Confidence Measure Research Articles

Development and Validation of a Measure of Self-Efficacy for Advance Care Planning.

Medical care received at end-of-life is often not aligned with individuals' values and care preferences. Much can get in the way of an individual communicating and documenting their preferences to care providers and close others, even if it is a goal to do so. The objective of this work was to develop a measure of Advance Care Planning Self-Efficacy (ACP SE) focused on three important behaviors: completing a living will, documenting a healthcare agent, and discussing quality versus quantity of life issues. Measure development was framed by the Transtheoretical Model (TTM) of behavior change. Following the completion of focus groups and formative qualitative work, an independent sample (N = 310, adults aged 50+) was randomly split into two halves for exploratory and confirmatory factor analyses (EFA/CFA). Multivariate analyses examined relationships between ACP SE and other TTM constructs. Results of the CFA demonstrated excellent internal consistency (α = .95) and good model fit (CFI = .89, RMSEA = .13). Self-efficacy was framed by the TTM as situation-specific confidence. Through the measure development process, it was found that confidence to do ACP was impacted by various situational and attitudinal variables. ACP Self-Efficacy was found to differ significantly by Stage of Change. This study produced a reliable and valid measure of situation-specific confidence for ACP grounded within the TTM that could enhance future interventions aimed at increasing participation in ACP.

Diagnosis with confidence: deep learning for reliable classification of laryngeal dysplasia.

Diagnosis of head and neck (HN) squamous dysplasias and carcinomas is critical for patient care, cure, and follow-up. It can be challenging, especially for grading intraepithelial lesions. Despite recent simplification in the last WHO grading system, the inter- and intraobserver variability remains substantial, particularly for nonspecialized pathologists, exhibiting the need for new tools to support pathologists. In this study we investigated the potential of deep learning to assist the pathologist with automatic and reliable classification of HN lesions following the 2022 WHO classification system. We created, for the first time, a large-scale database of histological samples (>2000 slides) intended for developing an automatic diagnostic tool. We developed and trained a weakly supervised model performing classification from whole-slide images (WSI). We evaluated our model on both internal and external test sets and we defined and validated a new confidence score to assess the predictions that can be used to identify difficult cases. Our model demonstrated high classification accuracy across all lesion types on both internal and external test sets (respectively average area under the curve [AUC]: 0.878 (95% confidence interval [CI]: [0.834-0.918]) and 0.886 (95% CI: [0.813-0.947])) and the confidence score allowed for accurate differentiation between reliable and uncertain predictions. Our results demonstrate that the model, associated with confidence measurements, can help in the difficult task of classifying HN squamous lesions by limiting variability and detecting ambiguous cases, taking us one step closer to a wider adoption of AI-based assistive tools.

Towards a scientific community consensus on designating Vulnerable Marine Ecosystems from imagery.

Management of deep-sea fisheries in areas beyond national jurisdiction by Regional Fisheries Management Organizations/Arrangements (RFMO/As) requires identification of areas with Vulnerable Marine Ecosystems (VMEs). Currently, fisheries data, including trawl and longline bycatch data, are used by many RFMO/As to inform the identification of VMEs. However, the collection of such data creates impacts and there is a need to collect non-invasive data for VME identification and monitoring purposes. Imagery data from scientific surveys satisfies this requirement, but there currently is no established framework for identifying VMEs from images. Thus, the goal of this study was to bring together a large international team to determine current VME assessment protocols and establish preliminary global consensus guidelines for identifying VMEs from images. An initial assessment showed a lack of consistency among RFMO/A regions regarding what is considered a VME indicator taxon, and hence variability in how VMEs might be defined. In certain cases, experts agreed that a VME could be identified from a single image, most often in areas of scleractinian reefs, dense octocoral gardens, multiple VME species' co-occurrence, and chemosynthetic ecosystems. A decision flow chart is presented that gives practical interpretation of the FAO criteria for single images. To further evaluate steps of the flow chart related to density, data were compiled to assess whether scientists perceived similar density thresholds across regions. The range of observed densities and the density values considered to be VMEs varied considerably by taxon, but in many cases, there was a statistical difference in what experts considered to be a VME compared to images not considered a VME. Further work is required to develop an areal extent index, to include a measure of confidence, and to increase our understanding of what levels of density and diversity correspond to key ecosystem functions for VME indicator taxa. Based on our results, the following recommendations are made: 1. There is a need to establish a global consensus on which taxa are VME indicators. 2. RFMO/As should consider adopting guidelines that use imagery surveys as an alternative (or complement) to using bycatch and trawl surveys for designating VMEs. 3. Imagery surveys should also be included in Impact Assessments. And 4. All industries that impact the seafloor, not just fisheries, should use imagery surveys to detect and identify VMEs.

