Definition Of Reliability Research Articles

РЕТРОСПЕКТИВНЫЙ АНАЛИЗ НАУЧНЫХ ИССЛЕДОВАНИЙ ПО РАЗРАБОТКЕ СПЕЦИАЛЬНОЙ ОДЕЖДЫ ДЛЯ СТРОИТЕЛЬНОЙ ОТРАСЛИ

The article has a review character on definition of reliability of special purpose products intended for workers of construction industry. In this article the prerequisites of choice, substantiation of directions of scientific researches on improvement of methods of designing and manufacturing of special clothes in order to provide safety of life activity of builders are defined. The conducted review has shown essential disadvantages of the used methods providing safety of special clothes and has revealed a degree of their dependence on those or other factors. Among the above-mentioned factors include selected materials, manufacturing technologies that have a significant impact on the properties of the designed special clothing. For this purpose it is proposed to use the methodology in which the weight coefficients are assigned to the indicators by numerical variation from 10 to 0 with 0.5 interval. Due to this methodology weighting coefficients are determined and calculated by the significance of the main and additional factors, which in turn have a significant impact on the functional properties of the designed special clothing. Keywords: special clothing, materials, products, prod

Reliable event rates for disease mapping.

When analyzing spatially referenced event data, the criteria for declaring rates as "reliable" is still a matter of dispute. What these varying criteria have in common, however, is that they are rarely satisfied for crude estimates in small area analysis settings, prompting the use of spatial models to improve reliability. While reasonable, recent work has quantified the extent to which popular models from the spatial statistics literature can overwhelm the information contained in the data, leading to oversmoothing. Here, we begin by providing a definition for a "reliable" estimate for event rates that can be used for crude and model-based estimates and allows for discrete and continuous statements of reliability. We then construct a spatial Bayesian framework that allows users to infuse prior information into their models to improve reliability while also guarding against oversmoothing. We apply our approach to county-level birth data from Pennsylvania, highlighting the effect of oversmoothing in spatial models and how our approach can allow users to better focus their attention to areas where sufficient data exists to drive inferential decisions. We then conclude with a brief discussion of how this definition of reliability can be used in the design of small area studies.

Dimensional Reliabilism

The paper argues that (i) the notion of epistemic reliability, as it is standardly defined within mainstream epistemology, is a multidimensional concept, and that (ii) paying attention to reliability’s multidimensional nature can significantly expand reliabilism’s purview both in the theoretical and practical domain. Reliabilist theories of knowledge and justification agree that a process is reliable just in case it leads to a ‘sufficiently high preponderance of true over false beliefs.’ Given this straightforward definition, reliability appears to be, and so far has been implicitly treated by epistemologists as, a mono-dimensional concept. Nevertheless, the terminology and formulas that statisticians and computers scientists have developed for assessing the reliability of binary classification processes reveal that for any belief-forming process whose aim is to identify the presence or the absence of some quality ι, there are at least four different interpretations of the above definition. These interpretations correspond to four separate dimensions of reliability: Precision, Negative Predictive Value (NPV), Recall and Specificity. Though epistemologists do not explicitly specify how they interpret the above definition of reliability, due to their main interest in ‘know-that’, their mono-dimensional focus seems to have so far been mainly directed at Precision alone (or, at best, to both Precision and NPV—but without distinguishing between the two). As it transpires, however, the (entirely) neglected dimensions of Recall and Specificity can guide the satisfaction of another important epistemic goal, best captured by the locution ‘know-most/all.’ The upshot is that different epistemic goals call for high levels of reliability along different dimensions (and, often, their combinations too). This observation invites upgrading reliabilism into dimensional reliabilism, according to which assessing whether a process is knowledge-conducive depends on whether it manifests high reliability along the appropriate dimension(s), given the epistemic goal at hand.

