Graphical Approach Research Articles

Sensor placement considering the observability of traffic dynamics: On the algebraic and graphical perspectives

In this paper, we present a new sensor location model that aims to maximize observability of link densities in a dynamic traffic network described using a piecewise linear system of ordinary differential equations. We develop an algebraic approach based on the eigenstructure to determine the sensor location for achieving full observability with a minimal number of sensors. Additionally, a graphical approach based on the concept of structural observability is developed. By exploiting the special property of flow conservation in traffic networks, we derive a simple analytical result that can be used to identify observable components in a partially observable system, which is the main contribution of this paper. The graphical and algebraic properties of observability are then integrated into a sensor location optimization model considering a wide range of traffic conditions. Through numerical experiments, we demonstrate the good performance of our sensor deployment strategies in terms of the average observability and estimation errors.

Caracterización del conocimiento de estudiantes universitarios al relacionar una función con su función derivada desde un enfoque gráfico

This paper aims to show how university students relate a function with its derivative function in a task with a purely graphical approach. This task is part of a diagnostic test used in a broader investigation whose objective is to build the concept of derivative in university students with the help of the APOE theory, which provides mental structures required to understand a mathematical concept: action, process, object, and scheme. Initially the paper shows the details of the test and the analyzed task, then it shows the qualitative and quantitative analysis of the observed responses. In general, the results obtained show deficiencies in students when faced with graphic focus exercises. Likewise, the justifications observed in students show an inclination towards solving exercises from an algebraic aspect, thus leaving aside the graphics of the activity. We assume that future research may use these results as background for their works, and active teachers may encourage their students to resolve this type of conceptual items in the classrooms.

Covert throughput maximization for NOMA based visible light covert communication networks

Covert throughput maximization for a non-orthogonal multiple access (NOMA)-based visible light covert communication (VLCC) network is investigated. The network consists of a light emitting diode (LED) transmitter, two NOMA users (one public, one covert), and a monitor tasked with detecting any covert transmissions between the LED and the covert user. The transmitter leverages its interaction with the public user to mask the covert communication with the covert user, adopting a random power transmission scheme. This strategy serves to amplify the monitor’s detection uncertainty and significantly enhance the covertness of the VLCC network. Two VLCC scenarios are covered: For the indoor static VLCC scenario where the LED is fixed, subject to the minimum detection error probability of the monitor (covertness constraint) and the outage probability of NOMA users (reliability constraint), the covert throughput is maximized by optimizing the ratio of the LED’s power allocation factor (PAF). For the mobile VLCC scenario where the LED is mounted on an unmanned aerial vehicle (UAV), subject to the constraints of the covertness, reliability and UAV’s flight region, the optimal LED’s PAF ratio and UAV’s location are jointly obtained via a graphical approach. Finally, simulations are carried out to analyze the influence of VLCC parameters on the maximum covert throughput, and results show that compared with benchmark schemes, the proposed scheme can greatly improve the covert throughput.

How collider bias affects the relationship between skin color and heart attack using directed acyclic graphs, propensity scores, and stepwise approaches

BackgroundStatistical methods are essential in epidemiology research, but they can generate erroneous estimates when selecting variables based only on statistical criteria. The use of directed acyclic graphs (DAG) helps to understand the causal relationships between variables and to avoid biases.ObjectiveCompare the estimate of the effect of skin color on heart attack obtained from three data analysis techniques: a stepwise approach based on statistical criteria, a propensity score technique, and a graphical approach based on causal criteria.MethodsPopulation-based cross-sectional study using data from the second National Health and Nutrition Examination Survey (NHANES). The exposure variable was skin color (black or non-black) and the outcome was heart attack (yes or no). Multivariable logistic regressions were carried out using the stepwise, propensity score techniques and the DAG-based approach to identify the association between the variables. In the stepwise technique, all variables potentially related to the outcome were included in the model and a forward or backward algorithm was used. The propensity score was applied, estimating the probability of exposure based on the covariates and helping to create balanced groups for comparison. Different possible causal models were developed between the variables in the DAG-based approach, identifying confounding, mediation, and collision factors. The models were created considering self-rated health as a confounding or collider variable, and the modeling results were verified.ResultsA total of 10,351 adults were evaluated, the majority female (52.1%), aged 20 to 39 years (48.5%), and with non-black skin color (90.4%). The prevalence of heart attacks was 3.0%, and 17% rated their health as fair or poor. Using different modeling techniques, no association was found between skin color and heart attack (p > 0.05), except when self-rated health, a collider variable, was included in the stepwise models. In this case, there was an inverse and biased association between the two variables, indicating a collision bias (stepwise-backward-OR 0.48; 95%CI 0.33–0.70; stepwise-forward-OR 0.64; 95%CI 0.44–0.94).ConclusionSkin color was not associated with heart attack when controlling for appropriate confounding factors. However, when adjusting for self-rated health in stepwise techniques, a colliding variable, there was an inverse and distorted association between the two variables, indicating a collider bias. The DAG-based approach and propensity score can avoid this bias by correctly identifying confounding factors and colliders.