HEEALing after an error – use of the novel mnemonic HEEAL to structure error disclosure to patients and peers

First, to determine the feasibility of providing a simple educational intervention using the HEEAL (Honesty, Empathy, Educate, Apology/awareness, Lessen the chance for future errors) mnemonic. Second, to assess the intervention’s ability to improve communication self-efficacy, knowledge and objective measures of error disclosure competence among providers. A 1-day (6-hour) pilot medical error curriculum was created to teach the HEEAL method of medical error disclosure to both patients and peers who have committed errors. The four-part curriculum consists of pre-intervention evaluation, HEEAL content lecture, rapid cycle deliberate practice (RCDP) with debriefing and post-intervention evaluation. This curriculum was repeated twice. The first training focused on medical error disclosure to patients’ families and the second on medical error disclosure to involved peers. Participating faculty developed, adapted and piloted simulation cases, skills checklists and knowledge questionnaires. The barriers to error disclosure assessment (BEDA) tool served as our confidence survey. Five additional questions developed and piloted by the research team were administered with the BEDA to assess learner confidence with peer–peer disclosure. Pre- and post-intervention written measures of knowledge and confidence (BEDA) were obtained for both iterations of the curriculum. Assessment of observed clinical skills was scored by the involved SP (standardized patient) immediately following the RCDP. An Fourteen learners completed all curricular components. Learners demonstrated statistically significant improvement in their confidence in medical error disclosure ( This pilot data suggest that the HEEAL intervention provides an effective and efficient way for medical educators to teach senior medical students how to provide competent error disclosure to both patients and peers.

Financial Stability and Public Confidence in Banks

We use a novel, household opinion-based measure – Public Confidence in a Bank – to explore the role of bank-level and industry-level determinants of retail customers’ trust in commercial banks. Our study tracks bank-month confidence measures during the 1/1/2010–1/1/2019 period in an unbalanced sample of 181 closely monitored large Russian banks that collectively account for the supermajority of the country’s banking assets. We find that contrary to common expectations, public confidence in a bank is heavily driven by the industry-level financial stability indicators, while bank-level risk measures play only a minimal – or, at best, supplementary – role. This result reveals the important role of overall banking sector stability in determining public perception of the safety and soundness of individual banks and justifies the leading role of industry-level stability in ensuring trust in banks.

Advancing Our Understanding of Martian Proton Aurora Through a Coordinated Multi‐Model Comparison Campaign