A framework for understanding reliability in container shipping networks

Improving reliability is increasingly regarded as an important topic in maritime transportation, especially given the significant impact that both uncertainty and delays in shipping and at ports have on the efficient flow of freight along wider supply chains. The term ‘reliability’ appears in different academic fields and with a variety of different meanings and interpretations. In transportation, reliability has been studied in most modes, but less so in the case of maritime containerisation. This paper reports on a systematic literature review of the concept of reliability in transportation, with a focus on reliability in container shipping networks. The selected papers were analysed to extract information according to the three identified sub-networks: (1) ports, including studies with a focus on infrastructure, service availability and risks in ports and hinterlands; (2) network structures, including the configuration of the networks, the vulnerability and resilience of the existing networks; (3) supply chains, including connectivity and planning of activities that integrate stakeholders within the supply chain. These sub-networks were then used to further query the database, searching for papers relevant to the research problem. Two research questions are addressed: (1) How is reliability best understood in the context of container shipping networks? (2) What are the determinants that affect container shipping network reliability? The review showed that there is no uniform definition of reliability in container shipping networks, but different approaches to understand it, depending on the theoretical perspective, have been adopted. Influencing factors and relevant metrics are discussed and a framework combining different dimensions of reliability, expressed as three themes, i.e., infrastructure reliability, network configuration reliability, and connectivity reliability, is developed. This can help both practitioners and researchers to understand in more detail the various dimensions and nuances of reliability specifically in the context of container shipping, its interrelationship with wider logistics systems and how, where possible, reliability can be improved.

Towards an understanding of reliability of software-intensive systems-of-systems

Large-scale software-intensive Systems-of-Systems (SoS) have become present in several critical domains and have sometimes depended on diverse trending technologies, such as cloud computing and machine learning. At the same time, the SoS dynamic architecture makes it difficult to assure SoS reliability leading to diverse studies with specific solutions, while the need for a shared view of what precisely SoS reliability refers to still exists. The main contribution of this article is to go towards an understanding of SoS reliability. We present a conceptual model whose concepts as well as their definitions and relationships were defined by systematically examining the literature of the field. We surveyed 36 practitioners and researchers regarding ambiguity, explanatory power, parsimony, generality, and utility of our model. Next, we adjusted our model according to their contribution. We reach a conceptual model containing 29 concepts and their relationships that help to comprehend SoS reliability. In addition, we provided a glossary with a definition of each concept of our conceptual model. We also proposed a SoS reliability definition grounded on the literature. By organizing the knowledge of SoS reliability, this conceptual model makes it possible to expand the body of knowledge in the area and opens several opportunities for further investigations; in particular, this model serves as a basis for novel solutions aiming to assure SoS reliability.

Psychometric validation of the generalized pustular psoriasis physician global assessment (GPPGA) and generalized pustular psoriasis area and severity index (GPPASI).

Generalized pustular psoriasis (GPP) is a rare and life-threatening skin disease often accompanied by systemic inflammation. There are currently no standardized or validated GPP-specific measures for assessing severity. To evaluate the reliability, validity and responder definitions of the Generalized Pustular Psoriasis Physician Global Assessment (GPPGA) and Generalized Pustular Psoriasis Area and Severity Index (GPPASI). The GPPGA and GPPASI were validated using outcome data from Week 1 of the Effisayil™ 1 study. The psychometric analyses performed included confirmatory factor analysis, item-to-item/item-to-total correlations, internal consistency reliability, test-retest reliability, convergent validity, known-groups validity, responsiveness analysis and responder definition analysis. Using data from this patient cohort (N=53), confirmatory factor analysis demonstrated unidimensionality of the GPPGA total score (root mean square error of approximation <0.08), and GPPGA item-to-item and item-to-total correlations ranged from 0.58 to 0.90. The GPPGA total score, pustulation subscore and GPPASI total score all demonstrated good test-retest reliability (intraclass correlation coefficient: 0.70, 0.91 and 0.95 respectively), and good evidence of convergent validity. In anchor-based analyses, all three scores were able to detect changes in symptom and disease severity over time; reductions of -1.4, -2.2 and - 12.0 were suggested as clinically meaningful improvement thresholds for the GPPGA total score, GPPGA pustulation subscore and GPPASI total score respectively. Anchor-based analyses also supported the GPPASI 50 as a clinically meaningful threshold for improvement. Overall, our findings indicate that the GPPGA and GPPASI are valid, reliable and responsive measures for the assessment of GPP disease severity, and support their use in informing clinical endpoints in trials in GPP.

