Intuitive Measure Research Articles

Validating automated resonance evaluation with synthetic data

The integrity and precision of nuclear data are crucial for a broad spectrum of applications, from national security and nuclear reactor design to medical diagnostics, where the associated uncertainties can significantly impact outcomes. A substantial portion of uncertainty in nuclear data originates from the subjective biases in the evaluation process, a crucial phase in the nuclear data production pipeline. Recent advancements indicate that automation of certain routines can mitigate these biases, thereby standardizing the evaluation process and enhancing reproducibility. This research aims to provide a methodology, framework, and metrics for the validation of automated nuclear data evaluation software leveraging high-quality synthetic data that closely mimic real experimental observables. An introduced error metric provides a scale and intuitive measure of the evaluation quality by quantifying the estimate’s accuracy and performance across the specified energy range. Synthetic data provides access to experimental observables and underlying resonance parameters, enabling comparison of different evaluations. The methodology is demonstrated using Ta-181 isotope data in the resolved resonance region. The Automated Resonance Identification Subroutine (ARIS), which operates without prior resonance information, was used to test and showcase the framework’s capabilities utilizing the proposed error metrics. The results demonstrate the effectiveness of the proposed approach and framework for optimizing software parameters and testing hypotheses through “what-if” controlled experiments, such as modifying assumptions about experimental conditions or average resonance parameters.

Chain novel, or Markov chain? Estimating the authority of U.S. Supreme Court case law

AbstractHow does the authority of case law evolve over time? On the Dworkinian legal formalist view, cases increase in authority as they become more embedded in the “chain” of legal precedent, but on the Holmesian legal realist view, each case's authority is proportional to its ability to predict future legal outcomes. In this article, I show how modeling the citation network of U.S. Supreme Court case law not as a chain novel (à la Dworkin) but instead as a Markov chain (à la Holmes, or so I argue) unlocks an intuitive measure of case authority—called HolmesRank—that outperforms the existing approach in a variety of validation tasks. I then demonstrate how the authority scores produced using this Markov machinery empower the analysis of two important normative questions: (1) the ideological basis of lasting precedential authority and (2) the causal effect of the Supreme Court's citation choices on lower court compliance.

Simplify, Consolidate, Intervene: Facilitating Institutional Support with Mental Models of Learning Management System Use

Measuring instructors' adoption of learning management system (LMS) tools is a critical first step in evaluating the efficacy of online teaching and learning at scale. Existing models for LMS adoption are often qualitative, learner-centered, and difficult to leverage towards institutional support. We propose depth-of-use (DOU): an intuitive measurement model for faculty's utilization of a university-wide LMS and their needs for institutional support. We hypothesis-test the relationship between DOU and course attributes like modality, participation, logistics, and outcomes. In a large-scale analysis of metadata from 30000+ courses offered at Virginia Tech over two years, we find that a pervasive need for scale, interoperability and ubiquitous access drives LMS adoption by university instructors. We then demonstrate how DOU can help faculty members identify the opportunity-cost of transition from legacy apps to LMS tools. We also describe how DOU can help instructional designers and IT organizational leadership evaluate the impact of their support allocation, faculty development and LMS evangelism initiatives.

FX resilience around the world: Fighting volatile cross-border capital flows

The volatility of cross-border capital flow (CF) has become higher in the last two decades. We use a sample of advanced and emerging market economies in the last two decades, with a focus on portfolio fund flows (equities and bonds). We find that the impact of portfolio CF volatility on exchange rate (FX) volatility is diminished in the short-term if specific economic fundamentals of the country are stronger, especially when these fundamentals pass certain threshold levels. The finding is controlled for different exchange rate regimes and the level of capital control. We further introduce an intuitive FX resilience measure that can be used to assess the stability of exchange rates in the context of heightened CF volatility. The decomposed FX resilience measure shows that the major contributing fundamentals to FX resilience are from the finance channel, including short-term interest rate, fiscal surplus, and financial development.

