Reliability Metrics Research Articles

A review of examples and opportunities to quantify the grid reliability and resilience impacts of energy efficiency

Grid reliability and resilience are foundational to meeting electricity needs and have significant economic and societal impacts. Energy efficiency can help meet grid reliability objectives and improve resilience, but metrics and methods used today may not fully recognize these benefits. This paper explains how existing planning processes for bulk power and distribution systems capture the impact of energy efficiency on power system reliability and resilience, with illustrative examples. We identify limitations in using existing reliability and resilience metrics to quantify efficiency and other distributed energy resource (DER) benefits. The paper concludes with opportunities for regulators and utilities to enhance planning practices to better capture the reliability and resilience value of energy efficiency and identifies research needs.

Are different station formats assessing different dimensions in multiple mini-interviews? Findings from the Canadian integrated French multiple mini-interviews

BackgroundMultiple mini-interviews (MMI) are used to assess non-academic attributes for selection in medicine and other healthcare professions. It remains unclear if different MMI station formats (discussions, role-plays, collaboration) assess different dimensions.MethodsBased on station formats of the 2018 and 2019 Integrated French MMI (IFMMI), which comprised five discussions, three role-plays and two collaboration stations, the authors performed confirmatory factor analysis (CFA) using the lavaan 0.6-5 R package and compared a one-factor solution to a three-factor solution for scores of the 2018 (n = 1438) and 2019 (n = 1440) cohorts of the IFMMI across three medical schools in Quebec, Canada.ResultsThe three-factor solution was retained, with discussions, role-plays and collaboration stations all loading adequately with their scores. Furthermore, all three factors had moderate-to-high covariance (range 0.44 to 0.64). The model fit was also excellent with a Comparative fit index (CFI) of 0.983 (good if > 0.9), a Tucker Lewis index of 0.976 (good if > 0.95), a Standardized Root Mean Square Residual of 0.021 (good if < .08) and a Root Mean Square Error of 0.023 (good if < 0.08) for 2018 and similar results for 2019. In comparison, the single factor solution presented a lower fit (CFI = 0.819, TLI = 0.767, SRMR = 0.049 and RMSEA = 0.070).ConclusionsThe IFMMI assessed three dimensions that were related to stations formats, a finding that was consistent across two cohorts. This suggests that different station formats may be assessing different skills, and has implications for the choice of appropriate reliability metrics and the interpretation of scores. Further studies should try to characterize the underlying constructs associated with each station format and look for differential predictive validity according to these formats.

Large-eddy simulations with ClimateMachine v0.2.0: a new open-source code for atmospheric simulations on GPUs and CPUs

Abstract. We introduce ClimateMachine, a new open-source atmosphere modeling framework which uses the Julia language and is designed to be scalable on central processing units (CPUs) and graphics processing units (GPUs). ClimateMachine uses a common framework both for coarser-resolution global simulations and for high-resolution, limited-area large-eddy simulations (LESs). Here, we demonstrate the LES configuration of the atmosphere model in canonical benchmark cases and atmospheric flows using a total energy-conserving nodal discontinuous Galerkin (DG) discretization of the governing equations. Resolution dependence, conservation characteristics, and scaling metrics are examined in comparison with existing LES codes. They demonstrate the utility of ClimateMachine as a modeling tool for limited-area LES flow configurations.

Multiobjective reconfiguration of unbalanced distribution networks using improved transient search optimization algorithm considering power quality and reliability metrics

This paper proposes a new intelligent algorithm named improved transient search optimization algorithm (ITSOA) integrated with multiobjective optimization for determining the optimal configuration of an unbalanced distribution network. The conventional transient search optimization algorithm (TSOA) is improved with opposition learning and nonlinearly decreasing strategies for enhancing the convergence to find the global solution and obtain a desirable balance between local and global search. The multiobjective function includes different objectives such as power loss reduction, enhancement of voltage sag and unbalance, and network energy not supplied minimization. The decision variables of the reconfiguration problem including opened switches or identification of optimal network configuration are determined using ITSOA and satisfying operational and radiality constraints. The proposed methodology is implemented on unbalanced 13-bus and 118-bus networks. The results showed that the proposed ITSOA is capable to find the optimal network configuration for enhancing the different objectives in loading conditions. The results cleared the proposed methodology's good effectiveness, especially in power quality and reliability enhancement, without compromising the different objectives. Comparing ITSOA to conventional TSOA, particle swarm optimization (PSO), gray wolf optimization (GWO), bat algorithm (BA), manta ray foraging optimization (MRFO), and ant lion Optimizer (ALO), and previous approaches, it is concluded that ITSOA in improving the different objectives.

