Showing papers on "Reliability (statistics) published in 2021"

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

Abstract: Recently, artificial intelligence and machine learning in general have demonstrated remarkable performances in many tasks, from image processing to natural language processing, especially with the advent of deep learning (DL). Along with research progress, they have encroached upon many different fields and disciplines. Some of them require high level of accountability and thus transparency, for example, the medical sector. Explanations for machine decisions and predictions are thus needed to justify their reliability. This requires greater interpretability, which often means we need to understand the mechanism underlying the algorithms. Unfortunately, the blackbox nature of the DL is still unresolved, and many machine decisions are still poorly understood. We provide a review on interpretabilities suggested by different research works and categorize them. The different categories show different dimensions in interpretability research, from approaches that provide “obviously” interpretable information to the studies of complex patterns. By applying the same categorization to interpretability in medical research, it is hoped that: 1) clinicians and practitioners can subsequently approach these methods with caution; 2) insight into interpretability will be born with more considerations for medical practices; and 3) initiatives to push forward data-based, mathematically grounded, and technically grounded medical education are encouraged.

Web of Science (WoS) and Scopus: The Titans of Bibliographic Information in Today’s Academic World

Abstract: Nowadays, the importance of bibliographic databases (DBs) has increased enormously, as they are the main providers of publication metadata and bibliometric indicators universally used both for research assessment practices and for performing daily tasks. Because the reliability of these tasks firstly depends on the data source, all users of the DBs should be able to choose the most suitable one. Web of Science (WoS) and Scopus are the two main bibliographic DBs. The comprehensive evaluation of the DBs’ coverage is practically impossible without extensive bibliometric analyses or literature reviews, but most DBs users do not have bibliometric competence and/or are not willing to invest additional time for such evaluations. Apart from that, the convenience of the DB’s interface, performance, provided impact indicators and additional tools may also influence the users’ choice. The main goal of this work is to provide all of the potential users with an all-inclusive description of the two main bibliographic DBs by gathering the findings that are presented in the most recent literature and information provided by the owners of the DBs at one place. This overview should aid all stakeholders employing publication and citation data in selecting the most suitable DB.

Psychometric Evaluation of the Arabic Version of the Fear of COVID-19 Scale.

Abstract: Fear is a central emotional response to imminent threats such as the coronavirus-19 disease (COVID-19). The Fear of COVID-19 Scale (FCV-19S) assesses the severity of fear towards COVID-19. The present study examined the psychometric properties of the Arabic version of the FCV-19S. Using a forward-backward translation, the FCV-19S was translated into Arabic. An online survey using the Arabic versions of FCV-19S and the Hospital Anxiety and Depression Scale (HADS) was administered. Reliability and concurrent and confirmatory validity were examined. The dataset consisted of 693 Saudi participants. The internal consistency of the Arabic FCV-19S was satisfactory (α = .88), with sound concurrent validity indicated by significant and positive correlations with HADS (r = .66). The unidimensional structure of the FCV-19S was confirmed. The Arabic version of the FCV-19S is psychometrically robust and can be used in research assessing the psychological impact of COVID-19 among a Saudi adult population.

Determination of Objective Weights Using a New Method Based on the Removal Effects of Criteria (MEREC)

Abstract: The weights of criteria in multi-criteria decision-making (MCDM) problems are essential elements that can significantly affect the results. Accordingly, researchers developed and presented several methods to determine criteria weights. Weighting methods could be objective, subjective, and integrated. This study introduces a new method, called MEREC (MEthod based on the Removal Effects of Criteria), to determine criteria’ objective weights. This method uses a novel idea for weighting criteria. After systematically introducing the method, we present some computational analyses to confirm the efficiency of the MEREC. Firstly, an illustrative example demonstrates the procedure of the MEREC for calculation of the weights of criteria. Secondly, a comparative analysis is presented through an example for validation of the introduced method’s results. Additionally, we perform a simulation-based analysis to verify the reliability of MEREC and the stability of its results. The data of the MCDM problems generated for making this analysis follow a prevalent symmetric distribution (normal distribution). We compare the results of the MEREC with some other objective weighting methods in this analysis, and the analysis of means (ANOM) for variances shows the stability of its results. The conducted analyses demonstrate that the MEREC is efficient to determine objective weights of criteria.

