Search

The lateral step-down test is used to appraise movement quality in patients with patellofemoral pain (PFP), however, it is unclear if reliability of the test is affected by physical therapist experience.Determine if there is a difference in reliability between 'experienced' and 'novice' physical therapists appraising movement quality of patients with PFP during the lateral step-down test.Three 'experienced' and 3 'novice' physical therapists analyzed movement quality of 22 participants [mean age (SD) 28.25 (6.5) years] with PFP. Physical therapists viewed two-dimensional videos of participants performing the lateral step-down test and appraised the quality with a score (0-1 = 'good'; 2-3 = 'fair', and 4-5 = 'poor') at baseline and 1 week. Inter- and intra-rater reliability were calculated with kappa and percent agreement. Differences between the groups were assessed with the chi-square test with an a priori alpha level of0.05.Inter- and intra-rater reliability ranged from fair to moderate (ĸ = 0.40-0.65). There was no difference in reliability between 'experienced' and 'novice' physical therapists at baseline (There was no difference in reliability between 'experienced' and 'novice' physical therapists using categories to appraise movement quality during the lateral step-down test for patients with chronic PFP.

Diagnostic theories are fundamental to information system (IS) practice and are represented as trees. While there are approaches for validating diagnostic trees, these validate the overall performance of the tree rather than identifying ways incorrect diagnoses can occur. It is important to fully validate diagnostic trees because even if the tree gives the correct decision “most of the time,” it is possible for incorrect decisions traveling down little-used branches of the tree to result in catastrophic decisions. In this article, we describe the process of using a variant of q-sorting to validate diagnostic trees. In this methodology, diagnostic trees that independent experts develop are transformed into a quantitative form, and that quantitative form is tested to determine the inter-rater reliability of the individual branches in the tree. The trees are then successively transformed to incrementally test if they branch in the same way. The results help researchers not only identify quality items for use in a diagnostic tree but also facilitate diagnoses of problems with those items and facilitate the reconciliation of discrepant trees by experts. The methodology validates not only the whole tree but also its subparts.

As a system reliability analysis technology, failure mode and effect analysis (FMEA) has been widely used in various industries to reduce the failure risk of products, systems, and services. In recent years, many researchers have found that this technology has limitations in failure mode assessment and failure mode prioritization in complex uncertain environment. In order to solve this problem, this article proposes a new FMEA model, which combines hesitant Pythagorean fuzzy sets (HPFSs) and ORESTE method. Specifically, HPFSs are used to describe the risk assessment information of experts. The ORESTE method is extended to determine the risk priority of the failure modes in FMEA. Moreover, the PIR structure of ORESTE is improved to reduce the subjective influence of experts. Finally, an example of risk assessment of intelligent aquaculture system is given to verify the applicability and effectiveness of the proposed FMEA model. The results show that the proposed FMEA model can not only accurately describe the hesitation and uncertainty of experts’ risk assessment information, but also obtain more suitable risk ranking of failure modes, based on which managers can further take corresponding preventive measures.

In this paper we propose a novel approach to estimating and testing skewness in a stochastic volatility (SV) model. Our key idea is to replace a normal return error in the standard SV model with a split normal error. We show that this simple variation in the model brings about two large computational advantages. First, the SV can be simulated fast and efficiently using a one-block Gibbs sampling technique. Second, more importantly, this is the first to provide a marginal likelihood calculation method to formally test the skewness and SV in a Bayesian framework. We subsequently demonstrate the efficiency and reliability of our posterior sampling and model comparison methods through a simulation study. The simulation study results also show that neglecting skewness leads to inaccurate SV estimates and conditional expected returns. Our empirical applications to daily stock return data also show strong evidence of negative skewness.

Motor vehicles are composed of a large number of parts, and planning the maintenance activities of different parts is a crucial decision that affects system reliability, operation costs, and capacity requirements of service providers. We propose a systematic method to determine the critical parts that should be handled with extra preventive maintenance (PM) and prepare alternative PM plans with different levels of cost and capacity usage. Our method uses a multicriteria decision-making approach to determine the critical parts and conducts statistical reliability analysis with failure data and expert knowledge to create the maintenance plans. We use the proposed method in a case study to determine optional PM packages that would support regular PM practices in the after-sales service of a large motor vehicle manufacturer. The main aim of the case study is to increase the satisfaction of customers who are more sensitive to failures, such as carriers of food and medical supplies. The results show that the optional PM packages can decrease the cost of failures while obeying the capacity limitation of the company.

This study contributes to emerging computationally efficient methods to account for multi-purpose reservoir operations at the dam design stage. In many cases, the water supply reliability objective introduces binary variables into an otherwise simpler linear programming (LP) formulation. This has led to widespread use of evolutionary methods such as particle swarm optimisation (PSO). Combining the branch and bound (BB) method with a hybrid PSO–LP, this study presents a new BB–PSO–LP algorithm to solve the joint design and operations problem. The proposed BB–PSO–LP algorithm was benchmarked against classical versions of the BB algorithm and PSO–LP. Superiority of the BB–PSO–LP algorithm was demonstrated by finding slightly better solutions in a shorter time. That said, PSO–LP also led to acceptable results in a similar time. Although the BB algorithm was found to be efficient in problem solving, under certain conditions it is difficult for the BB method to find a feasible solution.