AbstractProton aurora are the most commonly observed yet least studied type of aurora at Mars. In order to better understand the physics and driving processes of Martian proton aurora, we undertake a multi‐model comparison campaign. We compare results from four different proton/hydrogen precipitation models with unique abilities to represent Martian proton aurora: Jolitz model (3‐D Monte Carlo), Kallio model (3‐D Monte Carlo), Bisikalo/Shematovich et al. model (1‐D kinetic Monte Carlo), and Gronoff et al. model (1‐D kinetic). This campaign is divided into two steps: an inter‐model comparison and a data‐model comparison. The inter‐model comparison entails modeling five different representative cases using similar constraints in order to better understand the capabilities and limitations of each of the models. Through this step we find that the two primary variables affecting proton aurora are the incident solar wind particle flux and velocity. In the data‐model comparison, we assess the robustness of each model based on its ability to reproduce a proton aurora observation. All models are able to effectively simulate the general shape of the data. Variations in modeled intensity and peak altitude can be attributed to differences in model capabilities/solving techniques and input assumptions (e.g., cross sections, 3‐D vs. 1‐D solvers, and implementation of the relevant physics and processes). The good match between the observations and multiple models gives a measure of confidence that the appropriate physical processes and their associated parameters have been correctly identified and provides insight into the key physics that should be incorporated in future models.

Framework to Study Migration Decisions Using Call Detail Record (CDR) Data

This article addresses the challenges of using call detail record (CDR) data to study migration. Repurposing CDR data for this task have many advantages, including the lower costs of data collection and the potential for contemporaneous analysis. We present a framework for the repurposing and analysis of CDR data. We identify the home location of a subscriber, with corresponding confidence measures, and determine if the subscriber is a definite migrant, likely migrant, likely nonmigrant, or definite nonmigrant. A predictive model then uses mobility and social network features, extracted from the CDR data, to predict the individual decision to migrate. We are the first to address the challenging task of predicting the migration decision at the individual level. We also provide insight into features that can have an impact on the decision to migrate. An in-depth evaluation using CDR data from two provinces in Sri Lanka provides a granular map of migrant inflow and outflow. The success of our prediction model and the insights gained from the evaluation prepare the way for the repurposing of CDR data for social good with a focus on migration.

Accelerating Research Careers in Science Through Early Mentored Research Experiences for Undergraduates and Masters' Students at the University of California, San Francisco Center for AIDS Research.

Investments early in the academic pathway are essential to increasing the diversity of the HIV research workforce. Applied mentored research experiences can advance research skills, self-efficacy, and retention in science among scholars considered to be underrepresented minorities. The UCSF Center for AIDS Research Scholars Program is a 16-week program that pairs URM undergraduate and masters-level students from San Francisco State University with URM investigators from the UCSF. The program includes one-on-one mentorship on an independent research project, a core-curriculum on research methods and HIV-specific topics, and clinical shadowing. We evaluated program outcomes and areas for improvement with preprogram and postprogram survey and qualitative data from scholars and mentors from June to October 2022. Outcomes included interest in HIV research and growth on a 20-item measure of research skills, knowledge, and confidence. Six URM scholars enrolled in the inaugural cohort and were paired with 6 mentors. Preprogram and postprogram evaluations showed gains in interest in HIV research, 0% preprogram and 100% of scholars postprogram reporting much or extensive interest in HIV research, and gains across all domains from self-confidence, skills, and clarification of a research career path. Qualitative findings noted the importance of peer support and interpersonal features of mentors, including treating them like equals. Areas for improvement included more opportunities for primary data collection and specific training around mentoring undergraduates for mentors. The UCSF Center for AIDS Research Scholars Program was successful in building foundational skills, knowledge, confidence, and interest in HIV research among URM undergraduates and masters-level students.

Understanding consumer confidence evolution in response to adverse shocks and how marketing contributes to business s success in economic downturns

