Graphical Insights Research Articles

Conceptualizing the Antecedents and Individual Impact of Business Intelligence in the Public Sector: The Technology-Organisation-Authoritative Framework

Business intelligence (BI) refers to a technological tool and process that transforms large and fragmented data into well-informed graphical insights to aid managerial decision-making. Recognizing the significant benefits of BI in leveraging extensive amounts of data, governments have been progressively investing in BI tools to improve efficiency in public offices. Nevertheless, despite a significant body of knowledge related to BI adoptions and their advantages, there is a dearth of theoretical understanding of how the effective use of the system affects employees' work performance, particularly among civil servants who have distinctive work natures compared to businesses. Most studies have also ignored external and institutional factors that influence individual usage, whilst studies on the effective use of BI tools remain limited. The paper thus proposed a new conceptual framework for examining the factors that influence the effective use of BI and its impact on individual job performance. The suggested propositions could provide a theoretical contribution by integrating technological, organizational and authoritative dimensions that are novel and unique to the public sector. It would also contribute to practical understandings for public managers and policymakers in ensuring the investment made on BI is worthwhile. Ultimately, the paper seeks to bridge the gap in BI studies related to public organizations.

Decomposition and graphical correspondence analysis of checkerboard copulas

Abstract We analyze optimal low-rank approximations and correspondence analysis of the dependence structure given by arbitrary bivariate checkerboard copulas. Methodologically, we make use of the truncation of singular value decompositions of doubly stochastic matrices representing the copulas. The resulting (truncated) representations of the dependence structures are sparse, in particular, compared to the number of squares on the checkerboard. The additive structure of the decomposition carries through to statistical functionals of the copula, such as Kendall’s τ \tau or Spearman’s ρ \rho , and also motivates similarity measures for checkerboard copulas. We link our analysis to continuous decompositions of copula densities and copula-generating algorithms and discuss further general properties of the decomposition and its truncation. For example, truncated series might lack nonnegativity, and approximation errors increase for monotonicity-like copulas. We provide algorithms and extensions that account for and counteract these properties. The low-rank representation is illustrated for various copula examples, and some analytical results are derived. The resulting correspondence analysis profile plots are analyzed, providing graphical insights into the dependence structure implied by the copula. An illustration is provided with an empirical data set on fuel injector spray characteristics in jet engines.

Graphical Insight: Revolutionizing Seizure Detection with EEG Representation.

Epilepsy is characterized by recurring seizures that result from abnormal electrical activity in the brain. These seizures manifest as various symptoms including muscle contractions and loss of consciousness. The challenging task of detecting epileptic seizures involves classifying electroencephalography (EEG) signals into ictal (seizure) and interictal (non-seizure) classes. This classification is crucial because it distinguishes between the states of seizure and seizure-free periods in patients with epilepsy. Our study presents an innovative approach for detecting seizures and neurological diseases using EEG signals by leveraging graph neural networks. This method effectively addresses EEG data processing challenges. We construct a graph representation of EEG signals by extracting features such as frequency-based, statistical-based, and Daubechies wavelet transform features. This graph representation allows for potential differentiation between seizure and non-seizure signals through visual inspection of the extracted features. To enhance seizure detection accuracy, we employ two models: one combining a graph convolutional network (GCN) with long short-term memory (LSTM) and the other combining a GCN with balanced random forest (BRF). Our experimental results reveal that both models significantly improve seizure detection accuracy, surpassing previous methods. Despite simplifying our approach by reducing channels, our research reveals a consistent performance, showing a significant advancement in neurodegenerative disease detection. Our models accurately identify seizures in EEG signals, underscoring the potential of graph neural networks. The streamlined method not only maintains effectiveness with fewer channels but also offers a visually distinguishable approach for discerning seizure classes. This research opens avenues for EEG analysis, emphasizing the impact of graph representations in advancing our understanding of neurodegenerative diseases.

ADCGNet: Attention-based dual channel Gabor network towards efficient detection and classification of electrocardiogram images

Heart disease is a major health issue, and accurate diagnosis of irregular heartbeats and heart failure is crucial. Current diagnostic processes can be time-consuming, requiring significant effort from clinicians. An effective classifier, ADCGNet: Attention-based Dual Channel Gabor Network is proposed to address this challenge by accurately classifying anomalies. ADCGNet involves pre-processing every ECG beat into two-dimensional images using Analytical Morlet transform and then applying thirty-two Gabor filters and Sobel edge detection to enhance features. ADCGNet comprises three blocks, with the first block using dual channels to extract essential features in the images efficiently. The second block includes a multi-head attention mechanism to focus on relevant features, and the third block uses a SoftMax activation function to perform classification tasks. Extensive experiments with public datasets from PhysioNet, and comparison with several state-of-the-art classifiers indicate ADCGNet is superior. Specifically, ADCGNet achieved an accuracy of 99.17%, 98.98% in precision, a recall of 98.87%, an F1-score of 98.82% and AUC, 98.75% with optimal hyperparameters. Further, a GRAD-CAM visualization of activated areas on the test samples gives graphical insight into the performance of ADCGNet. The proposed ADCGNet classifier has promising potential for enhancing the diagnosis of heart disease, and we believe it will be of much interest to the medical community.