Development and validation of a sepsis risk index supporting early identification of ICU-acquired sepsis: an observational study

BackgroundSepsis is a threat to global health, and domestically is the major cause of in-hospital mortality. Due to increases in inpatient morbidity and mortality resulting from sepsis, healthcare providers (HCPs) would accrue significant benefits from identifying the syndrome early and treating it promptly and effectively. Prompt and effective detection, diagnosis, and treatment of sepsis requires frequent monitoring and assessment of patient vital signs and other relevant data present in the electronic health record. MethodsThis study explored the development of machine learning-based models to generate a novel sepsis risk index (SRI) which is an intuitive 0–100 marker that reflects the risk of a patient acquiring sepsis or septic shock and assists in timely diagnosis. Machine learning models were developed and validated using openly accessible critical care databases. The model was developed using a single database (from one institution) and validated on a separate database consisting of patient data collected across multiple ICUs. ResultsThe developed model achieved an area under the receiver operating characteristic curve of 0.82 and 0.84 for the diagnosis of sepsis and septic shock, respectively, with a sensitivity and specificity of 79.1% [75.1, 82.7] and 73.3% [72.8, 73.8] for a sepsis diagnosis and 83.8% [80.8, 86.5] and 73.3% [72.8, 73.8] for a septic shock diagnosis. ConclusionThe SRI provides critical care HCPs with an intuitive quantitative measure related to the risk of a patient having or acquiring a life-threatening infection. Evaluation of the SRI over time may provide HCPs the ability to initiate protective interventions (e.g., targeted antibiotic therapy).

Industrial‐Level Modulation of Catalyst‐Electrolyte Microenvironment for Electrocatalytic CO2 Reduction: Challenges and Advancements

AbstractCO2 reduction reaction (CO2RR), as a promising strategy for storing renewable energy and promoting carbon resource recycling, is critical for industrial development. Previous reports have extensively explored catalyst‐electrolyte microenvironmental modulation to elucidate coupling mechanisms and enhance catalytic conversion to multicarbon products. Currently, most reviews mainly focus on the impact of microenvironment modulation in low‐current systems on mechanism exploration and performance optimization, yet few of them can integrate macroscopic applications with microscopic investigations to explore the relevance between industrial development and catalyst‐electrolyte microenvironmental optimization. To address the gap, this review focuses on summarizing the challenges and advancements in microenvironment modulation for the development of high‐current devices. By introducing models of different scales sequentially, the connection between microenvironmental modulation and device performance is clarified. Then, various invalidation mechanisms and effective solutions are summarized to intuitively expound the impact of microenvironment modulation on high‐current stability. Meanwhile, as an intuitive measure of the rationality of microenvironment modulation, evaluation methods of device performance should be refined, which are also covered in further detail below. Finally, more valuable and challenging prospects are discussed for guiding the further industrial transformation of CO2RR.

The Fragility Index of Risk Factors for Hamstring Injuries.

OBJECTIVE: To determine the Fragility Index of hamstring injury risk factors, defined as the minimum number of participants who would need to change classification to make a hamstring injury risk factor statistically nonsignificant. DESIGN: Retrospective secondary data analysis. METHODS: Studies that investigated 1 or more risk factors for hamstring injury, and presented sufficient data to develop a 2 × 2 contingency table were included. A systematic literature search and reference screening of a recent hamstring injury systematic review were conducted to identify 78 articles. Relative risk and 95% confidence intervals were determined and then systematically recalculated by removing 1 observation from the high-risk injury count and adding it to the high-risk noninjury count. The Fragility Index for a risk factor was the number of observations required to be moved between groups until the relative risk was no longer significant. RESULTS: The median Fragility Index of all hamstring injury risk factors was 3 (Q1-Q3 = 2-6). The Fragility Index for nonmodifiable risk factors was 3 (Q1-Q3 = 2-6) and 3 (Q1-Q3 = 2-5) for modifiable risk factors. Over 35% of all included hamstring injury risk factors had a Fragility Index of ≤2. CONCLUSION: Most statistically significant hamstring injury risk factors are fragile associations. The interpretation of significant hamstring injury risk factors should consider a range of statistical metrics, and while the Fragility Index should never be considered in isolation, it is an intuitive measure to help assess the robustness of findings. J Orthop Sports Phys Ther 2024;54(10):672-678. Epub 4 September 2024. doi:10.2519/jospt.2024.12300.

