Level Of Reliability Research Articles

Trust with increasing and decreasing reliability.

The primary purpose was to determine how trust changes over time when automation reliability increases or decreases. A secondary purpose was to determine how task-specific self-confidence is associated with trust and reliability level. Both overtrust and undertrust can be detrimental to system performance; therefore, the temporal dynamics of trust with changing reliability level need to be explored. Two experiments used a dominant-color identification task, where automation provided a recommendation to users, with the reliability of the recommendation changing over 300 trials. In Experiment 1, two groups of participants interacted with the system: one group started with a 50% reliable system which increased to 100%, while the other used a system that decreased from 100% to 50%. Experiment 2 included a group where automation reliability increased from 70% to 100%. Trust was initially high in the decreasing group and then declined as reliability level decreased; however, trust also declined in the 50% increasing reliability group. Furthermore, when user self-confidence increased, automation reliability had a greater influence on trust. In Experiment 2, the 70% increasing reliability group showed increased trust in the system. Trust does not always track the reliability of automated systems; in particular, it is difficult for trust to recover once the user has interacted with a low reliability system. This study provides initial evidence into the dynamics of trust for automation that gets better over time suggesting that users should only start interacting with automation when it is sufficiently reliable.

Seismic performance and failure mechanisms evaluation of multi-story elliptic and mega-elliptic bracing frames: Experimental and numerical investigation

In recent decades, researchers have evaluated the seismic performance of the innovative Elliptic-Braced Resisting Frames (ELBRFs) only in single-story single-span configurations. Although numerical studies have investigated the behavior of multi-story ELBRF configurations, the lack of laboratory data has cast doubt on the reliability of these numerical results. To address this gap in knowledge, this article evaluates the seismic performance and failure mechanisms of multi-story ELBRFs through a laboratory program and compares them with a developed type of this bracing system known as Mega Elliptic-Braced Resisting Frames (MELBRF). The key contribution of this research is the provision of laboratory test data for multi-story ELBRF and MELBRF systems, which can be utilized to validate numerical models and investigate their seismic characteristics. In this study, laboratory tests are used to examine the cyclic behavior and to calculate parameters such as strength, ductility, stiffness, energy dissipation, seismic performance, and failure modes in multi-story specimens. To this end, an experimental test of a 1/6 scale single-span four-story ELBRF specimen and a two-span four-story MELBRF specimen under cyclic quasi-static loading was conducted. Next, the seismic behavior of the proposed specimens is compared with other types of bracing systems such as X-, V-, Inverted-V, Two-Story X-, and Two-tiered diagonal braced frames in a story-base model under cyclic quasi-static loading through nonlinear FEM analyses. The results indicated that the yielding of elliptic braces would delay the failure mode of adjacent elliptic columns and thus help tolerate significant nonlinear deformation to the point of ultimate failure. The response modification factor in ELBRF and MELBRF is 7.3 and 6.5, respectively. Symmetrical behavior, high energy absorption, appropriate stiffness, and high ductility in comparison with conventional systems are some of the advantages of the proposed systems.

A New Adaptation of the Authentic Leadership Questionnaire in Turkish: Evidence From Nursing.

There is a wide range of leadership approaches, and it is important to measure the types and impact of leadership in nursing. This study aimed to adapt the Authentic Leadership Questionnaire and test its suitability in the Turkish nursing context. Employing a two-phase cross-cultural research design in accordance with STROBE guidelines, language adaptation was conducted following ISPOR guidelines. Subsequently, data were collected from a sample of 382 nurses across three hospitals in İzmir, Turkey. The confirmatory factor analysis confirmed the four sub-scale structures and indices revealed an adequate fit. The content validity index score was 0.97 for the questionnaire and greater than 0.80 for its items, demonstrating excellent validity. The intraclass correlation coefficient obtained was 0.80, indicating a good level of reliability. The findings confirm the questionnaire has the potential to determine the extent and impact of authentic leadership being practiced in hospital settings. It could be used in a range of hospital settings to ensure that accurate evidence concerning authentic leadership is gathered and used to inform the further development of nursing leadership more widely.