How Validation Methodology Influences Human Activity Recognition Mobile Systems

In this article, we introduce explainable methods to understand how Human Activity Recognition (HAR) mobile systems perform based on the chosen validation strategies. Our results introduce a new way to discover potential bias problems that overestimate the prediction accuracy of an algorithm because of the inappropriate choice of validation methodology. We show how the SHAP (Shapley additive explanations) framework, used in literature to explain the predictions of any machine learning model, presents itself as a tool that can provide graphical insights into how human activity recognition models achieve their results. Now it is possible to analyze which features are important to a HAR system in each validation methodology in a simplified way. We not only demonstrate that the validation procedure k-folds cross-validation (k-CV), used in most works to evaluate the expected error in a HAR system, can overestimate by about 13% the prediction accuracy in three public datasets but also choose a different feature set when compared with the universal model. Combining explainable methods with machine learning algorithms has the potential to help new researchers look inside the decisions of the machine learning algorithms, avoiding most times the overestimation of prediction accuracy, understanding relations between features, and finding bias before deploying the system in real-world scenarios.

Graphic modeling in Distributed Autonomous and Asynchronous Automata (DA3)

Automated verification of distributed systems becomes very important in distributed computing. The graphical insight into the system in the early and late stages of the project is essential. In the design phase, the visual input helps to articulate the collaborative distributed components clearly. The formal verification gives evidence of correctness or malfunction, but in the latter case, graphical simulation of counterexample helps for better understanding design errors. For these purposes, we invented Distributed Autonomous and Asynchronous Automata (DA3), which have the same semantics as the formal verification base—Integrated Model of Distributed Systems (IMDS). The IMDS model reflects the natural characteristics of distributed systems: unicasting, locality, autonomy, and asynchrony. Distributed automata have all of these features because they share the same semantics as IMDS. In formalism, the unified system definition has two views: the server view of the cooperating distributed nodes and the agent view of the migrating agents performing distributed computations. The automata have two formally equivalent forms that reflect two views: Server DA3 for observing servers exchanging messages, and Agent DA3 for tracking agents, which visit individual servers in their progress of distributed calculations. We present the DA3 formulation based on the IMDS formalism and their application to design and verify distributed systems in the Dedan environment. DA3 formalism is compared with other concepts of distributed automata known from the literature.

Gas diffusivity‐based characterization of aggregated soils linking to methane migration in shallow subsurface

AbstractMethane transport in soil is primarily affected by soil physical conditions such as soil texture and soil structure, soil moisture, soil‐gas diffusivity, permeability, and soil temperature. Aggregated soils have distinct soil structure with two pore regions characteristics (i.e., interaggregate and intraaggregate regions) and therefore show bimodal behavior with respect to soil physical properties controlling gas migration. This study characterized an aggregated soil retrieved near a natural gas (NG) extraction site at Denver–Julesburg (D‐J) basin in northeast Colorado (USA) with respect to soil‐water characteristic (SWC), pore‐size distribution, gas diffusivity and thermal conductivity. The investigated soil exhibited distinctive two‐region characteristics, which were adequately parameterized with extended, existing, and newly developed bimodal functions. We carried out an analysis with integrated model parameters to obtain a graphical insight on the correlation of properties. In addition, CH4 concentration profiles originated from a point source representing a buried pipeline leakage at three different flow rates (6, 12, and 24 L min–1) were simulated with a numerical tool that can simulate the multiphase flow of gas mixture under dry and different saturation conditions of the soil. Simulated results highlighted pronounced effects of soil moisture and, to a lesser degree, of gas leakage rate on subsurface CH4 concentrations profiles, suggesting diffusion‐dominated movement of CH4 in subsurface.