Measures of Performance and Clinical Superiority Thresholds for 'Test-and-treat' Predictive Biomarkers.

Predictive biomarkers are intended to predict an individual's expected response to specific treatments. These are an important component of precision medicine. We explore measures of biomarker performance that are based on the expected probability of response to individual treatment conditional on biomarker status. We show how these measures can be used to establish thresholds at which testing strategies will be clinically superior. We used a decision model to compare expected probabilities of response of treat-all and test-and-treat strategies. Based on this, R-Shiny-based apps were developed which produce plots of the threshold positive and negative predictive values or sensitivities and specificities above which a 'test-and-treat' strategy will outperform a 'treat-all' strategy. We present a case study using data on the use of RAS status to predict response to panitumumab in metastatic colorectal cancer. Where a companion diagnostic is predictive of response to one of the treatments being compared, it is possible to estimate threshold sensitivities and specificities above which a testing strategy will outperform a treat-all strategy, based only on the odds ratio of response. Where negative and positive predictive values were used, the threshold depended on the prevalence of the biomarker-positive patients. These intuitive performance measures for predictive biomarkers, based on expected response to individual treatments, can be used to identify promising candidate companion diagnostic tests and indicate the potential magnitude of the net benefit of testing.

Ocean weather, biological rates, and unexplained global ecological patterns.

As on land, oceans exhibit high temporal and spatial temperature variation. This "ocean weather" contributes to the physiological and ecological processes that ultimately determine the patterns of species distribution and abundance, yet is often unrecognized, especially in tropical oceans. Here, we tested the paradigm of temperature stability in shallow waters (<12.5 m) across different zones of latitude. We collated hundreds of in situ, high temporal-frequency ocean temperature time series globally to produce an intuitive measure of temperature variability, ranging in scale from quarter-diurnal to annual time spans. To estimate organismal sensitivity of ectotherms (i.e. microbes, algae, and animals whose body temperatures depend upon ocean temperature), we computed the corresponding range of biological rates (such as metabolic rate or photosynthesis) for each time span, assuming an exponential relationship. We found that subtropical regions had the broadest temperature ranges at time spans equal to or shorter than a month, while temperate and tropical systems both exhibited narrow (i.e. stable) short-term temperature range estimates. However, temperature-dependent biological rates in tropical regions displayed greater ranges than in temperate systems. Hence, our results suggest that tropical ectotherms may be relatively more sensitive to short-term thermal variability. We also highlight previously unexplained macroecological patterns that may be underpinned by short-term temperature variability.

Beyond 'statistical significance': A nontechnical primer of Bayesian statistics and Bayes factors for health researchers.

Hypothesis testing is integral to health research and is commonly completed through frequentist statistics focused on computing p values. p Values have been long criticized for offering limited information about the relationship of variables and strength of evidence concerning the plausibility, presence and certainty of associations among variables. Bayesian statistics is a potential alternative for inference-making. Despite emerging discussion on Bayesian statistics across various disciplines, the uptake of Bayesian statistics in health research is still limited. To offer a primer on Bayesian statistics and Bayes factors for health researchers to gain preliminary knowledge of its use, application and interpretation in health research. Theoretical and empirical literature on Bayesian statistics and methods were used to develop this methodological primer. Using Bayesian statistics in health research without a careful and complete understanding of its underlying philosophy and differences from frequentist testing, estimation and interpretation methods can result in similar ritualistic use as done for p values. Health researchers should supplement frequentists statistics with Bayesian statistics when analysing research data. The overreliance on p values for clinical decisions making should be avoided. Bayes factors offer a more intuitive measure of assessing the strength of evidence for null and alternative hypothesis.