Reliabilities of estimated breeding values in models with metafounders

BackgroundReliabilities of best linear unbiased predictions (BLUP) of breeding values are defined as the squared correlation between true and estimated breeding values and are helpful in assessing risk and genetic gain. Reliabilities can be computed from the prediction error variances for models with a single base population but are undefined for models that include several base populations and when unknown parent groups are modeled as fixed effects. In such a case, the use of metafounders in principle enables reliabilities to be derived.MethodsWe propose to compute the reliability of the contrast of an individual’s estimated breeding value with that of a metafounder based on the prediction error variances of the individual and the metafounder, their prediction error covariance, and their genetic relationship. Computation of the required terms demands only little extra work once the sparse inverse of the mixed model equations is obtained, or they can be approximated. This also allows the reliabilities of the metafounders to be obtained. We studied the reliabilities for both BLUP and single-step genomic BLUP (ssGBLUP), using several definitions of reliability in a large dataset with 1,961,687 dairy sheep and rams, most of which had phenotypes and among which 27,000 rams were genotyped with a 50K single nucleotide polymorphism (SNP) chip. There were 23 metafounders with progeny sizes between 100,000 and 2000 individuals.ResultsIn models with metafounders, directly using the prediction error variance instead of the contrast with a metafounder leads to artificially low reliabilities because they refer to a population with maximum heterozygosity. When only one metafounder is fitted in the model, the reliability of the contrast is shown to be equivalent to the reliability of the individual in a model without metafounders. When there are several metafounders in the model, using a contrast with the oldest metafounder yields reliabilities that are on a meaningful scale and very close to reliabilities obtained from models without metafounders. The reliabilities using contrasts with ssGBLUP also resulted in meaningful values.ConclusionsThis work provides a general method to obtain reliabilities for both BLUP and ssGBLUP when several base populations are included through metafounders.

Pouzdanost i raspoloživost elektronskih sistema vođeni podacima

Introduction/purpose Reliability and availability are important especially for military, medical, and other professional equipment. Reliability and availability management and/or prognostic reliability calculations have always been data driven. This article focuses on the analysis of the data impact on reliability and availability. Methods: This research is based mostly on the articles published by the author of this work as well as on some other papers. Results: This paper results in a discussion on the definition of the data-driven concept, preceded by brief definitions of reliability and availability, and followed by the analysis of the main impacts of uncertain data on prognostic reliability calculations as well as by reliability and availability of data used in reliability calculations. Conclusions: Reliability and availability are still very important. Reliability and availability have always been data driven while valid and relevant data have always been the main problem. Without good data, prognostic reliability is useless in spite of a good reliability model.

К ВОПРОСУ О ДОСТОВЕРНОСТИ ИСТОЧНИКА ЖАНРА

The question of the inconsistency of the definition of linguistic reliability and the principles of its identification is debatable. The problem of the authenticity of the genre source has not been raised in the scientific review, but it turns out to be relevant due to the contradictions that arise at the level of choosing a genre description source. The decision of linguists to use any of the available statements to describe the genre is a priori debatable. The contradictions of this approach lie in the difference in the types of information contained in the varieties of speech. The question of the qualification of the type of information lies in the differentiation of natural and artificial speech variants, where the natural variety of speech is characterized by the greatest degree of reliability.