Enhanced support vector machine-based moving regression strategy for response prediction and reliability estimation of complex structure

For predicting response property and estimating reliability level of complex structure, enhanced support vector machine-based moving regression (MR-ESVM) strategy is proposed based on support vector machine (SVM), heuristic algorithm and moving least square (MLS) technique. Under this strategy, we develop four different SVM models including SVM-based moving regression (MR-SVM), SVM-based improved moving regression (IMR-SVM), improved SVM-based moving regression (MR-ISVM) and bi-optimized SVM-based moving regression (BiOMR-SVM) methods. In these developed MR-ESVM approaches, the MR-SVM method is explored by introducing the MLS technique into the SVM model; the IMR-SVM method is discussed by fusing the MR-SVM method and artificial rabbits optimization (ARO), and the ARO is used to search the optimal radius of compact region; the MR-ISVM method is raised by integrating the ARO into the MR-SVM, and the ARO is applied to find the optimal values in the SVM model; The BiOMR-SVM method is emerged by merging the IMR-SVM and MR-ISVM methods. To verify the effectiveness of these developed MR-ESVM strategies, a multivariate nonlinear function approximation is implemented to illustrate the advantages from the mathematics perspective, an aeroengine turbine blisk radial deformation reliability analysis and an aircraft hydraulic system low pressure reliability analysis are derived to demonstrate the applicability in engineering practice. The analytical results show that these four MR-ESVM approaches hold excellent merits in modeling features and simulation characteristics. The efforts of this work provide a novel idea for the response prediction of complex structure, and enrich the reliability estimation principle of surrogate models of complex structure.

Shear band analysis of silt-clay transition soils under three-dimensional stress-strain conditions

This paper investigates shear banding as a possible failure mode for silt–clay transition soils under general three-dimensional stress conditions. Drained and undrained true triaxial tests with constant b values were performed on tall prismatic specimens of such soils with systematically varying silt contents. Based on the values of critical plastic hardening modulus, shear banding does not govern the strength characteristics of the soils for b values less than 0.2. For larger b values, shear band formation is essentially critical as it takes place in the hardening regime of the stress–strain curves prior to the smooth peak failure points. An increase in silt content appears to move the onset of shear banding to lower levels of shear in the stress–strain relations of the silt–clay transition soils. It is also demonstrated that a non-associated constitutive model with a single hardening law is capable of accurately predicting the onset of shear banding in normally consolidated silt–clay transition soils based on bifurcation theory.

Federated Learning-Based Authentication and Trust Scoring System for Cloud IoT Security

Abstract: A number of IoT-related security challenges are faced due to the introduction of IoT devices in almost all sections, including smart homes and industrial applications. New security solutions will be required in IoT networks to tackle issues pertaining to privacy, scalability, and efficiency with solid robustness against potential vulnerabilities. Even though some occasions these are effective centralized security frameworks suffer from significant limitations, including single points of failure, increased latency, and risks associated with data centralization [1][2]. These problems worsen as IoT networks grow in size and complexity. The below is the Federated Learning-Based Authentication and Trust Scoring System proposal toward overcoming these challenges while keeping in view the benefits offered by FL. It enables IoT devices to jointly train a global model for anomaly detection without raw data sharing [3]. This happens in a distributed manner so that data privacy is ensured, with scalability and network performance improving [4]. With this, there is a dynamic trust scoring mechanism that evaluates each device's reliability in accordance with the behavioral patterns and history of interactions [5][6]. When combining FL with the trust scoring mechanism, the proposed solution would allow an IoT network to be autonomously able to identify anomalies and manage security. This system has shown practical applicability in such experiments in both the smart home and industrial environments to enhance IoT security, retain scalability, and maintain high privacy standards [7]. The adaptability of the system coupled with federated learning makes it apt for a variety of IoT ecosystems [8]

Optimizing Cardiomegaly Detection: A Random Forest Approach to Processed Chest X-ray Imagery