Immunogenicity of Atezolizumab: Influence of Testing Method and Sampling Frequency on Reported Anti-drug Antibody Incidence Rates.

Measurement of anti-drug antibodies (ADA) to assess the incidence of ADA in a clinical trial is a critical step in immunogenicity assessment during the development of a protein therapeutic. We developed novel graphical approaches to illustrate clinical trial ADA data for the PD-L1 inhibitor atezolizumab (Tecentriq) that included a systematic analysis of the impact of the timing of ADA sampling and ADA assay drug tolerance on reported ADA incidence. We found that approaches used across the industry for ADA incidence analysis provide a limited view of immunogenicity in oncology studies, where ADA detection may be confounded by both drug dosage and patient attrition. Moreover, these approaches can miss important temporal information about the immune response. Our results demonstrated that the methodology of ADA assessment for the atezolizumab program was specifically designed to capture most ADA responses to ensure accurate reporting of ADA incidence. We further showed that the use of sparse sampling and/or ADA test methods with insufficient drug tolerance may result in a significant underreporting of ADA incidence. We conclude that the comparison of ADA incidence between different drugs can be highly misleading and that a test method with appropriate sensitivity in the presence of the drug and a clinical sampling scheme that is aligned with ADA responses to a drug is required to accurately report ADA incidence.

Qualitative evaluation of a molecular point-of-care testing study for influenza in UK primary care.

Influenza contributes to the surge in winter infections and the consequent winter pressures on the health service. Molecular point-of-care testing(POCT) for influenza might improve patient management by providing rapid and accurate clinical diagnosis to inform the timely initiation of antiviral therapy and reduce unnecessary admissions and antibiotics use. To explore factors that influence the adoption or non-adoption of POCT in English general practices and provide insights to enable its integration into routine practice workflows. A qualitative implementation evaluation was conducted in ten general practices within the English national sentinel network (Oxford-RCGP Research and Surveillance Centre), from April to July 2023. Using the nonadoption, abandonment, scale-up, spread, and sustainability framework, data collection and analysis were conducted across ten practices. We made ethnographic observations of the POCT workflow and surveyed the practice staff for their perspectives on POCT implementation. Data were analysed using a mix of descriptive statistics, graphical modelling techniques and framework approach. Ethnographic observations identified two modes of POCT integration into practice workflow: 1) clinician POCT workflow - typically involving batch testing due to time constraints, 2) research nurse/healthcare assistant POCT workflow - characterised by immediate testing of individual patients. Survey indicated that most primary care staff considered the POCT training offered was sufficient, and these practices were ready for change and had the capacity and resources to integrate POCT in workflows. General practices should demonstrate flexibility in the workflow and workforce they deploy to integrate POCT into routine clinical workflow.

A graphical method to determine robust stabilizing region of FOPID controllers for stable/unstable fractional-order plants with interval uncertainties of a fractional order and model coefficients

This paper focuses on robustly stabilizing stable and unstable fractional-order plants with one uncertain fractional-order term and interval uncertainties using fractional order P I μ D λ controllers. Two necessary and sufficient conditions are provided to check the robust stability of the closed-loop control system. Moreover, the D-decomposition technique is utilized to determine the robust stability region of the system. Subsequently, evolutionary algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE), can be utilized to discover a fractional-order controller within the region of robust stability. This work introduces three primary contributions, outlined as follows: (1) Utilizing a graphical approach, a set of stabilizing controller is obtained. (2) Rather than employing just a single stabilizing fractional-order controller, a collection of controllers is provided for the control system. (3) Employing evolutionary algorithms to find an optimal fractional-order controller. Finally, four numerical examples are presented to validate the results.