The State of Life on Land (SDG 15) in the United Arab Emirates

In 2015, the United Nations (UN) defined 17 sustainable development goals (SDGs) as a road path to sustainable development in the world. Achieving these SDGs are among the top items on the agenda of several state organizations, including the United Arab Emirates. This article analyzes the UAE’s recent performance in achieving SDG 15, labeled as Life on Land. Using the data from international databases, namely the UN SDG Database and the Bertelsmann Stiftung SDG-index, we analyze the indicators that measure the UAE’s performance towards achieving SDG 15. This research sets a strategy for preserving biodiversity in the UAE and attaining the relevant goals to protect life on land. The findings suggest that while UAE is motivated to achieve the goals stated in SDG15, significant challenges remain ahead. Specifically, many of the relevant indicators are missing, making it hard to measure country performance based on reliable metrics.

Designing phase I oncology dose escalation using dose-exposure-toxicity models as a complementary approach to model-based dose-toxicity models.

One of the objectives of oncology phase I dose‐escalation studies has been to determine the maximum tolerated dose (MTD). Although MTD is no longer set as the dose for further development in contemporary oncology drug development, MTD determination is still important for informing the therapeutic index. Bayesian adaptive model‐based designs are becoming mainstream in oncology first‐in‐human trials. Herein, we illustrate via simulations the use of systemic exposure in Bayesian adaptive dose–toxicity models to estimate MTD. We extend traditional dose–toxicity models to incorporate pharmacokinetic exposure, which provides information on exposure–toxicity relationships. We pursue dose escalation until the maximum tolerated exposure (corresponding to the MTD) is reached. By leveraging pharmacokinetics, dose escalation considers exposure and interindividual variability on a continuous rather than discrete domain, offering additional information for dose‐escalation decisions. To demonstrate this, we generated 1000 simulations (starting dose of 1/25th the reference dose and six dose levels) for several different scenarios. Both rule‐based and model‐based designs were compared using metrics of potential safety, accuracy, and reliability. The mean results over simulations and different toxicity scenarios showed that model‐based designs were better than rule‐based methods and that exposure–toxicity model‐based methods have the potential to valuably complement dose–toxicity model‐based methods. Exposure–toxicity model‐based methods had decreased underdose risk accompanied by a relatively smaller increase in overdose risk, resulting in improved net reliability. MTD estimation accuracy was compromised when exposure variability was large, emphasizing the importance of appropriate control of pharmacokinetic variability in phase I dose‐escalation studies.

Radiographic and Serologic Response to First-Line Chemotherapy in Unresected Localized Pancreatic Cancer.

This study aimed to determine the clinical relevance of putative radiographic and serologic metrics of chemotherapy response in patients with localized pancreatic cancer (LPC) who do not undergo pancreatectomy. Studies evaluating the response of LPC to systemic chemotherapy have focused on histopathologic analyses of resected specimens, but such specimens are not available for patients who do not undergo resection. We previously showed that changes in tumor volume and CA 19-9 levels provide a clinical readout of histopathologic response to preoperative therapy. Our institutional database was searched for patients with LPC who were treated with first-line chemotherapy between January 2010 and December 2017 and did not undergo pancreatectomy. Radiographic response was measured using RECIST 1.1 and tumor volume. The volume of the primary tumor was compared between pretreatment and posttreatment images. The percentage change in tumor volume (%Δvol) was calculated as a percentage of the pretreatment volume. Serologic response was measured by comparing pretreatment and posttreatment CA 19-9 levels. We established 3 response groups by combining these metrics: (1) best responders with a decline in %Δvol in the top quartile and in CA 19-9, (2) nonresponders with an increase in %Δvol and in CA 19-9, and (3) other patients. This study included 329 patients. Individually, %Δvol and change in CA 19-9 were associated with overall survival (OS) (P≤.1), but RECIST 1.1 was not. In all, 73 patients (22%) were best responders, 42 (13%) were nonresponders, and there were 214 (65%) others. Best responders lived significantly longer than nonresponders and others (median OS, 24 vs 12 vs 17 months, respectively; P<.01). A multivariable model adjusting for type of chemotherapy regimen, number of chemotherapy doses, and receipt of radiotherapy showed that best responders had longer OS than did the other cohorts (hazard ratio [HR], 0.35; 95% CI, 0.21-0.58 for best responders, and HR, 0.55; 95% CI, 0.37-0.83 for others). Changes in tumor volume and serum levels of CA 19-9-but not RECIST 1.1-represent reliable metrics of response to systemic chemotherapy. They can be used to counsel patients and families on survival expectations even if pancreatectomy is not performed.