Psychometric properties of the EQ-5D-5L: a systematic review of the literature

Abstract: Purpose: Although the EQ-5D has a long history of use in a wide range of populations, the newer five-level version (EQ-5D-5L) has not yet had such extensive experience. This systematic review summarizes the available published scientific evidence on the psychometric properties of the EQ-5D-5L. Methods: Pre-determined key words and exclusion criteria were used to systematically search publications from 2011 to 2019. Information on study characteristics and psychometric properties were extracted: specifically, EQ-5D-5L distribution (including ceiling and floor), missing values, reliability (test–retest), validity (convergent, known-groups, discriminate) and responsiveness (distribution, anchor-based). EQ-5D-5L index value means, ceiling and correlation coefficients (convergent validity) were pooled across the studies using random-effects models. Results: Of the 889 identified publications, 99 were included for review, representing 32 countries. Musculoskeletal/orthopedic problems and cancer (n = 8 each) were most often studied. Most papers found missing values (17 of 17 papers) and floor effects (43 of 48 papers) to be unproblematic. While the index was found to be reliable (9 of 9 papers), individual dimensions exhibited instability over time. Index values and dimensions demonstrated moderate to strong correlations with global health measures, other multi-attribute utility instruments, physical/functional health, pain, activities of daily living, and clinical/biological measures. The instrument was not correlated with life satisfaction and cognition/communication measures. Responsiveness was addressed by 15 studies, finding moderate effect sizes when confined to studied subgroups with improvements in health. Conclusions: The EQ-5D-5L exhibits excellent psychometric properties across a broad range of populations, conditions and settings. Rigorous exploration of its responsiveness is needed.

Nexus between financial innovation and bankruptcy: evidence from information, communication and technology (ict) sector

Abstract: This study examines the reliability of Altman’s Z-score model to predict the financial failure of the ICT sector in Pakistan. Data for 11 PSE-listed (Pakistan Stock Exchange) ICT companies were col...

Assessing the reliability of species distribution projections in climate change research

Abstract: Aim Forecasting changes in species distribution under future scenarios is one of the most prolific areas of application for species distribution models (SDMs). However, no consensus yet exists on the reliability of such models for drawing conclusions on species distribution response to changing climate. In this study we provide an overview of common modelling practices in the field and assess model predictions reliability using a virtual species approach. Location Global Methods We first provide an overview of common modelling practices in the field by reviewing the papers published in the last 5 years. Then, we use a virtual species approach and three commonly applied SDM algorithms (GLM, MaxEnt and Random Forest) to assess the estimated (cross-validated) and actual predictive performance of models parameterized with different modelling settings and violations of modelling assumptions. Results Our literature review shows that most papers that model species distribution under climate change rely on single models (65%) and small samples ( Main conclusions Our study calls for extreme caution in the application and interpretation of SDMs in the context of biodiversity conservation and climate change research, especially when modelling a large number of species where species-specific model settings become impracticable.

Dynamic fit index cutoffs for confirmatory factor analysis models.