The aim of this study was to develop an efficient probability-based seismic fragility assessment method for steel moment-resisting frames (MRFs). In this method, Bayes estimators are used to improve the accuracy of the results based on new analyses or experimental observations. The efficiency of Bayes estimators in the updating process of fragility curves is demonstrated based on the results of incremental dynamic analysis of three-, five- and seven-storey steel MRFs under a set of 40 spectrum-compatible earthquake ground motions. The results indicate that the constant-hazard-level (engineering demand parameter based) fragility analysis approach leads to less than 6% error in the prediction of performance levels and therefore can be considered a competent alternative to the more computationally expensive constant-performance (intensity measure based) approach. While the earthquake duration can considerably affect the results of fragility analysis, the prediction errors can be reduced to an acceptable level through selection of four different time intervals with at least three records in each group. It is also concluded that Bayes estimators can generally provide a very good level of accuracy in updating fragility curves by reducing the maximum errors to less than 2.5% compared with the exact values.

Music is commonly regarded as one of the fine arts, but aesthetic responses to music are still poorly understood. The aim of this study was thus to shed light on the psychological process through ...

An application of geometric reliability techniques on various pile types based on 67 load–displacement curves obtained from pertinent literature is presented in this paper. These static loading tests were performed at local scale (even building-specific sites) under essentially identical geotechnical conditions. A power-law function with two parameters was used to fit the measured load–settlement curves. For each site, the means and coefficients of variation for the power-law parameters were obtained. Since the number of tests conducted at each site is usually small, it is extremely difficult to identify a certain distribution type for these regression parameters. Thus, for simplicity, a bivariate normal distribution was assumed to represent the set of regression pairs. This joint distribution was incorporated into a geometric reliability method, which offers an estimation of the bearing capacity of piles at the serviceability limit state. Examples are provided to illustrate the application of the proposed reliability method to interpret the visually determined reliability index of the bearing capacity of piles. These case studies showed that the proposed method is a useful and comprehensive tool for capturing the load–displacement response and for evaluating the bearing capacity of piles by considering uncertainties in their load–displacement behaviour.

The extrusion process of plastic transformation involves a series of variables and components which must be associated with the concept of reliability to achieve an expected performance in operational and quality terms. This paper describes the reliability framework of an extruder of a packaging company located in Brazil through the following mathematical representations: probability density function f(t), reliability curve R(t) and function rate of failure h(t). Through a statistical software the set of representations was able to develop the reliability framework of the machine, which can be used to support a FMEA analysis. Besides that, it was evidenced that in the current operating conditions of the machine, approximately 50% of the failure observations happen on the hour 100 of operating time. The main outcome is that it is recommended a systematic preventive maintenance based on 100h cycles. It is expected that this work may act as a guide for future implementation of improvement activities.

This study aimed to develop and evaluate the performance of a service system platform based on the Internet of Things (IoT) for monitoring nutritional deficiencies in plants and providing fertilizer recommendations. There are two distinct differences between this work and previous ones; namely, this service system platform has been developed based on IoT using a system engineering approach and its performance has been evaluated using dependability. We have successfully developed and integrated a service system platform and chlorophyll meter that is based on IoT. We have also successfully tested the performance of the service system platform using the JMeter software. The dependability value measured from the five tested variables (reliability, availability, integrity, maintainability, and safety) showed a value of 0.97 which represents a very good level of system confidence in not failing to deliver services to users under normal operational conditions. From a future perspective, this platform can be used as an alternative service to monitor nutrient deficiencies in plants and provide fertilization recommendations to increase yields, reduce fertilizer costs, and prevent the use of excessive fertilizers, which can cause environmental pollution.

The Wan Jiang Bridge in China was damaged by ship impact and subsequently restored. This paper presents a detailed evaluation of the residual service life (RSL) of the bridge using the reliability analysis method of the Joint Committee on Structural Safety, based on the structural load-bearing capacity (LBC) of the bridge and the design load. Data were collected from bridge design codes, field loading tests and finite-element (FE) analysis. The LBC was initially calculated and subsequently revised using specific coefficients. Using FE analysis, the bending moment on the control section of the bridge was determined under the most unfavourable loading conditions and the ultimate LBC of the bridge was checked. Subsequently, static and dynamic loading tests on the restored bridge were conducted, with the load and capacity determined for the control section based on FE analysis. The loads considered were dead load, vehicle load and crowd load. A time-dependent reliability index was developed for the restored bridge using the probability distributions of capacity and design load variables, and the RSL was determined. This case study, predicting the RSL of the bridge based on a multitude of data, should be valuable for future bridge maintenance and management.

The reliability of the result significantly affects the use of the Coefficient of Variation (within the test variation), which reflects the variability of the concrete properties rather than the standard deviation. For the estimation of reliability and precision, comprehensive statistical analysis (within test variation) is conducted on 534 test samples using a rebound hammer. Different standards, like BIS, EN, ACI, and ASTM, are used in this study to compare the test results. Empirical research revealed that there are contradictions in literature as well as the standards used in many countries. The present paper focuses on detailed investigations of uncertainty measurements of the surface hardness test by statistical parameters (range of rebound index, standard deviation, and Coefficient of Variation). The unbiased estimate of the variability parameters from the published results has been verified concerning to the European standard. Concrete parameters namely cement type, water to cement ratio, compaction deficiency, curing condition, and elevated temperature have an impact on the output of the rebound hardness in terms of the variability of the compressive strength. Based on the results, the authors are willing to propose amendments to the Indian code graciously in compliance with the variability of the rebound hammer.