On the explicit formulation of reliability assessment of distribution systems with unknown network topology: Incorporation of DG, switching interruptions, and customer-interruption quantification

This paper presents an original approach for the evaluation of reliability of active distribution networks with unknown topology. Built upon novel reformulations of conventional definitions for distribution reliability indices, the dependence of system-oriented reliability metrics on network topology is explicitly formulated using a set of mixed-integer linear expressions. Unlike previously reported works also modeling mathematically the relationship between reliability assessment and network topology, the proposed approach allows considering the impact of distributed generation (DG) while accounting for switching interruptions. Moreover, for the first time in the emerging closely related literature, the nonlinearity and nonconvexity of the customer average interruption duration index are precisely characterized. The proposed mixed-integer linear model is suitable for various distribution optimization problems in which the operational topology of the network is not specified a priori. Aiming to exemplify its potential applicability, the proposed formulation is incorporated into a distribution reconfiguration optimization problem. The effectiveness and practicality of the proposed approach are numerically illustrated using various test networks.

A signature-based approach for reliability modeling and maintenance optimization of a coherent system

In reliability context, mean residual lifetime (MRL) models and proportional hazard (PH) models are widely used for reliability analysis and failure prediction. Since maintenance actions typically are based on aforementioned reliability metrics, recent researches have been focused on the extensions of these two models for accurate decision-making. In this paper, using signature arguments, we propose a state-dependent mean residual life (SDMRL) model and demonstrate its applicability in both reliability and maintenance field. In preference to existing models, the SDMRL modeling can be applied to coherent systems and account both changes in the operating environment and for the natural ageing process of the system. Assuming a bivariate threshold-type policy, the paper aims at minimizing the long-run average maintenance cost per unit time by determining appropriate inspection intervals and optimal replacement policy. The proposed model and the behavior of the optimal solutions as the model parameters vary are illustrated through numerical examples.

Sparsity Dependent Metrics Depict Alteration of Brain Network Connectivity in Parkinson's Disease.

To date, regional brain atrophy unfolded using neuroimaging methods is observed to be the signature of Parkinson's disease (PD). In addition, graph theory-based studies are proving altered structural connectivity in PD. This motivated us to employ regional grey matter volume of PD patients (N=70) for comparative network analysis with an equal number of age- and gender-matched healthy controls (HC). In the current study, normalized grey matter maps obtained from structural magnetic resonance imaging (sMRI) were parcellated into 56 ROI (regions of interest) for construction of symmetric matrix using partial correlation between every pair of regional grey matter volumes. Sparsity thresholding was used to binarize the matrices and MATLAB functions from brain connectivity toolbox were employed to obtain the connectivity metrics. We observed PD with a significantly lower clustering coefficient as well as local efficiency at higher sparsities (above 0.9 and 0.84, respectively) with p<0.05. The right fusiform gyrus was found to be the conserved hub, besides disruption of four hubs and regeneration of five other hubs. Lower clustering coefficient and local efficiency were indicative of reduced local integration and information processing, respectively. Hence, we suggest that global clustering coefficient and local efficiency could have a pivotal role in evaluating network topology. Thereby, our findings confirmed impairment of normal structural brain network topology reflecting disconnectivity mechanisms in PD. Clinical Relevance - Analyzing structural brain connectivity in Parkinson's disease might provide the researchers and clinicians with a signature pattern of the disease to discriminate patients from normal controls.

A customized multi-cycle model for measuring the sustainability of circular pathways in agri-food supply chains

Circular economy (CE) is claimed to be a promising pathway to achieve the Sustainable Development Goals (SDGs), but a reliable metric is needed to validate closed-loop strategies by measuring sustainability performances together with the degree of circularity. A significant contribution is offered by Life Cycle (LC) scholars in terms of methodological advances and operational tools for different sectors, also those more complex such as the agro-industrial systems that encompass biological and anthropogenic variables at different scales. However, to date, LC methodologies have not yet answered how to model the complexity of circular pathways. LC evaluations are often modelled for cradle-to-grave analyses, while a circularity evaluation would require an extension of the system boundaries to more interconnected life cycles, orienting towards a cradle-to-cradle perspective. This research gap led us to propose a multi-cycle approach with expanded assessment boundaries, including co-products, into a cradle-to-cradle perspective, in an attempt to internalize circularity impacts. The customized LC framework here proposed is based on the Life Cycle Assessment (LCA), the Environmental Life Cycle Costing (ELCC) in terms of internal and external costs, and the Social Life Cycle Assessment (SLCA) in terms of Psychosocial Risk Factor (PRF) impact pathway. The model is designed to be applied to the olive-oil sector, which commonly causes significant impacts by generating many by-products whose management is often problematic. Results are expected to show that the customized LC framework proposed can better highlight the environmental and socioeconomic performances of the system of cycles, allowing CE to deliver its promises of sustainability, as the circularity of materials per se is a means, not an end in itself.