Graphical Heatmap-Based Approach to Indoor Radio Signal Propagation: Adapting Advanced Ray Tracing and Global Illumination Algorithms

Graphical Heatmap-Based Approach to Indoor Radio Signal Propagation: Adapting Advanced Ray Tracing and Global Illumination Algorithms

DIGITAL TECHNOLOGIES AND THE RESILIENCE OF UKRAINE'S SOCIAL PROTECTION SYSTEM: WARTIME EXPERIENCE

The article focuses on the problem of digitalization under military shocks in Ukraine. The research key aim is to substantiate the digitalization of the social sector of Ukraine as a strong determinant of its societal resilience in the full-scale war conditions. One of the main objectives is to determine the impact of digital technologies on the stability of the social security population under martial law. The study applies a comprehensive methodology based on a combination of logical-historical, analytical, institutional, graphical, and sociological approaches. It is all-important that in 2020 Ukraine adopted the Strategy for Digital Transformation of the Social Sphere, and within its framework carried out digitalization. Since the beginning of the war in February 2022, the Unified State Web Portal of Electronic Services "Diia" provided the opportunity to: receive pension and social payments on bank cards; new registration or recalculation of such payments; the restoration of documents on work experience (E-work book) or disability; etc. Digital technologies such as access to ID cards, digital signatures, etc. also helped economically active Ukrainians during the war. It is concluded that digitalization has become a strong factor for stability of the national social protection system, as well as the Ukraine’s socio-economic and societal resilience in in the most difficult times of war.

Trends and projections in fall death in the Netherlands from 1990 to 2045

BackgroundIncreasing life expectancy in high-income countries has been linked to a rise in fall mortality. In the Netherlands, mortality rates from falls have increased gradually from the 1950s, with some...

Latent growth factors as predictors of distal outcomes.

A currently overlooked application of the latent curve model (LCM) is its use in assessing the consequences of development patterns of change-that is as a predictor of distal outcomes. However, there are additional complications for appropriately specifying and interpreting the distal outcome LCM. Here, we develop a general framework for understanding the sensitivity of the distal outcome LCM to the choice of time coding, focusing on the regressions of the distal outcome on the latent growth factors. Using artificial and real-data examples, we highlight the unexpected changes in the regression of the slope factor which stand in contrast to prior work on time coding effects, and develop a framework for estimating the distal outcome LCM at a point in the trajectory-known as the aperture-which maximizes the interpretability of the effects. We also outline a prioritization approach developed for assessing incremental validity to obtain consistently interpretable estimates of the effect of the slope. Throughout, we emphasize practical steps for understanding these changing predictive effects, including graphical approaches for assessing regions of significance similar to those used to probe interaction effects. We conclude by providing recommendations for applied research using these models and outline an agenda for future work in this area. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Interpreting variational quantum models with active paths in parameterized quantum circuits

Variational quantum machine learning (VQML) models based on parameterized quantum circuits (PQC) have been expected to offer a potential quantum advantage for machine learning (ML) applications. However, comparison between VQML models and their classical counterparts is hard due to the lack of interpretability of VQML models. In this study, we introduce a graphical approach to analyze the PQC and the corresponding operation of VQML models to deal with this problem. In particular, we utilize the Stokes representation of quantum states to treat VQML models as network models based on the corresponding representations of basic gates. From this approach, we suggest the notion of active paths in the networks and relate the expressivity of VQML models with it. We investigate the growth of active paths in VQML models and observe that the expressivity of VQML models can be significantly limited for certain cases. Then we construct classical models inspired by our graphical interpretation of VQML models and show that they can emulate or outperform the outputs of VQML models for these cases. Our result provides a new way to interpret the operation of VQML models and facilitates the interconnection between quantum and classical ML areas.

Corrosion maps: Stability and composition diagrams for corrosion problems in CO2 transport

We present a Pourbaix-like graphical approach to assess the corrosive nature of impure CO2 streams for carbon capture and storage purposes. The effects of critical N- and S-containing impurities is evaluated. The model behind the approach assumes that chemical equilibrium is established, with some kinetic effects accommodated. The input is the composition of the stream (total quantity of S, N, H and O) and the output is the equilibrium composition (speciation). Comparison with experimental data shows that the content of acids in the CO2 stream upon reaching chemical equilibrium is a strong indicator of the corrosiveness of a given mixture.