Collusion occurs when companies conspire to remove competition from the process of bidding and collectively agree who will win an auction by engaging in price fixing and bid-rigging. In this article, we modify an existing method based on order statistics to improve its accuracy and reliability to detect the presence of collusion. The new approach can be robustly implemented in practice and is difficult to defraud. We use data from the Brazilian Federal Police's ongoing “Operation Car Wash” investigation to identify collusive behavior during an uncapped auction. Such a rich dataset enabled the new approach to be tested in a real-life setting. We demonstrate that our method's increased predictive power makes it the best option to determine full collusion in uncapped auctions. Our new approach can be used in conjunction with other collusion detection methods to improve their accuracy and reliability. The contributions of our article are twofold: 1) Public and private sectors procurement authorities are better positioned to detect collusion even when limited auction data is made available; and 2) law enforcement agencies are provided with a robust method that can be used together with prevailing evidence to pursue a conviction for engaging in collusive behavior.

Project selection has a critical role in the successful execution of the lean six sigma (LSS) program in any industry. The poor selection of LSS projects leads to limited results and diminishes the credibility of LSS initiatives. For this reason, in this article, we propose a method for the assessment and effective selection of LSS projects. Intuitionistic fuzzy sets based on the weighted average were adopted for aggregating individual suggestions of decision makers. The weights of selection criteria were computed using entropy measures and the available projects are prioritized using the multiattribute decision making approach, i.e., modified TOPSIS and VIKOR. The proposed methodology is validated through a case example of the LSS project selection in a manufacturing organization. The results of the case study reveal that out of eight LSS projects, the assembly section (A8) is the best LSS project. A8 is the ideal LSS project for swift gains and manufacturing sustainability. The robustness and reliability of the obtained results are checked through a sensitivity analysis. The proposed methodology will help manufacturing organizations in the selection of the best opportunities among complex situations, results in sustainable development. The engineering managers and LSS consultants can also adopt the proposed methodology for LSS project selections.

As a powerful tool for synthesizing information from multiple studies, meta-analysis has gained high popularity in many disciplines. Conclusions stemming from meta-analyses are often used to direct theory development, calibrate sample size planning, and guide critical decision-making and policymaking. However, meta-analyses can be conflicted, misleading, and irreproducible. One of the reasons for meta-analyses to be misleading is the improper handling of measurement unreliability. We show that even when there is no publication bias, the current meta-analysis procedures would frequently detect nonexistent effects, and provide severely biased estimates and intervals with coverage rates far below the intended level. In this study, an effective approach to correcting for unreliability is proposed and evaluated via simulation studies. Its sensitivity to the violation of the homogeneous reliability and residual correlation assumption is also tested. The proposed method is illustrated using a real meta-analysis on the relationship between extroversion and subjective well-being. Substantial differences in meta-analytic results are observed between the proposed method and existing methods. Further, although not specifically designed for aggregating effect sizes with various measures, the proposed method can be used to fulfill the purpose. The study ends with discussions on the limitations and guidelines for implementing the proposed approach. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Sentiment analysis is the method to detect stock market behaviour. Various components are functioning their ups and downs because the stock market is unestimated. The trend of sequence gets influenced through factors and non-linear relationship. Both academic and real-life business, the Stock market prediction is an attractive topic. The prediction is carried out by gathering the customer reviews and classifying them as positive reviews or negative reviews. Various statistical and econometric techniques were introduced for stock market prediction using sentimental analysis. Stock market predictions are suffered from errorless through sentiment analysis techniques. Lesser classification accuracy has a direct cause for the reliability of stock market indicators. To improve prediction performance, the existing problems of stock market prediction were reviewed in this paper.

The aim of the present paper is to compare the current design codes’ predictions for reinforced concrete (RC) T-beams with the alternative physical methods – namely, the compressive force path (CFP) method and artificial neural networks (ANNs). Therefore, two databases, for T-beams without stirrups and with stirrups, are developed using the available experimental studies. The comparative study on prediction (obtained from the American Concrete Institute and Eurocode 2, CFP and ANN models) shows that the predictions of the ANN model provide a closer fit to the experimental results; after ANN the predictions of the CFP method are close to the experimental results when compared with the counterpart physical model. Comparative studies are also conducted on the critical parameters for the behaviour of RC T-beams. Furthermore, a non-linear finite-element tool (i.e. Abaqus) is used to validate the prediction of the ANN and CFP model. The crack pattern from Abaqus exhibited the same mechanics, on which the CFP models are based.