Electrical Power Reliability Metrics for the Petrochemical Industry: Applying Electrical Reliability Analytics

Electrical Power Reliability Metrics for the Petrochemical Industry: Applying Electrical Reliability Analytics

A Machine Learning-Based Reliability Evaluation Model for Integrated Power-Gas Systems

This paper proposes a machine learning method for the reliability evaluation of integrated power-gas systems (IPGS) under the uncertain component failure probability distributions. The Random Forest (RF) method is designed to select important features to solve the insufficient quantity of data and the curse of dimensionality problems. The Extreme Gradient Boosting (XGBoost) regression algorithm is developed to quantify the relationship between the uncertain parameters and reliability metrics. Moreover, a ten-fold cross-validation method is employed to further improve the accuracy of the regression model. Simulation results on three test systems show that the proposed method can achieve high accuracy for the reliability evaluation.

Prefrontal cortical connectivity and coupling of infraslow oscillation in the resting human brain: a 2-channel broadband NIRS study.

The resting-state infraslow oscillation (ISO) of the cerebral cortex reflects the neurophysiological state of the human brain. ISO results from distinct vasomotion with endogenic (E), neurogenic (N), and myogenic (M) frequency bands. Quantification of prefrontal ISO in cortical hemodynamics and metabolism in the resting human brain may facilitate the identification of objective features that are characteristic of certain brain disorders. The goal of this study was to explore and quantify the prefrontal ISO of the cortical concentration changes of oxygenated hemoglobin (Δ[HbO]) and redox-state cytochrome c oxidase (Δ[CCO]) as hemodynamic and metabolic activity metrics in all 3 E/N/M bands. Two-channel broadband near-infrared spectroscopy (2-bbNIRS) enabled measurements of the forehead of 26 healthy young participants in a resting state once a week for 5 weeks. After quantifying the ISO spectral amplitude (SA) and coherence at each E/N/M band, several key and statistically reliable metrics were obtained as features: (i) SA of Δ[HbO] at all E/N/M bands, (ii) SA of Δ[CCO] in the M band, (iii) bilateral connectivity of hemodynamics and metabolism across the E and N bands, and (iv) unilateral hemodynamic–metabolic coupling in each of the E and M bands. These features have promising potential to be developed as objective biomarkers for clinical applications in the future.

Statistical perspectives on reliability of artificial intelligence systems

Artificial intelligence (AI) systems are increasingly popular in many applications. Nevertheless, AI technologies are still developing, and many issues need to be addressed. Among those, the reliability of AI systems needs to be demonstrated so that AI systems can be used with confidence by the general public. In this paper, we provide statistical perspectives on the reliability of AI systems, focusing on the time dimension. That is, the system can perform its designed functionality for the intended period of time. We introduce a so-called “SMART” statistical framework for AI reliability research, which includes five components: Structure of the system, Metrics of reliability, Analysis of failure causes, Reliability assessment, and Test planning. We review traditional methods in reliability data analysis and software reliability, and discuss how those existing methods can be transformed for reliability modeling and assessment of AI systems. Different from traditional reliability studies, the focus of AI reliability is on the software system to include the training data. Thus, we describe recent developments in modeling and analysis of AI reliability for software systems. The paper outlines statistical research challenges in this area, including out-of-distribution detection, the effect of the training set, adversarial attacks, model accuracy, and uncertainty quantification. We discuss how those topics can be related to AI reliability, with illustrative examples. The final element of SMART (test planning), is critical for the demonstration of AI reliability. Therefore we discuss data collection and testing planning, highlighting methods for improving system design in order to achieve higher AI reliability. The paper closes with some concluding remarks.

A longitudinal resource for studying connectome development and its psychiatric associations during childhood

Most psychiatric disorders are chronic, associated with high levels of disability and distress, and present during pediatric development. Scientific innovation increasingly allows researchers to probe brain-behavior relationships in the developing human. As a result, ambitions to (1) establish normative pediatric brain development trajectories akin to growth curves, (2) characterize reliable metrics for distinguishing illness, and (3) develop clinically useful tools to assist in the diagnosis and management of mental health and learning disorders have gained significant momentum. To this end, the NKI-Rockland Sample initiative was created to probe lifespan development as a large-scale multimodal dataset. The NKI-Rockland Sample Longitudinal Discovery of Brain Development Trajectories substudy (N = 369) is a 24- to 30-month multi-cohort longitudinal pediatric investigation (ages 6.0–17.0 at enrollment) carried out in a community-ascertained sample. Data include psychiatric diagnostic, medical, behavioral, and cognitive phenotyping, as well as multimodal brain imaging (resting fMRI, diffusion MRI, morphometric MRI, arterial spin labeling), genetics, and actigraphy. Herein, we present the rationale, design, and implementation of the Longitudinal Discovery of Brain Development Trajectories protocol.