This study explores the application of a Random Forest Classifier for the automated detection of Cardiomegaly from chest X-ray images, utilizing a dataset processed and derived from the NIH Chest X-ray Dataset. Given the crucial need for accurate and timely diagnosis of Cardiomegaly to inform appropriate treatment decisions, this research aims to determine the efficacy of machine learning models in augmenting diagnostic processes. Employing image pre-processing techniques such as Sobel filtering for edge detection and Hu Moments for feature extraction, the study enhances the input features for the model. The performance of the classifier was evaluated using a 5-fold cross-validation approach, yielding results with average accuracy, precision, recall, and F1-scores ranging approximately between 52% and 54%. These findings suggest a moderate level of reliability and consistency, indicating the potential utility of ensemble machine learning methods in medical imaging analysis. However, the variability in performance across different data subsets highlights the challenges and necessitates further optimization. This research contributes to the ongoing discourse on integrating machine learning into clinical settings, demonstrating the potential benefits and current limitations. Future research is recommended to expand the dataset variety, integrate advanced deep learning methodologies, and rigorously test these models in clinical environments. The findings hold significant implications for the development of automated diagnostic tools in healthcare, potentially leading to enhanced diagnostic accuracy and efficiency.

Inference for big data assisted by small area methods: an application on sustainable development goals sensitivity of enterprises in Italy

Abstract In this study, we proposed a new method for estimating the sensitivity of enterprises in Italy to the United Nation’s sustainable development goals at the provincial level using web-scraping data (a nonprobability sample) because this value is not surveyed by the Italian National Institute of Statistics. The proposed method used a probability sample to reduce the selection bias of estimates obtained from the nonprobability sample in the context of small area estimation and integrated nonprobability and probability samples using a double robust estimator that combined (i) propensity weighting to improve the representativeness of the nonprobability sample and (ii) a statistical model to predict the units that were not in the nonprobability sample. A bootstrap procedure for estimating variance was also proposed. To validate the proposed method, a Monte Carlo simulation was performed. Results showed that the proposed method allowed the correction of bias from the nonprobability sample while maintaining a good level of estimate reliability.

Measurement of Science Literacy Skills of Elementary School Teacher Education Students: Development and Validity Testing of Assessment Instruments

This study aims to develop and test the validity and reliability of a scientific literacy instrument for students of the Elementary School Teacher Education (PGSD) Program. The instrument was designed to measure three dimensions of scientific literacy: content knowledge, procedural knowledge, and epistemic knowledge, focusing on Earth and Space Science topics. A total of 18 questions were constructed based on indicators that assess the ability to explain scientific phenomena, evaluate scientific investigations, and interpret scientific data. The instrument was tested on 33 PGSD students at Universitas Sebelas Maret (UNS) and analyzed using the Content Validity Index (CVI) and reliability tests via SPSS software. The results indicated that most items were valid, with an S-CVI/Ave value of 0.981 and an S-CVI/UA value of 0.8333. However, one item was found invalid with an I-CVI score of 0.67. The reliability test showed that some questions had a good level of reliability, while others required revision due to low reliability. Additionally, analysis using the Item Response Theory (IRT) revealed that the questions varied in difficulty and discrimination, with most questions having positive discrimination values and a range of difficulty levels. Overall, the instrument was found to be valid and reliable for assessing the scientific literacy of PGSD students, although some questions need revision to improve consistency and accuracy.

Comparison of Ridaforolimus-Eluting and Zotarolimus-Eluting Coronary Stents: 5-Year Outcomes From the BIONICS and NIREUS Trials.

The BIONICS (BioNIR Ridaforolimus-Eluting Coronary Stent System in Coronary Stenosis) and the NIREUS (BioNIR Ridaforolimus Eluting Coronary Stent System [BioNIR] European Angiography Study) randomized clinical trials showed noninferiority of the ridaforolimus-eluting stent (RES) compared with the zotarolimus-eluting stent (ZES) with respect to 1-year target-lesion failure and 6-month angiographic late lumen loss. We aimed to evaluate clinical outcomes between treatment groups over a 5-year follow-up. Patient-level data from the BIONICS (n=1919) and NIREUS (n=302) were pooled, comparing the outcomes of patients implanted with RES and ZES. The primary end point was the 5-year rate of target-lesion failure. A total of 2221 patients (63.2±10.3 years, 79.7% men) undergoing percutaneous coronary intervention with RES (n=1159) or ZES (n=1062) were included. Most clinical and angiographic characteristics were similar between groups. At 5 years, the primary end point of target-lesion failure was similar between treatment groups (12.2% RES versus 11.3% ZES, P=0.52). Rates of TLR (7.6% RES versus 6.8% ZES, P=0.42) target-vessel-related myocardial infarction (4.8% RES versus 4.9% ZES, P=0.95) and stent thrombosis (0.9% RES versus 0.9% ZES, P=0.87) also did not differ between groups. Target-vessel revascularization and cardiac death were higher among the RES group (12.3% versus 9.5% P=0.037, and 3.6% versus 2.2% P=0.042, respectively). However, after correction for baseline characteristics, there was no significant difference in cardiac death between groups. In a pooled analysis of 2 randomized trials, 5-year clinical outcomes were similar between patients undergoing percutaneous coronary intervention with RES and ZES. These results support the long-term safety and efficacy of RES for the treatment of patients with coronary artery disease.