Abstract: Model fit assessment is a central component of evaluating confirmatory factor analysis models and the validity of psychological assessments. Fit indices remain popular and researchers often judge fit with fixed cutoffs derived by Hu and Bentler (1999). Despite their overwhelming popularity, methodological studies have cautioned against fixed cutoffs, noting that the meaning of fit indices varies based on a complex interaction of model characteristics like factor reliability, number of items, and number of factors. Criticism of fixed cutoffs stems primarily from the fact that they were derived from one specific confirmatory factor analysis model and lack generalizability. To address this, we propose a simulation-based method called dynamic fit index cutoffs such that derivation of cutoffs is adaptively tailored to the specific model and data characteristics being evaluated. Unlike previously proposed simulation-based techniques, our method removes existing barriers to implementation by providing an open-source, Web based Shiny software application that automates the entire process so that users neither need to manually write any software code nor be knowledgeable about foundations of Monte Carlo simulation. Additionally, we extend fit index cutoff derivations to include sets of cutoffs for multiple levels of misspecification. In doing so, fit indices can more closely resemble their originally intended purpose as effect sizes quantifying misfit rather than improperly functioning as ad hoc hypothesis tests. We also provide an approach specifically designed for the nuances of 1-factor models, which have received surprisingly little attention in the literature despite frequent substantive interests in unidimensionality. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Hybrid Learning Algorithm of Radial Basis Function Networks for Reliability Analysis

Abstract: With the wide application of industrial robots in the field of precision machining, reliability analysis of positioning accuracy becomes increasingly important for industrial robots. Since the industrial robot is a complex nonlinear system, the traditional approximate reliability methods often produce unreliable results in analyzing its positioning accuracy. In order to study the positioning accuracy reliability of industrial robot more efficiently and accurately, a radial basis function network is used to construct the mapping relationship between the uncertain parameters and the position coordinates of the end-effector. Combining with the Monte Carlo simulation method, the positioning accuracy reliability is then evaluated. A novel hybrid learning algorithm for training radial basis function network, which integrates the clustering learning algorithm and the orthogonal least squares learning algorithm, is proposed in this article. Examples are presented to illustrate the high proficiency and reliability of the proposed method.

Image-Based Crack Detection Methods: A Review

Abstract: Annually, millions of dollars are spent to carry out defect detection in key infrastructure including roads, bridges, and buildings. The aftermath of natural disasters like floods and earthquakes leads to severe damage to the urban infrastructure. Maintenance operations that follow for the damaged infrastructure often involve a visual inspection and assessment of their state to ensure their functional and physical integrity. Such damage may appear in the form of minor or major cracks, which gradually spread, leading to ultimate collapse or destruction of the structure. Crack detection is a very laborious task if performed via manual visual inspection. Many infrastructure elements need to be checked regularly and it is therefore not feasible as it will require significant human resources. This may also result in cases where cracks go undetected. A need, therefore, exists for performing automatic defect detection in infrastructure to ensure its effectiveness and reliability. Using image processing techniques, the captured or scanned images of the infrastructure parts can be analyzed to identify any possible defects. Apart from image processing, machine learning methods are being increasingly applied to ensure better performance outcomes and robustness in crack detection. This paper provides a review of image-based crack detection techniques which implement image processing and/or machine learning. A total of 30 research articles have been collected for the review which is published in top tier journals and conferences in the past decade. A comprehensive analysis and comparison of these methods are performed to highlight the most promising automated approaches for crack detection.

Task Scheduling for Multi-Cloud Computing Subject to Security and Reliability Constraints

Abstract: The rise of multi-cloud systems has been spurred. For safety-critical missions, it is important to guarantee their security and reliability. To address trust constraints in a heterogeneous multi-cloud environment, this work proposes a novel scheduling method called matching and multi-round allocation (MMA) to optimize the makespan and total cost for all submitted tasks subject to security and reliability constraints. The method is divided into two phases for task scheduling. The first phase is to find the best matching candidate resources for the tasks to meet their preferential demands including performance, security, and reliability in a multi-cloud environment; the second one iteratively performs multiple rounds of re-allocating to optimize tasks execution time and cost by minimizing the variance of the estimated completion time. The proposed algorithm, the modified cuckoo search (MCS), hybrid chaotic particle search (HCPS), modified artificial bee colony (MABC), max-min, and min-min algorithms are implemented in CloudSim to create simulations. The simulations and experimental results show that our proposed method achieves shorter makespan, lower cost, higher resource utilization, and better trade-off between time and economic cost. It is more stable and efficient.

A guide to the measurement and interpretation of fMRI test-retest reliability.