Visualising and quantifying the usefulness of new predictors stratified by outcome class: The U-smile method.

Binary classification methods encompass various algorithms to categorize data points into two distinct classes. Binary prediction, in contrast, estimates the likelihood of a binary event occurring. We introduce a novel graphical and quantitative approach, the U-smile method, for assessing prediction improvement stratified by binary outcome class. The U-smile method utilizes a smile-like plot and novel coefficients to measure the relative and absolute change in prediction compared with the reference method. The likelihood-ratio test was used to assess the significance of the change in prediction. Logistic regression models using the Heart Disease dataset and generated random variables were employed to validate the U-smile method. The receiver operating characteristic (ROC) curve was used to compare the results of the U-smile method. The likelihood-ratio test demonstrated that the proposed coefficients consistently generated smile-shaped U-smile plots for the most informative predictors. The U-smile plot proved more effective than the ROC curve in comparing the effects of adding new predictors to the reference method. It effectively highlighted differences in model performance for both non-events and events. Visual analysis of the U-smile plots provided an immediate impression of the usefulness of different predictors at a glance. The U-smile method can guide the selection of the most valuable predictors. It can also be helpful in applications beyond prediction.

Productivity and energy consumption as performance criteria to overcome bias caused by membrane resistance

Membrane scientists and engineers routinely compare the performance of membranes for different applications. Such comparisons are required to optimize surface chemistries and to evaluate and select the best membrane to scale-up industrial applications. In most cases, the potential of the feed to foul membrane surfaces is also critical. When different membranes under consideration have different resistances Rm, such comparisons must be done with care. Here we elucidate the impact of Rm on fouling kinetics for a range of fouling mechanisms and experimental protocols. We show that traditional plots of flux versus time for constant pressure operation, or pressure versus volume in constant flux operation, obscure two important performance criteria: accumulated volume over a defined time period (i.e., productivity) and energy consumption; practical implications include the risk that higher performance may be attributed to membranes that actually have lower productivity and/or that consume more energy, and that lower performing membranes may be inadvertently chosen from screening studies as offering superior performance. To facilitate membrane comparison with different Rm, we have developed a graphical approach using normalized coordinates. This approach yields linear plots having slopes that are a function of fouling parameters only, and are independent of membrane resistance Rm. Therefore, such normalization coordinates, presented for each fouling mechanism and each operation mode, can be employed to isolate fouling potential. In addition, we developed a new graphical approach employing contour plots of iso-volume or iso-energy lines to visualize the potential trade-offs between antifouling performance, membrane permeability, and either productivity in the case of constant pressure operation, or specific energy in the case of constant flux operation. This study provides a framework for judging the performance of different membranes under fouling conditions, considering fouling, energy consumption and productivity as metrics, independent of membrane resistance. This framework will help guide process design and the design and preparation of antifouling membranes.

A predictor-informed multi-subject bayesian approach for dynamic functional connectivity.

Dynamic functional connectivity investigates how the interactions among brain regions vary over the course of an fMRI experiment. Such transitions between different individual connectivity states can be modulated by changes in underlying physiological mechanisms that drive functional network dynamics, e.g., changes in attention or cognitive effort. In this paper, we develop a multi-subject Bayesian framework where the estimation of dynamic functional networks is informed by time-varying exogenous physiological covariates that are simultaneously recorded in each subject during the fMRI experiment. More specifically, we consider a dynamic Gaussian graphical model approach where a non-homogeneous hidden Markov model is employed to classify the fMRI time series into latent neurological states. We assume the state-transition probabilities to vary over time and across subjects as a function of the underlying covariates, allowing for the estimation of recurrent connectivity patterns and the sharing of networks among the subjects. We further assume sparsity in the network structures via shrinkage priors, and achieve edge selection in the estimated graph structures by introducing a multi-comparison procedure for shrinkage-based inferences with Bayesian false discovery rate control. We evaluate the performances of our method vs alternative approaches on synthetic data. We apply our modeling framework on a resting-state experiment where fMRI data have been collected concurrently with pupillometry measurements, as a proxy of cognitive processing, and assess the heterogeneity of the effects of changes in pupil dilation on the subjects' propensity to change connectivity states. The heterogeneity of state occupancy across subjects provides an understanding of the relationship between increased pupil dilation and transitions toward different cognitive states.