Background The isokinetic dynamometer has been considered the gold-standard measurement of muscle performance. However, the reliability for the isometric and isokinetic wrist flexor and extensor strength using the Biodex isokinetic dynamometer has not been reported to date. This study evaluated test-retest reliability of isometric and isokinetic wrist strength using the Biodex System 3 isokinetic dynamometer. Methods This is a test-retest reliability study. Peak concentric and isometric torque was determined of the dominant limb wrist flexor and extensor of healthy individuals using the Biodex Multi-Joint System 3 dynamometer. The participants were evaluated by the same examiner in two different sessions separated by an interval of two to seven days. Reliability was investigated using the intraclass correlation coefficient (ICC2,1), standard error of measurement (SEM) and minimum detectable difference (MDD). Results Twenty healthy subjects participated in the study. The ICCs for concentric and isometric torque of the wrist flexor and extensor ranged from 0.79 to 0.91, revealing excellent intra-examiner test-retest reliability; SEM ranging from 0.4 to 1.5; and MDD ranged from 1.12 Nm to 4.17 Nm. Conclusions Excellent intra-examiner test-retest reliability was found regarding peak concentric toque of the wrist flexor and extensor at an angular velocity of 45°/s as well as isometric torque of the wrist flexor and extensor in healthy individuals measured using the Biodex System 3 isokinetic dynamometer.

As the backbone of Industry 4.0, industrial cyber-physical systems (ICPSs) that are geographically dispersed, federated, cooperative, and security-critical systems become the center of interest from both industry and academia. In ICPS, there are huge amounts of devices, such as sensors and actuators, which are embedded and networked together to improve the performance of real-time monitoring and control. Reliability and temperature are two important concerns of these embedded and networked devices in ICPS due to their stringent requirement of reliable execution and long lifespan. In this article, we study the problem of maximizing soft-error reliability of CPU- and GPU-integrated embedded platforms deployed in ICPS under the temperature constraint. To speed up the estimation of soft-error rate (SER) and temperature, we train an artificial neural network (ANN) that is able to quickly and accurately derive the system's SER and temperature. To solve the temperature-constrained reliability optimization problem, we propose a feedback control-based task scheduling scheme that adaptively determines the number of tasks admitted in the system and the number of replicas for the admitted tasks. We perform a series of simulation experiments to verify the efficacy of our scheme. The experimental results demonstrate that: 1) the estimated SER and temperature derived by our ANN-based method are very close to the ground-truth data and 2) our proposed feedback control-based task scheduling method can improve system reliability by up to 184.2% with a lower peak temperature when compared with one baseline and two state-of-the-art methods.

Worker reliability estimation is fundamental for crowdsensing applications. This paper studies a robust feedback rating approach to estimate worker reliability. In this approach, the requester provides a feedback rating to reflect the quality of the sensor data submitted by each worker. The aggregation of each worker's historical feedback ratings serves as a reliability estimate. The challenges are: (1) Feedback ratings are subjected to noise or bias; (2) Workers cognitive bias in task selection leads to higher sensor data quality variations. We develop a mathematical model to quantify the degree of rating bias and degree of cognitive bias. We also derive sufficient conditions, under which the aggregate rating is asymptotically accurate in estimating worker reliability, via stochastic approximation techniques. These conditions identify a class of asymptotically accurate rating aggregation rules for crowdsensing applications. We further derive the minimum number of ratings needed to guarantee a given reliability estimation accuracy, via martingale theory. Via extensive experiments: (1) We reveal fundamental understandings on how various factor such as rating bias influence the minimum number of ratings needed to achieve certain accuracy; (2) Our feedback rating approach improves air quality index estimation accuracy by as high as 50% over the URP algorithm.

With increased awareness on environmental concerns, there has been a significant shift toward the use of renewable energy sources (RES). In this regard, smart grid technologies have been presented in order to facilitate greater RES penetration. Microgrids are critical components of smart grids. Microgrids enable the integration of various distributed energy resources (DER) into the distribution system, such as distributed generation (DGs) and energy storage systems (ESSs), and thus eliminate the need for distribution system expansion. This research focuses on an islanding operation of a microgrid. This paper employs a hybrid analytical-simulation method to assess the reliability of a microgrid with prioritised loads and distributed RES. The stochasticity of RES complicates the evaluation of reliability. The problem is solved using a mixed integer optimization model that maximizes the reliability of the micro-grid system by determining the number of critical and non-critical loads that can be satisfied at each time step. The maximum number of loads is limited by the available renewable and stored generation in the system at each time step. The results show that the presence of the microgrid topology improves the overall system's reliability. The incorporation of energy storage into the micro-grid improves the system's System Average Interruption Frequency Index, as demonstrated in this study.

INTRODUCTION: Measurement instruments are important in clinical practice and research for assessing physical function in critically ill patients in the intensive care unit (ICU).OBJECTIVE: To investigate inter-rater reliability and responsiveness of the Danish version of the CPAx (CPAx-D).METHOD: Critically ill patients from three Danish ICUs were included. Patients were assessed with CPAx-D by two blinded testers during a regular physiotherapy session. Follow-up tests were performed in patients who stayed in the ICU for more than 24 hours, were not transferred to another hospital or received palliative care. Floor and ceiling effects were examined in all assessments.Results For the reliability analysis 66 patients were included.Results Showed no significant difference between raters. For the total score, intra class correlation coefficient (ICC) was 0.996 (95% CI: 0.993; 0.997), standard error of measurement was 0.72 point and minimal detectable change 2.0 points. Bland-Altman plot revealed no heteroscedacity. The responsiveness results of 24 patients showed that the effect size was 1.2 and the standardized response mean 1.1, which was in accordance with the hypothesis. No ceiling or floor effect was revealed.CONCLUSION: The CPAx-D showed excellent inter-rater reliability and responsiveness.