Analyzing the Relationship between Risk Management Strategies and Supply Chain

Purpose: The purpose of this article examined how different risk management strategies affect supply chain resilience in Malaysia. It aims to identify effective strategies, such as supplier diversification and digitalization, that help Malaysian companies mitigate disruptions. The findings offer insights to enhance operational continuity and adaptability within Malaysia's unique risk environment. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that effective risk management strategies, like supplier diversification and real-time monitoring, significantly enhance supply chain resilience in Malaysia. Companies adopting these strategies experienced fewer disruptions and quicker recovery times. However, challenges such as high costs and limited technology access still hinder broader adoption among smaller firms. Unique Contribution to Theory, Practice and Policy: Resource-based view (RBV), contingency theory & high reliability theory (HRT) may be used to anchor future studies on the relationship between risk management strategies and supply chain resilience in Malaysia. Practically, companies should implement risk management strategies that are tailored to their specific supply chain contexts and risks. At the policy level, governments and industry bodies should support the adoption of resilience-enhancing technologies by offering financial incentives or subsidies, particularly for small- and medium-sized enterprises.

Investigating the Reliability of Heating, Ventilation, and Air Conditioning Systems Utilized in Passenger Vehicles

A Heating, Ventilation, and Air Conditioning (HVAC) system is often utilized in passenger vehicles to enhance the comfort of both the driver and the passengers. The reliability of an HVAC system refers to the probability that a component within the system will fulfil its intended function during a specified timeframe while operating according to the predefined operational and environmental conditions. Conducting a reliability analysis for the HVAC system of a passenger vehicle is crucial to ensure safety, comfort, cost-effectiveness, and a positive standing. A methodology for analyzing the reliability analysis of a HVAC system using field failure data were developed to identify the critical failure modes, components, and subsystems. A detailed Pareto analysis was applied at subsystem and failure mode levels in order to prioritize them accordingly to their failure frequency. The analysis showed that the A/C evaporator and blower front sides were observed to be the most critical subsystems, contributing to approximately 50% of all failures. Furthermore, the leakages at the joints and vibrations are the primary failure modes of the HVAC system. The Weibull++ software package (version 2021) was used to estimate the best-fit probability distributions for each subsystem and system reliability modelling using a Reliability Block Diagram. The results show that the exponential distribution fits well for several subsystem’s Time-To-Failure (TTF) data and show that the failures were random and due to external reasons.

Game on: a performance comparison of interpolation techniques applied to Shamir’s secret sharing

Abstract Public-key encryption is typically managed through a public key infrastructure. However, it relies on a central control point, the certification authority, which acts as a single point of failure. Recent technological advancements have led to the need for decentralized cryptographic protocols. This paper presents a comprehensive study on enhancing public-key encryption via threshold cryptography and multiparty computation to ensure robust security in decentralized systems. The focus lies in exploring various polynomial interpolation techniques within Shamir’s secret sharing scheme, particularly addressing the efficiency and practicality of Newton interpolation, fast Fourier transformation (FFT), and advanced versions of Lagrange’s method. Utilizing SageMath for a dedicated testing environment, the research investigates the swiftest interpolation methods for secret recovery, introducing new shares into the system, and evaluating the impact of optimizations on performance. The findings highlight FFT as the most effective interpolation method in speed and efficiency, albeit with limitations on the number of shares that can be processed. This paper critically evaluates these interpolation techniques against practical constraints and aims to answer pivotal research questions regarding the optimal approach for large-scale scenarios, challenging existing notions on the efficiency of Newton’s method and providing experimental evidence to support the superiority of FFT in specific contexts.