Purpose- Firstly, this paper examines how current measurements of consumer confidence might be used to forecast future household spending by using economic variable (i.e. unemployment rate, national adjusted income per capita) data in China, with the help of statistic tools (i.e. Python and Pandas) for the job of data cleaning and analyzing. Secondly, I will focus on the ways in which businesses alter their marketing mix in the face of economic hardship to survive by understanding the importance of utilizing effective marketing strategies during economic downturns. Methodology- This paper examines the predictive performance of consumer confidence index on change in consumption growth by constructing three different OLS regression models and by integrating several existing proposals for effective marketing strategies for businesses in times of low consumer confidence to help business managers to make wise and effective response to economic downturns. Findings- It is shown both 1 year lagged consumer confidence and 3 years lagged consumer confidence are good predictors for current change in consumption patterns, whereas 2 years lagged consumer confidence shows negative correlation with consumption change, and consumer confidence have their own predictive power regardless of the macroeconomic variables. Conclusion- Therefore, this paper contributes to the existing literature by providing empirical findings that consumer confidence cannot always fulfill the role of anticipating consumption change and by further providing concrete policy recommendations for consumer confidence’ revival during recessions in an effort to fill the gap. Keywords: Consumer confidence, marketing, consumption, consumer behavior, forecasting. JEL Codes: B16, B22, E21, E27, M21, M31

A Multi-Objective Approach for Optimizing the Layout of Additional Boreholes in Mineral Exploration

Accurate subsurface exploration requires an optimal network of boreholes. This paper proposes a multi-objective approach to optimize the layout of additional exploratory boreholes. In order to illustrate this approach, geochemical analyses of core samples at the eastern part of the Kahang copper deposit, Central Iran, were used. A measure of the grade uncertainty (kriging standard deviation) and a confidence measure on the ore/waste classification were first calculated by implementing ordinary and indicator kriging. An ore value function was then determined to measure the total value of each block by considering the grades of all the effective variables and their ore membership degree derived from a fuzzy treatment of the grades. Finally, a misclassification cost is defined for each block based on the expected economic effects of ore loss and waste dilution. As a result, an index for the selection of additional boreholes was introduced in order to maximize the kriging standard deviation, the ore misclassification cost, and the ore value and to minimize the confidence measure. Applied to the Kahang copper deposit, this index allowed the prioritization of areas for infill sampling, leading to the recommendation for eight vertical and two directional additional boreholes.

SelANet: decision-assisting selective sleep apnea detection based on confidence score

BackgroundOne of the most common sleep disorders is sleep apnea syndrome. To diagnose sleep apnea syndrome, polysomnography is typically used, but it has limitations in terms of labor, cost, and time. Therefore, studies have been conducted to develop automated detection algorithms using limited biological signals that can be more easily diagnosed. However, the lack of information from limited signals can result in uncertainty from artificial intelligence judgments. Therefore, we performed selective prediction by using estimated respiratory signals from electrocardiogram and oxygen saturation signals based on confidence scores to classify only those sleep apnea occurrence samples with high confidence. In addition, for samples with high uncertainty, this algorithm rejected them, providing a second opinion to the clinician.MethodOur developed model utilized polysomnography data from 994 subjects obtained from Massachusetts General Hospital. We performed feature extraction from the latent vector using the autoencoder. Then, one dimensional convolutional neural network—long short-term memory (1D CNN-LSTM) was designed and trained to measure confidence scores for input, with an additional selection function. We set a confidence score threshold called the target coverage and performed optimization only on samples with confidence scores higher than the target coverage. As a result, we demonstrated that the empirical coverage trained in the model converged to the target coverage.ResultTo confirm whether the model has been optimized according to the objectives, the coverage violation was used to measure the difference between the target coverage and the empirical coverage. As a result, the value of coverage violation was found to be an average of 0.067. Based on the model, we evaluated the classification performance of sleep apnea and confirmed that it achieved 90.26% accuracy, 91.29% sensitivity, and 89.21% specificity. This represents an improvement of approximately 7.03% in all metrics compared to the performance achieved without using a selective prediction.ConclusionThis algorithm based on selective prediction utilizes confidence measurement method to minimize the problem caused by limited biological information. Based on this approach, this algorithm is applicable to wearable devices despite low signal quality and can be used as a simple detection method that determine the need for polysomnography or complement it.

Floor-to-Stand Performance Among People Following Stroke.

