulation in subwavelength scales assisted by the circuit-related technologies in electronics. As a representative technique in electronics, Smith Chart provides a fast, less-computation and graphical approach to solve the problems related to impedance matching. Here, we transplant the Smith Chart into the paradigm of optical metatronics to develop an analytical approach for impedance matching for light propagation and coined the name of graphical metatronics. In this approach, the impedance characteristics of four basic types of ultrathin metatronic layers are creatively mapped into each rotation trace on the complex Γ mathematical plane (Γ means the reflection coefficient). The impedance matching problems can be graphically solved by searching for feasible rotation traces on the Γ plane without full-wave simulations. Based on this approach, various applications related to impedance matching (e.g., antireflection coating, perfect transmission, absorber, etc.) are developed analytically and validated by numerical results. The proposed approach constructs the bridge among Smith Chart, plasmonics and photonics, providing a fast, visualized and less-computation route and guideline to develop various nanophotonic structures and devices for impedance-matching applications.

Application of the permutation method to the assessment of predictive ability of the models of spatial distribution of copper and iron concentrations in the topsoil

The article proposes the use of the permutation method for assessment of the predictive ability of models based on artificial neural networks. To test this method, three models based on artificial neural networks were implemented: a multilayer perceptron, a radial basis function network, and a generalized regression neural network. For modeling, data on the spatial distribution of copper and iron in the topsoil (depth 0.05 m) on the territory of the subarctic city of Noyabrsk, Yamalo-Nenets Autonomous Okrug, Russia, were used. A total of 237 soil samples were collected. For modelling, the copper and iron concentration data were divided into two subsets: training and test. The modelled spatial datasets were compared with the observed values of the test subset. To assess the performance of the constructed models, three approaches were used: 1) calculation of correlation coefficients, error or agreement indexes, 2) graphical approach (Taylor diagram), 3) randomization assessment of the probability of obtaining a divergence between the observed and modelled datasets, assuming that both of these datasets taken from the same population. For the randomization algorithm, two statistics were used: difference in means and correlation coefficient. The permutation method proved its productivity, as it allowed to assess the significance of the divergence between the observed and predicted datasets.

Spin-density calculation via the graphical unitary group approach

In this work we discuss the calculation of the spin-density matrix from fundamental spin principles as implemented in the COLUMBUS Program System employing the graphical unitary group approach (GUGA). First, a general equation for the spin-density matrix is derived in terms of the one-and two-particle reduced density matrices, quantities that are spin-independent and readily available within the GUGA formalism. Next, the evaluation of this equation using the Shavitt loop values is discussed. Finally, the spatially resolved counterpart of the spin-density matrix, the spin distribution, is calculated for the phenalenyl radical and structures produced by heteroatoms with mono- and di-substitutions. The physical meaning of the spin-density along with its computational description using various methods is discussed putting special emphasis on negative contributions to the spin-density and their quantification via a spin-promotion index.

Graphical approaches for cleaner production and sustainability in process systems

This work provides an overview of recent graphical approaches which can provide beneficial support for various applications enhancing cleaner production and, consequently, sustainability. Graphical tools, mostly based on a physical inside, play an important role in the decision-making process in engineering and can also be in cleaner production and contribute to sustainability. They have the advantage of illustrating the process by providing clear insight. This paper reviews the graphical approaches to show the developing timeline and trend of some tool series and the method of implementing these tools for specific issues. The review focuses on the energy, water, material, pollution and greenhouse gas emission reduction and also the safety issues that may affect human health and the normal production process. A total of 48 visual tools, especially some classic and widely used diagrams, are illustrated, and their characteristics are analysed. The future direction and potential of graphical tools to include the consideration of flexibility and reliability in cleaner production to reduce energy/water/material consumption and emissions in a longer period and to avoid harm to human health and living conditions have been assessed. The development of efficient concepts and visualisations for Circular Economy implementations is also an important area to be tackled.