Critical heart failure associated with beta-blocker-induced cardiac phospholipidosis: a case report

Critical heart failure associated with beta-blocker-induced cardiac phospholipidosis: a case report

Simplified Gravity Load Collapse Dynamic Analysis of Old-Type Reinforced Concrete Frames

The results of shaking table tests from previous studies on a one-story, two-bay reinforced concrete frame—exhibiting both shear and axial failures—were compared with nonlinear dynamic analyses using simplified models intended to evaluate the collapse potential of older reinforced concrete structures. To replicate the nonlinear behavior of columns, whether shear-critical or primarily flexure-dominant, a one-component beam model was applied. This model features a linear elastic element connected in series to a rigid plastic, linearly hardening spring at each end, representing a concentrated plasticity component. To account for strength degradation through path-dependent plasticity, a negative slope model as degradation was implemented, linking points at both shear and axial failure. The shear failure points were determined through pushover analysis of shear-critical columns using Phaethon software. Although the simplified model provided a reasonable approximation of the overall frame response and lateral strength degradation, especially in terms of drift, its reduced computational demands led to some discrepancies between the calculated and measured shear forces and drifts during certain segments of the time-history response.

Reliability of online visual and proprioceptive feedback: impact on learning and sensorimotor coding.

Multisensory integration is essential for learning and sensorimotor coding, facilitating learners' adaptation to environmental changes. Recent findings confirm that introducing unreliability into visual feedback enhances the use of motor coding, probably because proprioceptive cues are given greater weight. The present study was designed to test this hypothesis and, more generally, to explore the impact of visual versus proprioceptive cue reliability on learning processes. Participants performed a 12-target pointing sequence 100 times with different combinations of visual and proprioceptive feedback: reliable versus unreliable. Retention tests and intermanual transfer tests were administered 24h later. Results showed that learning and sensorimotor coding were both affected by the different combinations of visual and proprioceptive cue reliability. Fully reliable feedback allowed for the best retention, while fully unreliable feedback resulted in the worst retention. Visual reliability alone mediated the level of visuospatial coding performance in visuospatial transfer, regardless of the level of proprioceptive reliability, and conversely, reliable proprioception combined with unreliable vision provided the optimum sensory environment for motor coding in the motor transfer test. Overall, our study highlighted the essential role of both visual cue reliability and proprioceptive cue reliability -and their interactions- in motor learning and its generalization.

Reliability analysis of DDV and SPM in BOP-based Weibull model and expand fault dataset using deep learning

The hydraulic systems of deepwater Blowout Preventers (BOPs) are crucial for ensuring the reliability and safety of offshore oil and gas extraction operations. A functional failure can lead to uncontrolled blowouts, resulting in casualties and significant economic losses on the rig. The Direct Drive Valve (DDV) and the Subsea Plated Mounted (SPM) valve are key components that help maintain the proper functioning of the hydraulic system in deepwater BOPs. This study begins by utilizing the Weibull analysis method to assess the reliability of the DDV and SPM valves using limited fault data samples. To enhance the accuracy of predictions, Weibull parameters are estimated through various methods, including Maximum Likelihood Estimation (MLE), Least Squares Estimation (LSE), and a combination of Correlation Coefficient Optimization with Support Vector Regression (CCO + SVR).Given the challenges in gathering extensive fault data for DDV and SPM valves—due to complex subsea environments, cost constraints, time limitations, and other factors—this study proposes a method employing a Back Propagation Neural Network (BPNN) model to augment the limited fault data samples. To ensure the reliable operation of the DDV and SPM valves, preventive maintenance cycles are established at 2840 and 7550 operations, respectively. At the same reliability level, as the number of operational cycles increases, the remaining service life of the valves gradually decreases, leading to a higher probability of failure over a shorter timeframe. The Mean Remaining Life (MRL) of the DDV and SPM valves, corresponding to different operational times, is analyzed, providing essential reference points for their usage and maintenance. When the extended data sample is utilized for reliability evaluation, the reliability characteristics of the small fault data samples are effectively reflected, and the parameter prediction error remains low. This indicates that the extended data sample is more suitable for reliability evaluation.