The Development Trajectory of Shared Epistemic Agency in Online Collaborative Learning: A Study Combing Network Analysis and Sequential Analysis

This study explored the development trajectory of shared epistemic agency in online collaborative learning through epistemic network analysis and lag sequential analysis. It was carried out in a postgraduate course with 14 in-service teachers. Drawing on the online discussion data from six sessions and the participants’ academic scores, this study found a nonlinear development trajectory of learners’ shared epistemic agency across the six sessions. The managerial dimension (e.g., regulative and relational actions) mediated the development of learners’ shared epistemic agency. The analysis of different groups’ mean networks and academic performance revealed a tentative relationship between them. Finally, the transition of shared epistemic agency actions in higher-achieving sessions and groups largely followed an upward sequential pattern. This study provides a graphical insight into how students learn in an online collaborative setting and can inform future pedagogical and technological designs of facilitating students’ shared epistemic agency for the creation of collective knowledge.

Identifying Patterns of Human and Bird Activities Using Bioacoustic Data

In general, humans and animals often interact within the same environment at the same time. Human activities may disturb or affect some bird activities. Therefore, it is important to monitor and study the relationships between human and animal activities. This paper proposed a system able not only to automatically classify human and bird activities using bioacoustic data, but also to automatically summarize patterns of events over time. To perform automatic summarization of acoustic events, a frequency–duration graph (FDG) framework was proposed to summarize the patterns of human and bird activities. This system first performs data pre-processing work on raw bioacoustic data and then applies a support vector machine (SVM) model and a multi-layer perceptron (MLP) model to classify human and bird chirping activities before using the FDG framework to summarize results. The SVM model achieved 98% accuracy on average and the MLP model achieved 98% accuracy on average across several day-long recordings. Three case studies with real data show that the FDG framework correctly determined the patterns of human and bird activities over time and provided both statistical and graphical insight into the relationships between these two events.

On a topological classification of recurrence plots: Application to noise perturbed molecular dynamics time series.

In the present paper, a topological classification of recurrence plots of time series that are constructed with equal embedding dimension and delay time is proposed by defining a metric structure in the set of those recurrence plots. To achieve this, the Recurrence Plot deviation distance and the Recurrence deviation plot are introduced along with qualitative and quantitative indices, which allows for a detailed comparison of the recurrence plots of two time series and their corresponding dynamical systems. In the range of values studied, the application of additive noise on the thermostat results in a less deterministic system behavior, quite close to that of the system with the unperturbed thermostat. On the other hand, multiplicative noise of small strength results in a less deterministic system quite close to the unperturbed system behavior. For a larger strength, the system presents a behavior which is significantly different from that of the unperturbed system. The new methodology introduced here provides detailed quantitative and graphical insights for the way that noise, additive and multiplicative, influences on the morphology of recurrence plots of the unperturbed and noise perturbed time series. The results are encouraging for potential future applications on the analysis of complex dynamical systems.

Understanding the pharmacokinetics of reversible metabolism.

This tutorial introduces the mathematical skills required to obtain exact and approximate solutions for reversible reactions and provides graphical insights to help understand the pharmacokinetics of reversible metabolism. The matrix method provides an easy way to derive the exact solution for the amount of each species as a function of time. The plots of the exact solutions reveal some characteristic features of the pharmacokinetic profiles of the reversible metabolism. We also describe two approximation approaches, steady-state approximation, and equilibrium approximation, to simplify the solutions. The skills and knowledge acquired through this tutorial will provide a basis for understanding more complex reversible reaction systems.

A hybrid method for synthesis of integrated water and regeneration networks with variable removal ratios

This work presents a systematic optimization framework that integrates graphical insights with mathematical modelling to reduce both model complexity and computational time. The graphical technique adopted in this work is Composite Table Algorithm (CTA), which is improved to determine an optimal regenerator removal ratio (RR) that simultaneously minimizes the freshwater requirement and wastewater generation within the water network. The improved CTA is demonstrated using literature examples for both fixed load and fixed flowrate problems. It is further adapted to solve a multiple contaminant problem using the reference contaminant approach. The mathematical model developed includes a detailed design of a reverse osmosis (RO) unit to allow for simultaneous optimization of water and energy used by the regenerator. This provides accurate cost estimation of the water network rather than the linear cost functions associated with the use of blackbox representations in graphical targeting. Upon integrating the graphical and mathematical techniques in this study, results show that there was a reduction of about 85% in CPU time. This implies that the model converges faster and therefore favours the use of insight-based techniques as a preprocessing step for mathematical modelling.

Towards systematic, data-driven validation of a collaborative, multi-scale model of Caenorhabditis elegans.