Self-Supervised Machine Learning to Characterize Step Counts from Wrist-Worn Accelerometers in the UK Biobank.

Step count is an intuitive measure of physical activity frequently quantified in health-related studies; however, accurate step counting is difficult in the free-living environment, with error routinely above 20% in wrist-worn devices against camera-annotated ground truth. This study aimed to describe the development and validation of step count derived from a wrist-worn accelerometer and assess its association with cardiovascular and all-cause mortality in a large prospective cohort. We developed and externally validated a self-supervised machine learning step detection model, trained on an open-source and step-annotated free-living dataset. Thirty-nine individuals will free-living ground-truth annotated step counts were used for model development. An open-source dataset with 30 individuals was used for external validation. Epidemiological analysis was performed using 75,263 UK Biobank participants without prevalent cardiovascular disease (CVD) or cancer. Cox regression was used to test the association of daily step count with fatal CVD and all-cause mortality after adjustment for potential confounders. The algorithm substantially outperformed reference models (free-living mean absolute percent error of 12.5% vs 65%-231%). Our data indicate an inverse dose-response association, where taking 6430-8277 daily steps was associated with 37% (25%-48%) and 28% (20%-35%) lower risk of fatal CVD and all-cause mortality up to 7 yr later, compared with those taking fewer steps each day. We have developed an open and transparent method that markedly improves the measurement of steps in large-scale wrist-worn accelerometer datasets. The application of this method demonstrated expected associations with CVD and all-cause mortality, indicating excellent face validity. This reinforces public health messaging for increasing physical activity and can help lay the groundwork for the inclusion of target step counts in future public health guidelines.

Pythagorean fuzzy intuitive distance measure with its applications in MADM issues

Pythagorean fuzzy intuitive distance measure with its applications in MADM issues

Health Care Contact Days Among Older Cancer Survivors.

Health care contact days-days spent receiving health care outside the home-represent an intuitive, practical, and person-centered measure of time consumed by health care. We linked 2019 Medicare Current Beneficiary Survey and traditional Medicare claims data for community-dwelling older adults with a history of cancer. We identified contact days (ie, spent in a hospital, emergency department, skilled nursing facility, or inpatient hospice or receiving ambulatory care including an office visit, procedure, treatment, imaging, or test) and described patterns of total and ambulatory contact days. Using weighted Poisson regression models, we identified factors associated with contact days. We included 1,168 older adults representing 4.51 million cancer survivors (median age, 76.4 years, 52.8% women). The median (IQR) time from cancer diagnosis was 65 (27-126) months. In 2019, these adults had mean (standard deviation) total contact days of 28.4 (27.6) and ambulatory contact days of 24.2 (23.6). These included days for tests (8.0 [8.8]), imaging (3.6 [4.1]), visits with any clinicians (12.4 [11.5]), and visits with primary care clinicians (4.4 [4.7]), and nononcology specialists (7.1 [9.4]) specifically. Sixty-four percent of days with a nonvisit ambulatory service (eg, a test) were not on the same day as a clinician visit. Factors associated with more total contact days included younger age, lower income, more chronic conditions, poor self-rated health, and tendency to "go to doctor as soon as feel bad." Older adult cancer survivors spent nearly 1 month of the year receiving health care outside the home. This care was largely ambulatory, often delivered by nononcologists, and varied by factors beyond clinical characteristics. These results highlight the need to recognize patient burdens and improve survivorship care delivery, including through care coordination.

Effective sample size: A measure of individual uncertainty in predictions.