A Graphical Deep Learning Approach to RF Fingerprinting in the Time-Frequency Domain

A Graphical Deep Learning Approach to RF Fingerprinting in the Time-Frequency Domain

An extended graphical solution for undrained cylindrical cavity expansion in K0‐consolidated Mohr–Coulomb soil

AbstractThis paper develops a general and complete solution for the undrained cylindrical cavity expansion problem in nonassociated Mohr‐Coulomb soil under nonhydrostatic initial stress field (i.e., arbitrary values of the earth pressure coefficient), by expanding a unique and efficient graphical solution procedure recently proposed by Chen and Wang in 2022 for the special in situ stress case with . It is interesting to find that the cavity expansion deviatoric stress path is always composed of a series of piecewise straight lines, for all different case scenarios of K0 being involved. When the cavity is sufficiently expanded, the stress path will eventually end, exclusively, in a major sextant with Lode angle θ in between and or on the specific line of . The salient advantage/feature of the present general graphical approach lies in that it can deduce the cavity expansion responses in full closed form, nevertheless being free of the limitation of the intermediacy assumption for the vertical stress and of the difficulty existing in the traditional zoning method that involves cumbersome, sequential determination of distinct Mohr–Coulomb plastic regions. Some typical results for the desired cavity expansion curves and the limit cavity pressure are presented, to investigate the impacts of soil plasticity parameters and the earth pressure coefficient on the cavity responses. The proposed graphical method/solution will be of great value for the interpretation of pressuremeter tests in cohesive‐frictional soils.

Causal inference in ethnographic research: Refining explanations with abductive logic, strength of evidence assessments, and graphical models.

In their classic accounts, anthropological ethnographers developed causal arguments for how specific sociocultural structures and processes shaped human thought, behavior, and experience in particular settings. Despite this history, many contemporary ethnographers avoid establishing in their work direct causal relationships between key variables in the way that, for example, quantitative research relying on experimental or longitudinal data might. As a result, ethnographers in anthropology and other fields have not advanced understandings of how to derive causal explanations from their data, which contrasts with a vibrant "causal revolution" unfolding in the broader social and behavioral sciences. Given this gap in understanding, we aim in the current article to clarify the potential ethnography has for illuminating causal processes related to the cultural influence on human knowledge and practice. We do so by drawing on our ongoing mixed methods ethnographic study of games, play, and avatar identities. In our ethnographic illustrations, we clarify points often left unsaid in both classic anthropological ethnographies and in more contemporary interdisciplinary theorizing on qualitative research methodologies. More specifically, we argue that for ethnographic studies to illuminate causal processes, it is helpful, first, to state the implicit strengths and logic of ethnography and, second, to connect ethnographic practice more fully to now well-developed interdisciplinary approaches to causal inference. In relation to the first point, we highlight the abductive inferential logic of ethnography. Regarding the second point, we connect the ethnographic logic of abduction to what Judea Pearl has called the ladder of causality, where moving from association to intervention to what he calls counterfactual reasoning produces stronger evidence for causal processes. Further, we show how graphical modeling approaches to causal explanation can help ethnographers clarify their thinking. Overall, we offer an alternative vision of ethnography, which contrasts, but nevertheless remains consistent with, currently more dominant interpretive approaches.

Visualizing hypothesis tests in survival analysis under anticipated delayed effects.

What can be considered an appropriate statistical method for the primary analysis of a randomized clinical trial (RCT) with a time-to-event endpoint when we anticipate non-proportional hazards owing to a delayed effect? This question has been the subject of much recent debate. The standard approach is a log-rank test and/or a Cox proportional hazards model. Alternative methods have been explored in the statistical literature, such as weighted log-rank tests and tests based on the Restricted Mean Survival Time (RMST). While weighted log-rank tests can achieve high power compared to the standard log-rank test, some choices of weights may lead to type-I error inflation under particular conditions. In addition, they are not linked to a mathematically unambiguous summary measure. Test statistics based on the RMST, on the other hand, allow one to investigate the average difference between two survival curves up to a pre-specified time point -a mathematically unambiguous summary measure. However, by emphasizing differences prior to , such test statistics may not fully capture the benefit of a new treatment in terms of long-term survival. In this article, we introduce a graphical approach for direct comparison of weighted log-rank tests and tests based on the RMST. This new perspective allows a more informed choice of the analysis method, going beyond power and type I error comparison.