The OpenWorm Project is an international open-source collaboration to create a multi-scale model of the organism Caenorhabditis elegans At each scale, including subcellular, cellular, network and behaviour, this project employs one or more computational models that aim to recapitulate the corresponding biological system at that scale. This requires that the simulated behaviour of each model be compared with experimental data both as the model is continuously refined and as new experimental data become available. Here we report the use of SciUnit, a software framework for model validation, to attempt to achieve these goals. During project development, each model is continuously subjected to data-driven 'unit tests' that quantitatively summarize model-data agreement, identifying modelling progress and highlighting particular aspects of each model that fail to adequately reproduce known features of the biological organism and its components. This workflow is publicly visible via both GitHub and a web application and accepts community contributions to ensure that modelling goals are transparent and well-informed.This article is part of a discussion meeting issue 'Connectome to behaviour: modelling C. elegans at cellular resolution'.

Vehicle control synthesis using phase portraits of planar dynamics

ABSTRACTPhase portraits provide control system designers strong graphical insight into nonlinear system dynamics. These plots readily display vehicle stability properties and map equilibrium point locations and movement to changing parameters and system inputs. This paper extends the usage of phase portraits in vehicle dynamics to control synthesis by illustrating the relationship between the boundaries of stable vehicle operation and the state derivative isoclines in the yaw rate–sideslip phase plane. Closed-loop phase portraits demonstrate the potential for augmenting a vehicle's open-loop dynamics through steering and braking. The paper concludes by applying phase portrait analysis to an envelope control algorithm for yaw stability and a sliding surface controller for stabilising a saddle point equilibrium in drifting.

Framework and Solution Techniques for Suppressing Electric Machine Winding MMF Space Harmonics by Varying Slot Distribution and Coil Turns

This paper studies the design of electric machine windings with reduced magnetomotive force (MMF) space harmonic content by adjusting the slot distribution and coil turns. Closed-form expressions for the winding factor and harmonic magnitudes established from a mathematical representation of winding functions are used to formulate the MMF space harmonic suppression problem. The framework is applicable to both distributed windings and fractional-slot concentrated windings (FSCWs) and can be solved with different techniques. No special requirements on the slot/pole combination and the number of layers are necessary. One example uses a linear algebraic technique to solve simultaneous equations and the result is a mixed-layer distributed winding. The process also reveals the criterion when multiple requirements on the winding factor and harmonic magnitudes can be satisfied concurrently. Contour plots are included to provide additional graphical insight. Another example designs a 24-slot 22-pole FSCW subset with numerical optimization. The resultant winding is a four-layer FSCW with two subsets and 3/4 of the harmonic orders are eliminated. The performance improvement of this winding is verified with finite-element simulations.

Insights into doxycycline adsorption onto graphene nanosheet: a combined quantum mechanics, thermodynamics, and kinetic study.

Recently, pharmaceutically active compounds including antibiotics have been detected in drinking water at very low levels, mostly nanogram/liter concentrations, proposing that these materials were unaffected by water treatment processes. Adsorption processes were suggested to play a significant role in the removal of antibiotics. In this study, the adsorption behavior of doxycycline (DC) in aqueous solution was evaluated. The four factors influencing the adsorption of DC onto graphene nanosheet (GNS) were studied. The results showed that initial pH∼ 6 to 7 and contact time ∼ 200min are optimum. The monolayer adsorption capacity was reduced with the increasing temperature from 25 to 45°C. Nonlinear regressions were carried out to define the best fit model for every system. Among various models, the Hill isotherm model represented the equilibrium adsorption data of antibiotics while the kinetic data were well fitted by the Elovich kinetic model. The maximum adsorption capacity (q max) was 110mg.g-1, obtained from the Hill equation. Semiempirical molecular orbital theory was used to investigate the molecular interaction of the adsorption system. The experiments and semiempirical computation have systematically demonstrated that DC could be adsorbed onto GNS by π- π and electrostatic interactions. It was shown that there is a good compromise with the experimental results. Graphical abstract Insights into doxycycline adsorption onto graphene nanosheet: quantum mechanics, thermodynamics, and kinetic study.

Analyzing complex mathematical model behavior by partial least squares regression‐based multivariate metamodeling