Enhancement of reliability is critical in designing and planning of distribution systems that operate efficiently and with minimal interruption to customer loads, because they use various types of resources and technologies to meet energy demand such as distributed generation (DG). DG is expected to play an enhanced role in emerging power systems. One of the major issues in the planning stage for future distribution systems is the optimal design of multiple micro-grids (MMGs) in distribution systems. This paper describes a novel method for clustering a traditional passive distribution system into a cluster of Multiple Micro Grids (MMGs). Clustering a traditional distribution system into MMGs provides numerous merits for costumers and distribution system operators, including a local control strategy to minimise interaction between various MGs, local reactive power compensation to prevent fault propagation, minimise power losses, and ultimately improving system reliability. In each MG area, DGs and reactive power resources are deployed to reach the acceptable level of reliability. The proposed clustering procedure is based on a weighted graph partitioning approach, with the weights representing the apparent power of the lines. Various reliability tests were performed in order to determine the best location for DG penetration in the distribution system. The passive distribution system becomes an active system after DG penetration. As a result, planners must investigate the influence of DG on distribution system reliability. The aim of this paper is to investigate the impact of DG on distribution system reliability. The results on the test distribution system demonstrate the proposed method’s performance and effectiveness.

Travel time variability (TTV) plays a significant role in analysing the reliability of public transit system. The research works carried out on travel time variability under Indian traffic conditions are very few and these studies did not analyse the performance of travel time distribution in detail, considering different temporal and spatial aggregations. In this study, travel time variability is analysed using travel time distributions considering different temporal and spatial aggregations. The Automatic Vehicle Location (AVL) data of four transit routes of Mysore City, Karnataka, India are used to evaluate travel time distributions with respect to temporal aggregations (peak period, off-peak period, 60 minutes, 30 minutes and 15 minutes) and spatial aggregations (route level and segment level). The performance of travel time distributions is analysed using the Anderson-Darling (AD) test. The segments with signalised intersections and different land-use types are analysed to evaluate the distribution fit for various conditions. The results of both route and segment level analysis report highest accuracy and robustness values for Generalised Extreme Value (GEV) distribution. The distribution is proved to be superior in describing travel time variability of public transit.

In this article, we propose an active kriging-based learning method for hybrid reliability analysis (HRA) with random and interval variables. An improved sampling strategy is proposed to target the sampling areas. Samples with maximum responses greater than 0 and minimum responses less than 0 are selected and regarded as the candidate samples; then, a U-based learning function is developed in which multiple samples of the interval are considered instead of one particular sample. To terminate the proposed method, a hybrid convergence criterion is proposed. Finally, an improved optimization strategy based on the DIRECT algorithm is developed for the Monte Carlo simulation conducted for the HRA. The performance of the proposed method is demonstrated by four numerical cases. The results illustrate that the proposed method is accurate and efficient for HRA.

Virtual reality (VR) technology improves engineering activities, enabling a better understanding of products and processes. A complete conversion from the existing CAx software to a VR environment could improve product and process design activities, leading to new practices. This article aims to provide key insights, technical evidences, and related impacts on the engineering activities of industrial companies while integrating the existing systems with VR as a supporting tool for product and process design. The results indicate improved design and limited negative impacts related to errors during the manufacturing phase. An action research study involving Oil&Gas manufacturing companies was conducted to explore the following four CAx-VR scenarios: CAD–VR for ergonomics/reachability, CAE(CFD)–VR and CAE(FEM)–VR for abstract data visualization, and CAM–VR for packaging. For each scenario, applicative cases focusing on Oil&Gas products, such as multistage centrifugal pumps and ball valves, have been investigated. Furthermore, a detailed discussion of the involved chain of software applications and impacts on the engineering activities for each scenario establishes the reliability of the integrated environment for CAD, CAE, and CAM data immersive visualization.

OBJECTIVE The Osteoarthritis Research Society International (OARSI) recommends assessment of physical function using a performance-based test of stair negotiation, but was unable to recommend any specific test. We assessed the reliability, validity, responsiveness, measurement error, and minimum important change (MIC) of the 6-step timed Stair Climb Test (SCT). METHODS We used pooled data from 397 participants with hip or knee osteoarthritis (54% women) from four clinical trials (86% retained at 12-week follow-up). Construct validity was assessed by testing six a priori hypotheses against other OARSI-recommended physical function measures. A self-reported Global Rating of Change scale was used to classify participants as worsened, improved and stable. Participants who worsened in physical function were excluded from all analyses. Responsiveness and MIC were assessed using multiple anchor-based and distribution-based approaches. Test-retest reliability, standard error of measurement (SEM) and smallest detectable change (SDC) were assessed on stable participants. RESULTS Five of six hypotheses (83%) for construct validity were met. Test-retest reliability was excellent (intraclass correlation coefficient2,1 : 0.83; 95% confidence interval: 0.71, 0.90). The SEM and SDC values were 0.44 and 1.21 seconds respectively. We did not find adequate support for responsiveness. The MIC values ranged from 0.78 to 1.95 seconds using different approaches (median=1.37 seconds). CONCLUSION The 6-step timed SCT adequately assesses the construct of physical function in individuals with hip or knee osteoarthritis with excellent 12-week test-retest reliability. However, support for its responsiveness was inadequate to recommend its use as an outcome measure in people with osteoarthritis for research and clinical practice.