Abstract: The test-retest reliability of functional neuroimaging data has recently been a topic of much discussion. Despite early conflicting reports, converging reports now suggest that test-retest reliability is poor for standard univariate measures-namely, voxel- and region-level task-based activation and edge-level functional connectivity. To better understand the implications of these recent studies requires understanding the nuances of test-retest reliability as commonly measured by the intraclass correlation coefficient (ICC). Here we provide a guide to the measurement and interpretation of test-retest reliability in functional neuroimaging and review major findings in the literature. We highlight the importance of making choices that improve reliability so long as they do not diminish validity, pointing to the potential of multivariate approaches that improve both. Finally, we discuss the implications of recent reports of low test-retest reliability in the context of ongoing work in the field.

The reliability and validity of the Timed Up and Go as a clinical tool in individuals with and without disabilities across a lifespan: a systematic review.

Abstract: Purpose: To summarize the available literature related to reliability and validity of the Timed Up and Go in typical adults and children, and individuals diagnosed with the following pathologies: H...

Statistics of Extreme Events in Fluid Flows and Waves

Abstract: Extreme events in fluid flows, waves, or structures interacting with them are critical for a wide range of areas, including reliability and design in engineering, as well as modeling risk of natura...

Lithium Battery State-of-Health Estimation via Differential Thermal Voltammetry With Gaussian Process Regression

Abstract: Accurate state-of-health estimation can give valuable guidelines for improving the reliability and safety of energy storage system. In this article, a novel battery degradation tracking method is proposed through the fusion of significant health features with Gaussian process regression (GPR). First, an advanced filter method is used to smooth differential thermal voltammetry (DTV) curves. Thereafter, considering the relationship between battery degradation and DTV curves, some health factors are extracted from DTV curves. In this article, these health factors involve different dimensions of the DTV curve, including peak position, peak, and valley values. Third, a correlation analysis method is employed to select four high-quality features from health factors, which are fed into GPR to learn and establish a battery degradation model. Finally, the estimation accuracy, robustness, and reliability of the proposed model are verified using four batteries with different aging test conditions and health levels. The results demonstrate that the proposed model can provide accurate battery health status forecasting.

Alpha, Omega, and H Internal Consistency Reliability Estimates: Reviewing These Options and When to Use Them

Abstract: Reliability evidence of test scores is essential in counseling research and program evaluation, as the quality of client care is, in part, based on the proper interpretation of test scores. Cronbac...

A thorough evaluation of the Language Environment Analysis (LENA) system

Abstract: In the previous decade, dozens of studies involving thousands of children across several research disciplines have made use of a combined daylong audio-recorder and automated algorithmic analysis called the LENAⓇ system, which aims to assess children's language environment. While the system's prevalence in the language acquisition domain is steadily growing, there are only scattered validation efforts on only some of its key characteristics. Here, we assess the LENAⓇ system's accuracy across all of its key measures: speaker classification, Child Vocalization Counts (CVC), Conversational Turn Counts (CTC), and Adult Word Counts (AWC). Our assessment is based on manual annotation of clips that have been randomly or periodically sampled out of daylong recordings, collected from (a) populations similar to the system's original training data (North American English-learning children aged 3-36 months), (b) children learning another dialect of English (UK), and (c) slightly older children growing up in a different linguistic and socio-cultural setting (Tsimane' learners in rural Bolivia). We find reasonably high accuracy in some measures (AWC, CVC), with more problematic levels of performance in others (CTC, precision of male adults and other children). Statistical analyses do not support the view that performance is worse for children who are dissimilar from the LENAⓇ original training set. Whether LENAⓇ results are accurate enough for a given research, educational, or clinical application depends largely on the specifics at hand. We therefore conclude with a set of recommendations to help researchers make this determination for their goals.