The Comparison of Premises for Recognizing Tax Books as Kept not Compliant in the Tax Law and Accounting Law in Poland

The purpose of this article. The purpose of the paper is to compare and assess provisions on premises for recognizing tax books as kept not compliant to the regulations in the tax law and accountancy law as well as to discuss the possibility of using the tax judgments on this issue in the interpretation of compliant account books in the accountancy law. The research hypothesis is as follows: The tax judgments on the premises for recognizing tax books as kept not compliant may be also used directly for the interpretation of compliancy of account books in the accountancy law. Methodology. The descriptive study including critical attitude to the legal acts and literature was used to solve the research problem. The result of the research. Both the tax law and accountancy law comprise the rules for recognizing tax books (account books) to be compliant to the regulations. The tax law uses the terms of the reliability and correctness. The Accountancy Act law uses the terms of the reliability, error-free, verifiable manner and on an on-going basis. Although definitions of reliability are very similar, as the analysis shows, the character of reliability of tax books is not always the same as for account books. The judgments in the field of interpretation of the reliability for tax purposes may not always be used for accounting purposes. The term correctness in the Tax Ordinance does not always correspond to the terms: error-free, verifiable manner and on an on-going basis in the Accountancy Act. So, the tax judgment concerning correctness of the tax book from the tax law cannot be directly applied into the interpretation of such terms as error-free, verifiable manner and on an on-going basis in the accountancy law. The paper additionally proposes some changes to the tax law concerning the unreliability of tax books, as well as changes to the Accountancy Act.

Reliability estimation of Lomax distribution with fuzziness

This paper considers the problem of estimating the reliability function for Lomax distribution with the presence of fuzziness through two procedures. The first procedure depends on fuzzy reliability definition that uses the composite trapezoidal rule in order to find the numerical integration and the second is Bayesian procedure which includes different cases depends on sample data and hyper-parameters of prior gamma distribution with squared error as a symmetric loss function and precautionary as an asymmetric loss function. In the Bayesian procedure, we proposed to consider three cases to estimate the fuzzy reliability with fuzzy observations, precise observations with fuzzy hyper-parameter, and fuzzy observations with fuzzy hyper-parameter.

A two-stage dimension-reduced dynamic reliability evaluation (TD-DRE) method for vibration control structures based on interval collocation and narrow bounds theories

Due to the uncertainties from modeling, manufacturing, and working environments, many vibration active control systems usually show dynamic uncertain properties. Hence structural reliability estimation benchmarking to full-cycle vibratory responses is vitally important. In this study, a novel two-stage dimension-reduced dynamic reliability evaluation (TD-DRE) method for linear quadratic regulator (LQR) controlled structures is developed. This method combines interval uncertainties and the time-variant reliability (TVR) concept. In the first stage, the Taylor series expansion is employed to analyze several typical limit states for definition of the time-discretized dynamic reliability. Then the interval collocation method tackles the solution. In the second stage, the TVR problem is indeed transformed to a time-invariant reliability (TIR) problem. Furthermore, the narrow bounds theorem deduces the presented TD-DRE index. Eventually, two application examples are utilized to verify the effectiveness and accuracy of the proposed method. The proposed TD-DRE is more accurate than the traditional first-order Taylor expansion and more effective than the first-passage reliability evaluation method. This method can provide a reference and an initial value for further design, and improve the efficiency of LQR controller design in practical engineering.

Reliability as Lindley Information

This paper introduces a definition of reliability based on Lindley information, which is the mutual information between an observed measure and latent attribute. This definition reduces to the traditional definition of reliability in the case of normal variables, but can be applied to any joint distribution of observed and latent variables. Importantly, unlike traditional definitions of reliability, this formulation of reliability applies to vector- or matrix-valued estimates and summaries of responses, and therefore generalizes reliability to sets of scores and estimates in addition to individual scores and estimates. This formulation also leads to new bounds for reliability, as well as newly reported relationships between reliability and the traditional Fisher information function familiar in item response theory literature. This form of reliability can be estimated using formulae, or methods used in Bayesian inference such as Markov Chain Monte Carlo (MCMC) depending on the case. Examples based on well-studied datasets are provided, as well as applications to drift-diffusion modeling and randomly-varying intraindividual covariance structures.