The increasing complexity of mathematical models of complex systems like living cells has created a need for methods to reduce computational demand, maintain overview of the capabilities and feasibility of the models, compare alternative models, and obtain more reliable and effective fitting of models to experimental data. Metamodeling—statistical modeling of the behavior of complex mathematical models, also called ‘surrogate modeling’—is well established in many scientific disciplines, such as mechanical engineering and process simulation, and has recently also found use in computational biology, as well as other fields of bioscience. Many of these are based on partial least squares regression (PLSR) and various nonlinear and N‐way extensions of the PLSR. This is a versatile family of multivariate data modeling methods that combines a simple, flexible model structure (low‐rank bilinear subspace regression) and an intuitively attractive optimization criterion (maximized explained input–output covariance) to provide both predictive ability and graphical insight. This review summarizes the background for PLSR‐based metamodeling, and the use of PLSR and related methods in the main application areas of metamodeling: reduction of computational demand, sensitivity analysis, model comparison, and parameterization of models in relation to measured data. The methodology is generic, but here illustrated by examples from computational biology. The advantages and limitations of metamodeling for analyzing complex model behavior are discussed. WIREs Comput Stat 2014, 6:440–475. doi: 10.1002/wics.1325This article is categorized under: Applications of Computational Statistics > Computational and Molecular Biology Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods

Correction for 'Causal inference, probability theory, and graphical insights'

One should clarify that the numbers in Table 1 of Stuart Baker's article 1 contain unfortunate numerical errors. The numbers with Y = 0 and Y = 1 for the not treated (X = 0) and the treated (X = 1) under the heading ‘ U = ?’ should read as given in the succeeding text. Indeed, these numbers are just the sum of the respective cell frequencies n the U = 1 and U = 0 columns, and pr(Y = 1) equals 260 ∕ (260 + 140) = 0.65 for the not treated and pr(Y = 1) equals 180 ∕ (180 + 220) = 0.45 for he treated. The numbers given in the published paper result in pr(Y = 1) = 300 ∕ (300 + 100) = 0.75 for he not treated and pr(Y = 1) = 100 ∕ (100 + 300) = 0.25 for treated. Corrected part of Table 1:

DIFFERENTIAL FORMS AND ELECTROMAGNETIC FIELD THEORY (Invited Paper)

Mathematical frameworks for representing fields and waves and expressing Maxwell's equations of electromagnetism include vector calculus, differential forms, dyadics, bivectors, tensors, quaternions, and Clifford algebras. Vector notation is by far the most widely used, particularly in applications. Of the more sophisticated notations, differential forms stand out as being close enough to vectors that most practitioners can readily understand the notation, yet at the same time offering unique visualization tools and graphical insight into the behavior of fields and waves. We survey recent papers and book on differential forms and review the basic concepts, notation, graphical representations, and key applications of the differential forms notation to Maxwell's equations and electromagnetic field theory.

Comment on ‘Causal inference, probability theory, and graphical insights’ by Stuart G. Baker.

Forthcoming, Statistics in Medicine, Volume 32, Number 25, November 2013. TECHNICAL REPORT R-412 June 2013 Comment on “Causal inference, probability theory, and graphical insights” by Stuart G. Baker Judea Pearl University of California, Los Angeles Computer Science Department Los Angeles, CA, 90095-1596, USA judea@cs.ucla.edu Modern causal inference owes much of its progress to a strict and crisp distinction between probabilistic and causal information. This distinction recognizes that prob- ability theory is insufficient for posing causal questions, let alone answering them, and dictates that every exercise in causal inference must commence with some extra knowledge that cannot be expressed in probability alone. 1 The paper by Baker at- tempts to overturn this distinction and argues that “probability theory is a desirable and sufficient basis for many topics in causal inference.” My comments will disprove Baker’s claim, in the hope of convincing readers of the importance of keeping the boundaries between probabilistic and causal concepts crisp and visible. Baker’s argument begins with: “...besides explaining such causal graph topics as M -bias (adjusting for a collider) and bias amplification and attenuation (when adjusting for instrumental variable), probability theory is also the foundation of the paired availability design for historical control” (abstract). While I am not versed in the intricacies of “paired availability design” (Google Scholar lists only a handful of entries in this category), I doubt it can be based solely on probabilities. Indeed, Baker himself resorts to counterfactuals and other non-probabilistic notions 2 in explaining the research questions a “paired availability design” attempts to answer. I am quite familiar however with the concepts of “M -bias,” “bias,” “Simpson’s paradox,” and “instrumental variable” which I will show to have no interpretation in probability theory alone. I will start with the concept of “instrumental variable” which should be familiar to most readers, and which is often mistaken to have probabilistic definition (see [2, pp. Cartwright [1] summarized this limitation in a well-known slogan: “no causes in, no causes out.” By “non-probabilistic notions” I mean relations or parameters that cannot be defined in terms of joint distributions of observed variables. The restriction to observed variable is important for, otherwise, everything would become probabilistic, including Cinderella’s age, horoscopic predictions, counterfactuals, latent variables, the answers to our research questions, and so on; we need merely hypothesize a distribution over such variables and turn every problem probabilistic. The distinction between causal in probability information would then lose its meaning and usefulness.