Revisiting undrained cavity expansion problem in critical state soils: A simple graph‐based approach

AbstractThis paper presents analytical solutions for the finite expansion problems of a spherical or cylindrical cavity, using a simple yet novel graphical approach recently proposed by Chen & Abousleiman in 2022, in both original Cam Clay (OCC) and modified Cam Clay (MCC) soils under undrained conditions. It is shown that, for a soil mass subjected to isotropic in situ stress conditions, the stress paths in the deviatoric plane for the spherical and cylindrical cavity expansions turn out to be two straight lines, which correspond to Lode angles equal to and , respectively. The desired limiting cavity pressure therefore can be directly and accurately evaluated through simple numerical integration with respect to the mean effective stress, while the relationship between the internal cavity pressure and the cavity radius, the cavity expansion curve, may be equally conveniently determined. Numerical results obtained from the current graphical method, for a range of the values of over consolidation ratio considered, compare extremely well with those from the conventional semianalytical formulations of the undrained cavity problem that involve solving a system of coupled governing differential equations. It is interesting to note that the representative and approximate solution developed by Collins & Yu in 1996 indeed is a correct one for the spherical cavity expansion problem, and, with minor modifications, will be applicable for the accurate calculation of the responses of the cylindrical cavity as well.

Use of the Weibull model on sizing thickeners—Part II: Methods of thickener sizing

AbstractAmong several methods employed for sizing thickeners available in the literature, the Kynch, Biscaia Jr., Talmadge and Fitch, Roberts, Coe and Clevenger, and Oltmann methods use experimental data from sedimentation curves and graphical approaches. By using the Weibull distribution, it is possible to represent sedimentation curves with algebraic equations, which does not require the use of graphical approaches and provides more accuracy and speed for sizing calculations. In the present work, the main objective is the development of a set of equations for sizing continuous thickeners, for six conventional methods found in the literature, using the Weibull model. A comparative analysis of calculated and literature diameters for each graphical method presented variations between 0.73% and 8.93%. The use of the Weibull model presented the best accuracy for the Biscaia Jr. method, with a 0.73% average absolute error.

Comparing the Soil Conservation Service model with new machine learning algorithms for predicting cumulative infiltration in semi-arid regions

Water infiltration into soil is an important process in hydrologic cycle; however, its measurement is difficult, time-consuming and costly. Empirical and physical models have been developed to predict cumulative infiltration (CI), but are often inaccurate. In this study, several novel standalone machine learning algorithms (M5Prime (M5P), decision stump (DS), and sequential minimal optimization (SMO)) and hybrid algorithms based on additive regression (AR) ( i.e ., AR-M5P, AR-DS, and AR-SMO) and weighted instance handler wrapper (WIHW) ( i.e ., WIHW-M5P, WIHW-DS, and WIHW-SMO) were developed for CI prediction. The Soil Conservation Service (SCS) model developed by the United States Department of Agriculture (USDA), one of the most popular empirical models to predict CI, was considered as a benchmark. Overall, 154 measurements of CI (explanatory/input variables) were taken from 16 sites in a semi-arid region of Iran (Illam and Lorestan provinces). Six input variable combinations were considered based on Pearson correlations between candidate model inputs (time of measuring and soil bulk density, moisture content, and sand, clay, and silt percentages) and CI. The dataset was divided into two subgroups at random: 70% of the data were used for model building (training dataset) and the remaining 30% were used for model validation (testing dataset). The various models were evaluated using different graphical approaches (bar charts, scatter plots, violin plots, and Taylor diagrams) and quantitative measures (root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and percent bias (PBIAS)). Time of measuring had the highest correlation with CI in the study area. The best input combinations were different for different algorithms. The results showed that all hybrid algorithms enhanced the CI prediction accuracy compared to the standalone models. The AR-M5P model provided the most accurate CI predictions (RMSE = 0.75 cm, MAE = 0.59 cm, NSE = 0.98), while the SCS model had the lowest performance (RMSE = 4.77 cm, MAE = 2.64 cm, NSE = 0.23). The differences in RMSE between the best model (AR-M5P) and the second-best (WIHW-M5P) and worst (SCS) were 40% and 84%, respectively.

Editorial Policy

Editorial Policy

A new graphical approach to assess CPTU-based correlations between hydraulic conductivity and soil behavior type

A new graphical approach to assess CPTU-based correlations between hydraulic conductivity and soil behavior type