Multiple cooperative task assignment is a crucial problem in spatial crowdsourcing, in which each task requires more than one appropriate user to complete. Existing studies generally assume that all users are trustworthy and can reliably perform assigned tasks. However, such assumptions do not hold in the real world. In this paper, we consider an essential crowdsourcing problem, namely Reliability-oriented Socially-Aware Crowdsourcing (R-SAC), which aims to recruit reliable users for multiple cooperative tasks so that the overall reliability of task assignment is maximized. We prove that the R-SAC problem is NP-hard. Then, we propose an approximation algorithm and provide a theoretical guarantee. Specifically, user reliability refers to the probability that a user can reliably perform assigned tasks. To achieve reliable user recruitment during task assignment, we formulate the reliability of a user by combining the matching between the user and tasks, and the reliability feedback from neighbors who share similar behaviors with the user in a social network. Besides, the distributed collaborative filtering technique is utilized to select the reliability feedback from the neighbors. Experiments on two real-world datasets validate that our proposed approach significantly outperforms the representative approaches.

Context: Various patterns of dynamic routing architectures are used in service- and cloud-based environments, including sidecar-based routing, routing through a central entity such as an event store, or architectures with multiple dynamic routers. Objective: Choosing the wrong architecture may severely impact the reliability or performance of a software system. This article’s objective is to provide models and empirical evidence to precisely estimate the reliability and performance impacts. Method: We propose an analytical model of request loss for reliability modeling. We studied the accuracy of this model’s predictions empirically and calculated the error rate in 200 experiment runs, during which we measured the round-trip time performance and created a performance model based on multiple regression analysis. Finally, we systematically analyzed the reliability and performance impacts and trade-offs. Results and Conclusions: The comparison of the empirical data to the reliability model’s predictions shows a low enough and converging error rate for using the model during system architecting. The predictions of the performance model show that distributed approaches for dynamic data routing have a better performance compared to centralized solutions. Our results provide important new insights on dynamic routing architecture decisions to precisely estimate the trade-off between system reliability and performance.

In recent years, the agricultural sector has taken advantage of real time monitoring, particularly in delicate systems such aquaculture plants. In this context there is a need to develop integrated systems for sustainable production of both vegetable crops and fish. Since a monitoring system able to collect all the involved metrics is not available on the market, a specific monitoring system called ISMaCS (Integrated Smart Monitoring and Control System) has been designed and developed to collect and analyse relevant data. A method is proposed for the validation of the measurements collected by ISMaCS based on comparisons between the data series collected by investigated and reference sensors. Firstly, experiments has been carried out recording with sensors, parameters and an indicator, called Distance between Measurements (DbM) that has been defined and calculated. DbM has been compared to two thresholds to test the sensor reliability. The first, the Identical Reference Value (IRV) was based on the characteristics of the reference sensor, whilst the second, the Acceptability Reference Value (ARV), was selected on the basis of the precision required by the application. Specifically, DbM showed that if the precision of the investigated sensor was suitable for a specific application, it quantifies the reliability of the measurements and can be used as a tool to compare and rank different sensors measuring the same metrics.

The reliability of data is important for complex systems modeling. However, it remains to be a great challenge mainly due to the lack of effective measurement of data reliability. On the condition that there exist the actual result and gold standard in a piece of data simultaneously, a data-driven method is proposed using the belief rule base (BRB) with data reliability and expert knowledge. In the proposed method, gold standards are used to indicate the degree to which the actual results are correct and, thus, the reliability of each piece of data is defined as the similarity between its actual result and the corresponding gold standard. According to the reliability of each piece of data, the training dataset is divided into multiple clusters. Each cluster of data is separately used to construct and optimize a BRB model. The weight and reliability of each model are then calculated using the data in the respective cluster with the consideration of the reliabilities of the data. Finally, the estimated results of the models are integrated using the evidential reasoning rule to generate the integrated results. The proposed method is applied to help radiologists diagnose thyroid nodules, in which historical examination reports are collected from a tertiary hospital located in Hefei, Anhui, China. Its superior performance is verified by experiments.

This study developed a measure of solidarity levels during pandemics, the Solidarity in Pandemics Scale (SPS). This was achieved using a study group of 842 individuals aged between 18 and 65 from different segments of society who had experienced the COVID-19 pandemic and differed in age, gender, and socioeconomic status. Exploratory factor analysis was used to establish the construct validity of the scale, producing a 15-item scale with a one-dimensional structure that explained 34.36% of the variance. Confirmatory factor analysis using a different study group revealed that all items have significant t-values, and the model established according to model fit indexes has meaningful and acceptable fit values. The internal consistency of the scale results was calculated using the Cronbach Alpha coefficient, and a reliability of .85 in terms of internal consistency was obtained. Test-retest reliability results as another indicator of reliability were found to be .85. Scores on the Altruism Scale were compared with those on the SPS to evaluate the scale’s criterion validity, and a significant relationship was found between the two scale scores. This analysis indicates that the Solidarity in Pandemics Scale is a valid and reliable psychometric tool.