Studies have examined floor-to-stand performance in varied adult populations both quantitatively and qualitatively. Despite an elevated risk of falls and inability to independently return to stand after a fall, few have examined the ability to stand from the floor in patients recovering from stroke. There were 2 objectives of the study: to identify the relationships between floor-to-stand performance using a timed supine-to-stand test (TSS) and physical performance measures of gait, balance, and balance confidence among persons in the subacute phase after stroke; and to analyze descriptive strategies used in the completion of the TSS. A cross-sectional design was implemented. Fifty-eight adults (mean age = 59.2 [standard deviation (SD) = 13.9] years; 34 [58.6%] men) who were in the subacute phase after ischemic or hemorrhagic stroke and who could stand from the floor with no more than supervision completed the TSS and physical performance assessments. The median time to complete the TSS in our sample was 13.0 (interquartile range = 15.5) seconds. TSS time was significantly correlated with physical performance tests, including the Timed "Up & Go" Test (ρ = 0.70), gait speed (ρ = -0.67), Dynamic Gait Index (ρ = -0.52), and Activities-Specific Balance Confidence Scale (ρ = -0.43). Thirty-two percent of the variance in TSS time was attributed to Timed "Up & Go" Test time and the use of the quadruped position to transition to standing. Participants who used a gait device were more likely to use a chair during rise to stand. The TSS demonstrates concurrent validity with physical performance measures. Findings serve to improve functional mobility examination after stroke and to formulate effective treatment interventions to improve floor-to-stand performance.

Framework for Classifying Explainable Artificial Intelligence (XAI) Algorithms in Clinical Medicine.

Artificial intelligence (AI) applied to medicine offers immense promise, in addition to safety and regulatory concerns. Traditional AI produces a core algorithm result, typically without a measure of statistical confidence or an explanation of its biological-theoretical basis. Efforts are underway to develop explainable AI (XAI) algorithms that not only produce a result but also an explanation to support that result. Here we present a framework for classifying XAI algorithms applied to clinical medicine: An algorithm's clinical scope is defined by whether the core algorithm output leads to observations (eg, tests, imaging, clinical evaluation), interventions (eg, procedures, medications), diagnoses, and prognostication. Explanations are classified by whether they provide empiric statistical information, association with a historical population or populations, or association with an established disease mechanism or mechanisms. XAI implementations can be classified based on whether algorithm training and validation took into account the actions of health care providers in response to the insights and explanations provided or whether training was performed using only the core algorithm output as the end point. Finally, communication modalities used to convey an XAI explanation can be used to classify algorithms and may affect clinical outcomes. This framework can be used when designing, evaluating, and comparing XAI algorithms applied to medicine.

The hazards of dealing with response time outliers.

The presence of outliers in response times can affect statistical analyses and lead to incorrect interpretation of the outcome of a study. Therefore, it is a widely accepted practice to try to minimize the effect of outliers by preprocessing the raw data. There exist numerous methods for handling outliers and researchers are free to choose among them. In this article, we use computer simulations to show that serious problems arise from this flexibility. Choosing between alternative ways for handling outliers can result in the inflation of p-values and the distortion of confidence intervals and measures of effect size. Using Bayesian parameter estimation and probability distributions with heavier tails eliminates the need to deal with response times outliers, but at the expense of opening another source of flexibility.

Optimal selection of suitable templates in protein interface prediction.