Clinical prediction models are estimated using a sample of limited size from the target population, leading to uncertainty in predictions, even when the model is correctly specified. Generally, not all patient profiles are observed uniformly in model development. As a result, sampling uncertainty varies between individual patients' predictions. We aimed to develop an intuitive measure of individual prediction uncertainty. The variance of a patient's prediction can be equated to the variance of the sample mean outcome in hypothetical patients with the same predictor values. This hypothetical sample size can be interpreted as the number of similar patients that the prediction is effectively based on, given that the model is correct. For generalized linear models, we derived analytical expressions for the effective sample size. In addition, we illustrated the concept in patients with acute myocardial infarction. In model development, can be used to balance accuracy versus uncertainty of predictions. In a validation sample, the distribution of indicates which patients were more and less represented in the development data, and whether predictions might be too uncertain for some to be practically meaningful. In a clinical setting, the effective sample size may facilitate communication of uncertainty about predictions. We propose the effective sample size as a clinically interpretable measure of uncertainty in individual predictions. Its implications should be explored further for the development, validation and clinical implementation of prediction models.

Measures of Agreement with Multiple Raters: Fréchet Variances and Inference

Most measures of agreement are chance-corrected. They differ in three dimensions: their definition of chance agreement, their choice of disagreement function, and how they handle multiple raters. Chance agreement is usually defined in a pairwise manner, following either Cohen’s kappa or Fleiss’s kappa. The disagreement function is usually a nominal, quadratic, or absolute value function. But how to handle multiple raters is contentious, with the main contenders being Fleiss’s kappa, Conger’s kappa, and Hubert’s kappa, the variant of Fleiss’s kappa where agreement is said to occur only if every rater agrees. More generally, multi-rater agreement coefficients can be defined in a g-wise way, where the disagreement weighting function uses g raters instead of two. This paper contains two main contributions. (a) We propose using Fréchet variances to handle the case of multiple raters. The Fréchet variances are intuitive disagreement measures and turn out to generalize the nominal, quadratic, and absolute value functions to the case of more than two raters. (b) We derive the limit theory of g-wise weighted agreement coefficients, with chance agreement of the Cohen-type or Fleiss-type, for the case where every item is rated by the same number of raters. Trying out three confidence interval constructions, we end up recommending calculating confidence intervals using the arcsine transform or the Fisher transform.

Vegetation quality assessment: A sampling-based loss-gain accounting framework for native, disturbed and reclaimed vegetation

Governments and society increasingly are demanding that industrial projects result in a net positive impact (NPI) on biodiversity. Impacts are commonly measured in terms of losses and gains of area and quality of vegetation, where quality refers to how closely a site matches the condition of native vegetation in its undisturbed state. Existing vegetation quality frameworks share a number of limitations, including little or no replication, uncertain scope of inference, vulnerability to bias, and inability to measure error. Here we present the Vegetation Quality Assessment (VQA) framework, a sampling-based extension of Quality Hectares that measures vegetation quality in terms of overlap between the probability distributions of ecological indicators at a project site and in undisturbed (benchmark) vegetation of the same kind. Distribution overlap incorporates natural variation at the landscape scale and provides an intuitive measure of quality that varies between 0 and 1. Indicators are measured using a stratified-random sampling design that minimizes bias and supports inference at the scale of the project landscape. Confidence limits of quality and quality hectares are determined by bootstrapping; power and minimum sample sizes are estimated by Monte Carlo simulation. Multiple assessments track losses and gains of quality hectares and enable accurate accounting of progress to NPI. The VQA framework can be implemented using a variety of vegetation sampling methods, allowing existing vegetation databases to be leveraged as sources of data. We conclude by demonstrating the application of VQA at several mining operations in the Elk Valley of southeastern British Columbia, Canada.

A graph theory based similarity metric enables comparison of subpopulation psychometric networks.