Saliency methods, which “explain” deep neural networks by producing heat maps that highlight the areas of the medical image that influence model prediction, are often presented to clinicians as an aid in diagnostic decision-making. Although many saliency methods have been proposed for medical imaging interpretation, rigorous investigation of the accuracy and reliability of these strategies is necessary before they are integrated into the clinical setting. In this work, we quantitatively evaluate seven saliency methods—including Grad-CAM, Grad-CAM++, and Integrated Gradients—across multiple neural network architectures using two evaluation metrics. We establish the first human benchmark for chest X-ray segmentation in a multilabel classification set up, and examine under what clinical conditions saliency maps might be more prone to failure in localizing important pathologies compared to a human expert benchmark. We find that (i) while Grad-CAM generally localized pathologies better than the other evaluated saliency methods, all seven performed significantly worse compared with the human benchmark; (ii) the gap in localization performance between Grad-CAM and the human benchmark was largest for pathologies that were smaller in size and had shapes that were more complex; (iii) model confidence was positively correlated with Grad-CAM localization performance. While it is difficult to know whether poor localization performance is attributable to the model or to the saliency method, our work demonstrates that several important limitations of saliency methods must be addressed before we can rely on them for deep learning explainability in medical imaging.

Failure mode and effects analysis (FMEA) is a systematic and proactive risk prevention and management tool used to improve the reliability and safety of a system, process or service. However, many defects around the traditional FMEA affect the effectiveness of its practical applications. When dealing with the FMEA problems, the first and the most important step is to collect the risk assessment information of experts regarding failure modes. Moreover, clustering failure modes is more adaptable to actual needs, especially under a tight resource constraint. In this article, a new FMEA model using double hierarchy hesitant fuzzy linguistic term sets (DHHFLTSs) and k-means clustering is developed to evaluate and cluster the risk of failure modes. First, the DHHFLTSs are utilized to describe the complex linguistic assessments on failure modes given by FMEA team members. Next, an improved k-means algorithm is proposed and used to cluster failure modes into priority classes. Finally, a case study concerning the risk evaluation over floating offshore wind turbine is provided to illustrate the effectiveness and usefulness of our proposed FMEA. The results show that the new risk classification approach is more reasonable and practical for risk management decision making.

The extremely low frequency power amplifier (ELFPA) used in the communication system is required to have high output accuracy, small size and high reliability under complex mission profiles, simultaneously. However, the system parameters that determine these performances are coupled with each other, and it is difficult to directly select the optimal solution that meets all output performances at the same time. Thus, a new high reliable parameters design method is proposed for ELFPA system, which can simultaneously consider the relationship among multiple mutually coupled, non-linear control variables and output performances. Moreover, the multi-objective particle swarm optimization (MOPSO) algorithm is used to search for the Pareto-optimal solutions and the best design. The use of this optimization algorithm aims to avoid the empirical trial-and-error method for adjusting the parameter values, and quickly find the system parameter value that best trade-off the total harmonic distortion (THD), the volume and lifetime of the system. To illustrate the superiority of the proposed approach, the proposed parameter design process of a 5kW power amplifier is verified by both simulation and experimental results.

Extensive research efforts are being carried out to evaluate and improve the reliability of computing devices, either through beam experiments or simulation-based fault injection. Unfortunately, it is still largely unclear to which extend fault injection can provide an accurate error rate estimation at early stages and if beam experiments can be used to identify the weakest resources in a device. The challenges associated with reliability evaluation grow with the increase of complexity of the hardware and the software. In this paper, we combine and analyze data gathered with extensive beam experiments (on the final physical CPU hardware) and microarchitectural fault injections (on early microarchitectural CPU models). We target a standalone Arm Cortex-A5 and an Arm Cortex-A9 integrated in an SoC and evaluate their reliability in bare-metal and Linux-based configurations. We find that both the SoC integration and the OS presence increase the system DUEs (Detected Unrecoverable Errors) rate (for different reasons) but do not significantly impact the SDCs (Silent Data Corruptions) rate which is solely attributed to the CPU core. Our reliability analysis demonstrates that, even considering SoC integration and OS inclusion, early, pre-silicon microarchitecture-level fault injection delivers accurate SDC rates estimations and lower bounds for the DUE rates.

In this article, a deterministic model for the selection and ranking of commercial off-the-shelf (COTS) components is developed on the basis of fuzzy modified distance-based approach (FMDBA). The COTS selection and ranking problem is modeled as multicriteria decision making problem due to the involvement of multiple ranking criteria like functionality, reliability, compatibility, etc. FMDBA is the combination of the fuzzy set theory and the modified distance-based approach. To show the working of the developed ranking model, a case study of the e-payment system is demonstrated which aims on the selection and ranking of eight COTS components over four major categories of ranking criteria. FMDBA provides a comprehensive ranking of the components based on their calculated composite distance values. To depict the docility of FMDBA method, the results obtained are also compared with the existing decision-making methodologies.