A New Model for Failure Mode and Effect Analysis Integrating Linguistic Z-Numbers and Projection Method

Abstract: As a proactive reliability analysis tool, failure mode and effect analysis (FMEA) was widely utilized in various industries to guarantee safety and reliability. Recently, many researchers in this field have emphasized the limitations of this technique, such as in failure mode evaluation, risk factor weighting, and failure mode prioritization. The objective of this article is to develop a new FMEA model combining linguistic Z-numbers and an extended projection method to enhance the inherent characteristics of FMEA. Specifically, the linguistic Z-numbers are used to express experts’ risk assessment information and the reliability of the assessment result. The normal projection method is extended to determine the risk priority of the failure modes considered in FMEA. Moreover, the relative weights of risk factors are derived objectively based on the idea of technique for order preference by similarity to ideal solution (TOPSIS) method. A practical risk evaluation case of aircraft landing system is given for verifying the applicability and effectiveness of the proposed FMEA. The results show that the proposed linguistic Z-number projection model is practical and flexible, which can not only depict experts’ complex and uncertain risk evaluation information accurately, but also obtain more accurate risk prioritization of failure modes.

Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review

Abstract: Radiomics research is rapidly growing in recent years, but more concerns on radiomics reliability are also raised. This review attempts to update and overview the current status of radiomics reliability research in the ever expanding medical literature from the perspective of a single reliability metric of intraclass correlation coefficient (ICC). To conduct this systematic review, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. After literature search and selection, a total of 481 radiomics studies using CT, PET, or MRI, covering a wide range of subject and disease types, were included for review. In these highly heterogeneous studies, feature reliability to image segmentation was much more investigated than reliability to other factors, such as image acquisition, reconstruction, post-processing, and feature quantification. The reported ICCs also suggested high radiomics feature reliability to image segmentation. Image acquisition was found to introduce much more feature variability than image segmentation, in particular for MRI, based on the reported ICC values. Image post-processing and feature quantification yielded different levels of radiomics reliability and might be used to mitigate image acquisition-induced variability. Some common flaws and pitfalls in ICC use were identified, and suggestions on better ICC use were given. Due to the extremely high study heterogeneities and possible risks of bias, the degree of radiomics feature reliability that has been achieved could not yet be safely synthesized or derived in this review. More future researches on radiomics reliability are warranted.

Sampling weight adjustments in partial least squares structural equation modeling: guidelines and illustrations

Abstract: Applications of partial least squares structural equation modelling (PLS-SEM) often draw on survey data. While researchers go to great lengths to document reliability and validity statistics that s...

An Ensemble Learning-Based Data-Driven Method for Online State-of-Health Estimation of Lithium-Ion Batteries

Abstract: The state-of-health (SOH) estimation of lithium-ion batteries (LIBs) is of great importance to the safety of systems. In this article, a novel ensemble learning method is proposed to accurately estimate the SOH of LIBs. A feature defined as the duration of the same charging voltage range (DSCVR) is extracted as the key health indicator for the LIB. The Pearson correlation analysis is performed to select four optimal indicators that are used as inputs of the prediction model. A random learning algorithm named extreme learning machine (ELM) is applied to extract the mapping knowledge relationship between the health indicators and the SOH due to its fast learning speed and efficient tuning mechanism. Moreover, an ensemble learning structure is proposed to reduce the prediction error of the single ELM models. A reliable decision-making rule is then designed to evaluate the credibility of the output of each single ELM model and remove the unreliable outputs, thereby significantly improving the accuracy and reliability of the estimation results. The testing results on two public data sets show that the proposed method can accurately estimate the SOH in 1 ms and is robust to the operating temperature and load profile. The average root-mean-square error (RMSE) is as low as 0.78%. The proposed method does not require any additional hardware or downtime of the system, which makes the method suitable for online practical applications.

Pump User’s Handbook: Life Extension

Abstract: A valuable reference, Pump User's Handbook: Life Extension explains just how and why the best-of-class pump users are consistently achieving superior run lengths, low maintenance expenditures, and unexcelled safety and reliability. The book conveys, in detail, what must be done to rapidly accomplish best-of-class performance and low life cycle cost