Network psychometrics leverages pairwise Markov random fields to depict conditional dependencies among a set of psychological variables as undirected edge-weighted graphs. Researchers often intend to compare such psychometric networks across subpopulations, and recent methodological advances provide invariance tests of differences in subpopulation networks. What remains missing, though, is an analogue to an effect size measure that quantifies differences in psychometric networks. We address this gap by complementing recent advances for investigating whether psychometric networks differ with an intuitive similarity measure quantifying the extent to which networks differ. To this end, we build on graph-theoretic approaches and propose a similarity measure based on the Frobenius norm of differences in psychometric networks' weighted adjacency matrices. To assess this measure's utility for quantifying differences between psychometric networks, we study how it captures differences in subpopulation network models implied by both latent variable models and Gaussian graphical models. We show that a wide array of network differences translates intuitively into the proposed measure, while the same does not hold true for customary correlation-based comparisons. In a simulation study on finite-sample behavior, we show that the proposed measure yields trustworthy results when population networks differ and sample sizes are sufficiently large, but fails to identify exact similarity when population networks are the same. From these results, we derive a strong recommendation to only use the measure as a complement to a significant test for network similarity. We illustrate potential insights from quantifying psychometric network similarities through cross-country comparisons of human values networks. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Temporal segmentation of EEG based on functional connectivity network structure

In the study of brain functional connectivity networks, it is assumed that a network is built from a data window in which activity is stationary. However, brain activity is non-stationary over sufficiently large time periods. Addressing the analysis electroencephalograph (EEG) data, we propose a data segmentation method based on functional connectivity network structure. The goal of segmentation is to ensure that within a window of analysis, there is similar network structure. We designed an intuitive and flexible graph distance measure to quantify the difference in network structure between two analysis windows. This measure is modular: a variety of node importance indices can be plugged into it. We use a reference window versus sliding window comparison approach to detect changes, as indicated by outliers in the distribution of graph distance values. Performance of our segmentation method was tested in simulated EEG data and real EEG data from a drone piloting experiment (using correlation or phase-locking value as the functional connectivity strength metric). We compared our method under various node importance measures and against matrix-based dissimilarity metrics that use singular value decomposition on the connectivity matrix. The results show the graph distance approach worked better than matrix-based approaches; graph distance based on partial node centrality was most sensitive to network structural changes, especially when connectivity matrix values change little. The proposed method provides EEG data segmentation tailored for detecting changes in terms of functional connectivity networks. Our study provides a new perspective on EEG segmentation, one that is based on functional connectivity network structure differences.

The Piggy Bank Index: An intuitive risk measure to assess liquidity and capital adequacy in banks

This study proposes a new liquidity and capital adequacy risk index, the Piggy Bank Index. This index measures the gap between liquid assets and total liabilities over liquid capital. Using data from the financial statements of 106 US banks during 2004–2009, we show the benefits of this index as an early warning indicator for liquidity and capital adequacy problems. The Piggy Bank Index becomes more effective as the bank failure approaches. The evidence shows that the further and the longer the bank's Piggy Bank Index is out of the 95 % confidence interval of the “healthy” banks, the closer the bank will be to failure. This index was successfully tested for the cases of Merrill Lynch in 2008, and for the case of Silicon Valley Bank, First Republic Bank and Silvergate Bank in 2023.

Direct dielectrophoretic characterization of particles in the high-density microwell array using optical tweezers

In this work, a quantitative and intuitive measurement of dielectrophoresis (DEP) behavior in a high-density dielectrophoretic microwell array chip was accomplished in assistance with the holographic optical tweezers. A one-dimensional force balance system with high reliability was achieved between the transverse optical trapping (TOT) force and the DEP force. The value of the DEP force was inferred by accurately calculating the TOT force, and the direction of the DEP force was observed with high-magnification microscopy. In order to estimate the availability and operability of the DEP force measurement system, we systematically investigated the influences of various external factors on the DEP behaviors of microspheres and live cells. All the quantitative measurement results, which agree well with the theory, showed that this system could serve as an accurate research tool for DEP behavior characterizations to optimize and improve the DEP-based manipulations of microparticles and live cells for a broad range of applications. The system also provides an approach to quantitatively obtain important parameters such as Clausius-Mossotti factors for cells in mediums, which could potentially be used to understand the dielectric properties of different types of cells for cell differentiation and diagnosis at the single-cell level.