Molecular-level classification of protein-protein interfaces can greatly assist in functional characterization and rational drug design. The most accurate protein interface predictions rely on finding homologous proteins with known interfaces since most interfaces are conserved within the same protein family. The accuracy of these template-based prediction approaches depends on the correct choice of suitable templates. Choosing the right templates in the immunoglobulin superfamily (IgSF) is challenging because its members share low sequence identity and display a wide range of alternative binding sites despite structural homology. We present a new approach to predict protein interfaces. First, template-specific, informative evolutionary profiles are established using a mutual information-based approach. Next, based on the similarity of residue level conservation scores derived from the evolutionary profiles, a query protein is hierarchically clustered with all available template proteins in its superfamily with known interface definitions. Once clustered, a subset of the most closely related templates is selected, and an interface prediction is made. These initial interface predictions are subsequently refined by extensive docking. This method was benchmarked on 51 IgSF proteins and can predict nontrivial interfaces of IgSF proteins with an average and median F-score of 0.64 and 0.78, respectively. We also provide a way to assess the confidence of the results. The average and median F-scores increase to 0.8 and 0.81, respectively, if 27% of low confidence cases and 17% of medium confidence cases are removed. Lastly, we provide residue level interface predictions, protein complexes, and confidence measurements for singletons in the IgSF. Source code is freely available at: https://gitlab.com/fiserlab.org/interdct_with_refinement.

Validation ofthe eHEALTH Literacy Scale (Eheals) With Military Service Members.

Medical readiness of individual service members (SMs) is an integral component of operational readiness. Electronic Health (eHealth) literacy, here defined as the ability to seek out, find, evaluate, and appraise, integrate, and apply what is gained in electronic environments toward solving a health problem, is a key skill to enhance individual medical readiness. The World Health Organization (WHO) has identified health literacy as an important social determinant of health. The Internet enables individuals to access information virtually anywhere, at any place, any time, which is especially beneficial for SMs who serve around the globe, often isolated from modern health care facilities. This study examined the utility of the eHealth literacy scale (eHeals) as a potentially valuable tool to assist commanders and medical teams for assessing the health literacy of SMs. The main objective of our study was to examine the validity of the three-factor eHEALS measure for an active duty sample, using confirmatory factorial analysis. Our findings support the validity of the eHEALS measure of electronic health literacy among military SMs, potentially identifying a means of measuring SMs' health literacy, and a target for intervention to increase medical readiness, well-being, and overall quality of life for individual SMs. The study highlights a brief 6-item assessment tool that can quickly and efficiently be added to the current validated health and well-being measures for military personnel. This brief assessment can provide a measure of knowledge, skills, and confidence regarding ehealth information leading to better-informed health care decisions in the military context. These findings are especially relevant given the military's current emphasis on modernization and the need to put people first in order to build the overall team and improve unit effectiveness.

Gaussian Processes for real-time 3D motion and uncertainty estimation during MR-guided radiotherapy.

Respiratory motion during radiotherapy causes uncertainty in the tumor's location, which is typically addressed by an increased radiation area and a decreased dose. As a result, the treatments' efficacy is reduced. The recently proposed hybrid MR-linac scanner holds the promise to efficiently deal with such respiratory motion through real-time adaptive MR-guided radiotherapy (MRgRT). For MRgRT, motion-fields should be estimated from MR-data and the radiotherapy plan should be adapted in real-time according to the estimated motion-fields. All of this should be performed with a total latency of maximally 200 ms, including data acquisition and reconstruction. A measure of confidence in such estimated motion-fields is highly desirable, for instance to ensure the patient's safety in case of unexpected and undesirable motion. In this work, we propose a framework based on Gaussian Processes to infer 3D motion-fields and uncertainty maps in real-time from only three readouts of MR-data. We demonstrated an inference frame rate up to 69 Hz including data acquisition and reconstruction, thereby exploiting the limited amount of required MR-data. Additionally, we designed a rejection criterion based on the motion-field uncertainty maps to demonstrate the framework's potential for quality assurance. The framework was validated in silico and in vivo on healthy volunteer data (n=5) acquired using an MR-linac, thereby taking into account different breathing patterns and controlled bulk motion. Results indicate end-point-errors with a 75th percentile below 1 mm in silico, and a correct detection of erroneous motion estimates with the rejection criterion. Altogether, the results show the potential of the framework for application in real-time MR-guided radiotherapy with an MR-linac.