An ensemble deep autoencoders based on asymmetric structure for operational reliability assessment of bearings

At present, with the rapid growth of manufacturing and big data, reliability technology has gradually become a topical issue in the industrial field. Aiming at the operation reliability assessment of rolling bearings, this paper proposes a bearings operational reliability assessment using an ensemble deep autoencoder based on asymmetric structure. In this method, an ensemble deep autoencoder is used to adaptively learn degradation features from condition monitoring data, where the ensemble deep autoencoder adopts an asymmetric structure with different activation functions in the encoder and decoder. Then, the learned features are classified by correlation analysis, and the typical features in each category are selected. Finally, the operation reliability of rolling bearings is evaluated through the definition of reliability based on Mahalanobis distance. Through the example evaluation of rolling bearing operation reliability and comparison with other feature extraction methods, it can be concluded that this method has stronger feature extraction ability and can effectively show the trend of bearing degradation.

Mathematical Representation for Reliability of Sensing-Based Semi-Persistent Scheduling in LTE-V2X

Since 3GPP has introduced sensing-based semi-persistent scheduling (SPS) into the MAC layer of LTE-V2X Mode 4 in release 14, various studies have been carried out to evaluate its performance. However, until now, there is still no clear mathematical expression for the reliability of this scheduling. To this end, we try to provide a theoretical representation of the scheduling reliability. Firstly, we present a description of the physical layer and MAC layer of sensing-based SPS, and then we abstract the scheduling mathematically and give the definition of the scheduling reliability and channel busy rate. Secondly, according to the characteristics of semi-persistent of scheduling, we formulate the reliability equation through the principle of iteration, thereby obtaining the closed-form expression of the scheduling reliability. Finally, we compare the theoretical representation of reliability with system simulations, and results show that the theoretical curve can represent the reliability of scheduling excellently.

Assessing the reliability and validity of an FRAM model: the case of driving in an overtaking scenario

Over the past two decades, systemic-based risk assessment methods have garnered more attention, and their use and popularity are growing. In particular, the functional resonance analysis method (FRAM) is one of the most widely used systemic methods for risk assessment and accident analysis. FRAM has been progressively evolved since its starting point and is considered to be the most recent and promising step in understanding socio-technical systems. However, there is currently a lack of any formal testing of the reliability and validity of FRAM, something which applies to Human Factors and Ergonomics research as a whole, where validation is both a particularly challenging issue and an ongoing concern. Therefore, this paper aims to define a more formal approach to achieving and demonstrating the reliability and validity of an FRAM model, as well as to apply this formal approach partly to an existing FRAM model so as to prove its validity. At the same time, it hopes to evaluate the general applicability of this approach to potentially improve the performance and value of the FRAM method. Thus, a formal approach was derived by transferring both the general understanding and definitions of reliability and validity as well as concrete methods and techniques to the concept of FRAM. Consequently, predictive validity, which is the highest maxim of validation, was assessed for a specific FRAM model in a driving simulator study using the signal detection theory. The results showed that the predictive validity of the FRAM model is limited and a generalisation with changing system conditions is impossible without some adaptations of the model. The applicability of the approach is diminished because of several methodological limitations. Therefore, the reliability and validity framework can be utilised to calibrate rather than validate an FRAM model.

Calibration of a Mechanistic-Empirical Cracking Model Using Network-Level Field Data

At the core of a mechanistic-empirical (M-E) pavement design method is a collection of performance models that each predicts the development of a specific pavement distress, such as fatigue cracking and surface rutting. Each model has both mechanistic and empirical parts. The empirical parts need to be calibrated to remove bias and increase prediction accuracy. This process has traditionally been conducted with small numbers of field sections for which materials may or may not have been sampled and tested. This paper presents a new calibration approach that uses network-level field data and statewide distributions of material properties, without having to sample and test every individual calibration section. The calibration of the fatigue and reflection cracking models for CalME, the M-E design software developed for the California Department of Transportation, is used as an example to illustrate the new approach. The new approach works by correlating the statistical distributions of M-E design inputs with the statistical distribution of pavement performance, both at the network level. The uncertainties affecting pavement performance are divided into those specific to a given project (within-project variability) and those that vary between projects (between-project variability). This distinction allows a clear definition of design reliability. The results showed that the new approach can overcome some of the network-level data limitations and provides a reasonable calibration ready for routine pavement design.