Identifying High-accuracy Regions in Traffic Camera Images to Enhance the Estimation of Road Traffic Metrics: A Quadtree-based Method

The growing number of real-time camera feeds in urban areas has made it possible to provide high-quality traffic data for effective transportation planning, operations, and management. However, deriving reliable traffic metrics from these camera feeds has been a challenge because of the limitations of current vehicle detection techniques, as well as the various camera conditions, such as height and resolution. In this work, a quadtree-based algorithm is developed to continuously partition the image extent until only regions with high detection accuracy remain. These regions are referred to as high-accuracy identification regions (HAIRs) in this paper. We demonstrate how the use of HAIRs can improve the accuracy of traffic density estimates using images from traffic cameras at different heights and resolutions in Central Ohio. Our experiments show that the proposed algorithm can be used to derive robust HAIRs in which vehicle detection accuracy is 41% higher than that in the original image extent. The use of HAIRs also significantly improves the traffic density estimation with an overall decrease of 49% in root mean squared error.

Reliability Analysis Based on Mixture of Lindley Distributions with Artificial Neural Network

AbstractThe study of reliability analysis of mixture model is essential in confirming the quality of devices, equipment, and electronic tube flops etc. In recent years, statisticians have developed more interest in mixture model research, notably in the last decade, without taking into account the issue of modeling the metrics of reliability of mixture models using artificial neural networks. In the present study, the influence of pertinent parameters on reliability metrics is studied. The effect of components and mixing parameters for failure function, reversed hazard rate function, mean time to failure, hazard rate function, mean inactivity time, mean residual life, reliability function, Mills Ratio profiles are plotted and discussed. A multi‐layer artificial neural network is developed using the numerical analysis results obtained using four different scenarios. The values extracted from the artificial neural network and the numerical findings of the reliability studies are extensively compared and examined. The deviation rates obtained for the developed artificial neural network model are obtained at values lower than 0.12%. The outcomes demonstrate that neural networks are a powerful and effective mathematical tool that can be used in the reliability analysis of mixing models.

Imperfect Debugging Software Belief Reliability Growth Model Based on Uncertain Differential Equation

Due to the increased dependency of the modern system on software-based system, software reliability has become the primary concern during the software development. To track and measure the software reliability, various software reliability growth models under the framework of probability theory have been proposed. Note that software failures involve lots of epistemic uncertainty, which cannot be depicted well by the probability theory, and debugging processes are usually imperfect due to the complexity and incomplete understanding of software systems. This article deduces an imperfect debugging software belief reliability growth model using the uncertain differential equation under the framework of uncertainty theory, and investigates properties of essential software belief reliability metrics, namely belief reliability, belief reliable time, and mean time between failures based on the belief reliability theory. Estimations for unknown parameters in this model are derived. Real data analyses validate our model and show that it performs better than previous models from the perspective of the sum of square error. A theoretical analysis for these results is presented.

Software Belief Reliability Growth Model Based on Uncertain Differential Equation

Software reliability plays an important role in modern society. To evaluate software reliability, software reliability growth models (SRGMs) investigate the number of software faults in the testing phase. Obviously, testing progresses are inevitably influenced by dynamic indeterministic fluctuations such as the testing effort expenditure, testing efficiency and skill, testing method, and strategy. To model these dynamic fluctuations, several probability theory-based SRGMs are proposed. However, probability theory is suitable for dealing with aleatory uncertainty, but fails to deal with epistemic uncertainty widely existing in software faults. Therefore, this article considers software reliability from a new perspective under the framework of uncertainty theory, which is a new mathematical system different from probability theory, and proposes a software belief reliability growth model (SBRGM) based on uncertain differential equations for the first time. Based on this SBRGM, properties of essential software reliability metrics are investigated under belief reliability theory, which is a brand-new reliability theory. Parameter estimations for unknown parameters in SBRGM are presented. Furthermore, some numerical examples and real data analyses illustrate our methodology in detail, and show that it performs better than several famous probability-based SRGMs in terms of fitting ability and prediction ability. Finally, an optimal software release policy is discussed.