Deep Confidence Propagation Stereo Network

Stereo matching depth estimation based on rectified image pairs is of great importance to many computer vision tasks such as vehicle navigation and autonomous driving. Confidence measures are typically used to refine stereo matching results, which provides robustness and efficiency for disparity estimation. However, previous learning-based confidence methods for stereo matching usually use the middle results or composition as a post-processing step to refine the stereo matching results. This cannot be optimized end-to-end and the performance is limited by the quality of the tri-modal output. To handle this issue, in this paper, we pursue an end-to-end hierarchical architecture and propose a differentiable confidence propagation (DCP) model of a cost aggregation network for stereo matching. The DCP model is integrated into an end-to-end neural network hierarchical architecture to guide matching cost volume aggregation. More specifically, to better represent the similarity of left and right feature maps, we extract unary context feature maps with an effective attention mechanism for matching cost construction. Moreover, we aggregate the cost volume with the multiple stacked DCP cost aggregation (DCPCA) networks to generate a more reliable and finer cost volume. This network suppresses multi-level disparity maps. Each output disparity is supervised with different training weights to learn in a coarse-to-fine way. Our method outperforms previous methods on the Sceneflow dataset by achieving the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.6735px$</tex-math> </inline-formula> EPE error, achieving 1.53% D1-all metric of Non-occluded pixels regions and 0.72% Non-occluded pixels of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5px$</tex-math> </inline-formula> metric on KITTI 2015 and 2012 dataset. Extensive experiments carried out on the KITTI Stereo benchmarks demonstrate that our DCPCA-Net can significantly minimize the trade-off between accuracy and efficiency for stereo matching.

Efficient thermodynamic modelling with uncertainty quantification of the Ag–Cu–Co system from its sub-binary systems

Thermodynamic modelling of the multi-component system from the low-order subsystems in a self-consistent way and a quantitative description of the uncertainty in the modelling are critical to multicomponent alloy design. The Ag–Cu, Ag–Co, and Co–Cu binary systems are the subsystems to build the Ag–Cu–Co ternary system. They are important for the Ag–Cu-based lead-free solders and represent typical eutectic, monotectic, and peritectic systems. In this work, we present a case study of modelling and uncertainty quantification with the recently developed Extensible Self-optimizing Phase Equilibria Infrastructure (ESPEI). Enthalpies of mixing at 0 K were calculated by density functional theory (DFT)-based first-principles method using the special quasi-random structures (SQS) to compensate for the lack of thermochemical solid solution data. Incorporating the collected experimental data, the three sub-binary systems were successfully assessed, and the Ag–Cu–Co ternary system was subsequently modelled. The calculated phase diagrams of the ternary system and the sub-binary system agree with the experiments well. The reliability of the obtained models was further validated by the “residual driving force” approach. The probability distribution of the mono-variant points reside within composition-temperature space is also determined by the Phase Diagram Uncertainty Quantification (PDUQ) tool, providing confidence measures in the design of solder alloys.

On the Relationship Between Reliability Diagrams and the “Signal‐To‐Noise Paradox”

AbstractThe “signal‐to‐noise paradox” for seasonal forecasts of the winter North Atlantic Oscillation (NAO) is often described as an “underconfident” forecast and measured using the ratio‐of‐predictable components (RPCs) metric. However, comparison of RPC with other measures of forecast confidence, such as spread‐error ratios, can give conflicting impressions, challenging this informal description. We show, using a linear statistical model, that the “paradox” is equivalent to a situation where the reliability diagram of any percentile forecast has a slope exceeding 1. The relationship with spread‐error ratios is shown to be far less direct. We furthermore compute reliability diagrams of winter NAO forecasts using seasonal hindcasts from the European Centre for Medium‐range Weather Forecasts and the UK Meteorological Office. While these broadly exhibit slopes exceeding 1, there is evidence of asymmetry between upper and lower terciles, indicating a potential violation of linearity/Gaussianity. The limitations and benefits of reliability diagrams as a diagnostic tool are discussed.