The metal-based additive manufacturing (MAM) processes have great potential in wide industrial applications, for their capabilities in building dense metal parts with complex geometry and internal characteristics. However, various defects in the MAM process greatly affect the precision, mechanical properties and repeatability of final parts. These defects limit its application as a reliable manufacturing process, especially in the aerospace and medical industries where high quality and reliability are essential. MAM process monitoring provides a technical basis for avoiding and eliminating defects to improve the build quality. Based on of the nature of the MAM build defects, this article conducts a thorough investigation of monitoring methods, and proposes a machine learning (ML) framework for process condition monitoring. According to the structure of ML models, they are divided into shallow ML-based and deep learning-based methods. The state-of-the-art ML monitoring approaches, as well as the advantages and disadvantages of their algorithmic implementations, are discussed. Finally, the prospects of ML based process monitoring researches are summarized and advised.

A high-reliability organization must have high-performing teams. Core teams in healthcare should include the patient and, where possible, the patient's family. Everyone on the team should understand what is expected of them in terms of expertise, leadership, communication, mutual support, and awareness of the situation. One test of the excellence of a clinical team is having high-quality handoffs of patients.

In today’s data-filled society, institutions are abundant with information but lacking in the transformation of information to knowledge and further utilization of knowledge to support actionable change. The focus of the present study was to develop an instrument to gauge institutional maturity with regard to the level in which information is utilized to support decisions, planning, and change. The findings from a field test yielded a conceptual model to assess institutional maturity of information utilization. Additionally, the pilot of the IUMI instrument found high rates of internal reliability and may be used as a catalyst to spark institutional movement towards a culture of evidence-informed practice.

ObjectiveThe Accelerator Program for Discovery in Brain Disorders using Stem Cells (ADBS) is a longitudinal study focused on collecting and analysing clinical, neuropsychological, neurophysiological, and multimodal neuroimaging data from five cohorts of patients with major psychiatric disorders from genetically high-risk families, their unaffected first-degree relatives, and healthy subjects. Here, we present a complete description of the acquisition of multimodal MRI data along with the quality assurance (QA) and quality check (QC) procedures that we are following in this study.MethodsThe QA procedure consists of monitoring of different quantitative measurements using an agar gel and a geometrical phantom. For the already acquired data from human subjects, we describe QC steps for each imaging modality. To quantify reliability of outcome measurements, we perform test-retest reliability on human volunteers.ResultsWe have presented results from analysis of phantom data and test-retest reliability on a human volunteer. Results show consistency in data acquisition and reliable quantification of different outcome measurements.ConclusionThe acquisition protocol and QA-QC procedures described here can yield consistent and reliable outcome measures. We hope to acquire and eventually release high quality longitudinal neuroimaging dataset that will serve the scientific community and pave the way for interesting discoveries.

Cavitation is an essential problem that occurs in all kinds of pumps. This cavitation contributes highly towards the deterioration in the performance of the pump. In industrial applications, it is very vital to detect and decrease the effect of the cavitation in pumps. Using different techniques to analysis and diagnose cavitation leads to increase in the reliability of cavitation detection. The use of various techniques such as vibration and acoustic analyses can provide a more robust detection of cavitation within the pump. In this work therefore, focus is put on detecting and diagnosing the cavitation phenomenon within a centrifugal pump using vibration and acoustic techniques. The results obtained from vibration and acoustic signals in time and frequency domains were analysed in order to achieve better understanding regarding detection of cavitation within a pump. The effect of different operating conditions related to the cavitation was investigated in this work using different statistical features in time domain analysis (TDA). Moreover, Fast Fourier Transform (FFT) technique for frequency domain analysis (FDA) was also applied. Furthermore, the comparison and evaluation system among different techniques to find an adequate technique incorporating for accuracy and to increase the reliability of detection and diagnosing different levels of cavitation within a centrifugal pump were also investigated.

The purpose of this study was to assess the within-day reliability of scapular and shoulder surface electromyography (sEMG) measurements during shoulder abduction. Twenty asymptomatic individuals performed 10 repetitions of shoulder abduction, which was then retested 10 minutes later. sEMG was used to record the activity of upper and lower trapezius; anterior, middle and posterior deltoids; supraspinatus; infraspinatus; and serratus anterior muscles. Muscle activation was expressed as a percentage of maximum voluntary isometric contraction (MVIC). The intraclass correlation coefficient (ICC) and standard error of measurement (SEM) were used to assess within-day reliability for concentric and eccentric phases of shoulder abduction. For the concentric phase, ICC values ranged from 0.87 to 0.98 and SEM values from 8.6% to 18.9% MVIC. For the eccentric phase, ICC values ranged from 0.65 to 0.97, and SEM values from 9.8% to 24.4% MVIC. In asymptomatic individuals, EMG measurements of the scapular and shoulder muscles during the concentric phase of shoulder abduction had excellent reliability, and for the eccentric phase, had good to excellent reliability depending on the analysed muscle. These findings provide valuable information on reliability of sEMG for assessing muscle activity of scapular and shoulder muscles.