From Accuracy to Reliability and Robustness in Cardiac Magnetic Resonance Image Segmentation: A Review

Since the rise of deep learning (DL) in the mid-2010s, cardiac magnetic resonance (CMR) image segmentation has achieved state-of-the-art performance. Despite achieving inter-observer variability in terms of different accuracy performance measures, visual inspections reveal errors in most segmentation results, indicating a lack of reliability and robustness of DL segmentation models, which can be critical if a model was to be deployed into clinical practice. In this work, we aim to bring attention to reliability and robustness, two unmet needs of cardiac image segmentation methods, which are hampering their translation into practice. To this end, we first study the performance accuracy evolution of CMR segmentation, illustrate the improvements brought by DL algorithms and highlight the symptoms of performance stagnation. Afterwards, we provide formal definitions of reliability and robustness. Based on the two definitions, we identify the factors that limit the reliability and robustness of state-of-the-art deep learning CMR segmentation techniques. Finally, we give an overview of the current set of works that focus on improving the reliability and robustness of CMR segmentation, and we categorize them into two families of methods: quality control methods and model improvement techniques. The first category corresponds to simpler strategies that only aim to flag situations where a model may be incurring poor reliability or robustness. The second one, instead, directly tackles the problem by bringing improvements into different aspects of the CMR segmentation model development process. We aim to bring the attention of more researchers towards these emerging trends regarding the development of reliable and robust CMR segmentation frameworks, which can guarantee the safe use of DL in clinical routines and studies.

Reliability Analysis for Finger Movement Recognition With Raw Electromyographic Signal by Evidential Convolutional Networks.

Hand gesture recognition with surface electromyography (sEMG) is indispensable for Muscle-Gesture-Computer Interface. The usual focus of it is upon performance evaluation involving the accuracy and robustness of hand gesture recognition. However, addressing the reliability of such classifiers has been absent, to our best knowledge. This may be due to the lack of consensus on the definition of model reliability in this field. An uncertainty-aware model has the potential to self-evaluate the quality of its inference, thereby making it more reliable. Moreover, uncertainty-based rejection has been shown to improve the performance of sEMG-based hand gesture recognition. Therefore, we first define model reliability here as the quality of its uncertainty estimation and propose an offline framework to quantify it. To promote reliability analysis, we propose a novel end-to-end uncertainty-aware finger movement classifier, i.e., evidential convolutional neural network (ECNN), and illustrate the advantages of its multidimensional uncertainties such as vacuity and dissonance. Extensive comparisons of accuracy and reliability are conducted on NinaPro Database 5, exercise A, across CNN and three variants of ECNN based on different training strategies. The results of classifying 12 finger movements over 10 subjects show that the best mean accuracy achieved by ECNN is 76.34%, which is slightly higher than the state-of-the-art performance. Furthermore, ECNN variants are more reliable than CNN in general, where the highest improvement of reliability of 19.33% is observed. This work demonstrates the potential of ECNN and recommends using the proposed reliability analysis as a supplementary measure for studying sEMG-based hand gesture recognition.

Evaluating confidence in toxicity assessments based on experimental data and in silico predictions

Understanding the reliability and relevance of a toxicological assessment is important for gauging the overall confidence and communicating the degree of uncertainty related to it. The process involved in assessing reliability and relevance is well defined for experimental data. Similar criteria need to be established for in silico predictions, as they become increasingly more important to fill data gaps and need to be reasonably integrated as additional lines of evidence. Thus, in silico assessments could be communicated with greater confidence and in a more harmonized manner. The current work expands on previous definitions of reliability, relevance, and confidence and establishes a conceptional framework to apply those to in silico data. The approach is used in two case studies: 1) phthalic anhydride, where experimental data are readily available and 2) 4-hydroxy-3-propoxybenzaldehyde, a data poor case which relies predominantly on in silico methods, showing that reliability, relevance, and confidence of in silico assessments can be effectively communicated within integrated approaches to testing and assessment (IATA).