Functionalized Triangular Carbon Quantum Dot Stabilized Gold Nanoparticles Decorated Boron Nitride Nanosheets Interfaced for Electrochemical Detection of Cardiac Troponin T.

The fast, and highly sensitive estimation of cardiac troponin T (cTnT) is crucial for the early identification of acute myocardial infarction (AMI). The electrochemical immunoassay-based (EIB) sensors are highly promising for this purpose, as they offer precise measurements and can be directly assessed in intricate matrices, including blood. To increase sensitivity, EIB sensors use nanomaterials or amplification processes, which can be laborious to develop. With this, we develop an electrochemical immunosensor for the sensitive detection of cardiac troponin T (cTnT). The sensing platform is composed of functionalized triangular carbon quantum dots stabilized gold nanoparticles which are integrated with boron nitride nanosheets (caf-TCQDs@AuNPs on HO-BNNS). Ferrocene carboxylic acid (Fc-COOH) serves as the signal label. The composite was developed and examined using several techniques such as X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), cyclic voltammetry, and chronocoulometry. The caf-TCQDs@AuNPs supported on HO-BNNS, have a large surface area and excellent electrical conductivity, and serve as an effective platform for the immobilization of anti-cTnT monoclonal antibodies via carbodiimide coupling. The Fc-COOH, functioning as a signal label through the oxidation process, was integrated with caf-TCQDs@AuNPs on the HO-BNNS platform to establish an electrochemical immunosensor for the detection of cTnT. The electrochemical immunosensor demonstrated excellent performance for the determination of cTnT under optimal conditions, exhibiting a linearity range spanning from 0.0001 to 100 ng mL-1, accompanied by a low detection limit of 0.0013 ng mL-1. Notably, the immunosensor revealed high specificity, as well as excellent levels of reproducibility and reliability for the examination of human serum samples.

Case study of a world hour record simulation in an elite cyclist: Insight into task failure.

The 'cycling hour-record' is one of the most prestigious events in cycling. However, little detailed analysis of such attempts is available. In preparation for a successful cycling hour-record attempt, an elite cyclist performed a full-hour simulation to provide insights into performance, physiological, aerodynamic and biomechanical limitations that could be identified in the preparation for a subsequent official attempt. Performance (speed, lap time, power and cadence), physiological (heart rate and estimated body temperature), aerodynamic (CDA, helmet angle, rotation and rock) and biomechanical (helmet, thigh and foot position changes) measurements were made throughout the attempt, in which an even-pacing strategy was employed where the point of task failure was defined as the lap which the rider could no longer perform at the targeted lap split (16.6s) or quicker. The cyclist did not achieve the target distance (54,000m) during the simulation. The final distance achieved for the hour was 53,250m. Task failure occurred at 38min and 33s (lap 139/34,750m) into the simulation. Notably, there was a decrease in power output, accompanied with an increase in the estimated body temperature, changes in pedalling kinematics and an increase in aerodynamic drag. The reduction in performance (leading to task failure) during a cycling hour record simulation is underpinned by a decrease in power output as well as an increase in aerodynamic drag due to biomechanical changes in the cycling technique.

Managing Angiography Unit Failure in Interventional Radiology: Lessons in Crisis Management and Considerations in Prevention.

Functional and efficient medical equipment is at the core of modern healthcare delivery, particularly in medical imaging. Growing healthcare costs and constrained budgets can delay equipment renewal. Aging equipment risks malfunction, potentially causing injury to patients and staff, and downtimes delaying patient care and impacting departmental revenue. Extensive equipment failure can lead to significant operational disruption which can compromise the delivery of timely and quality healthcare. Although extensive equipment failure is uncommon, 2 interventional radiology divisions at tertiary academic hospitals in Canada and the UK recently faced such a crisis. Their experiences of crisis and recovery inform this review of angiography equipment failure, and the principles learned. The concept of organizational resilience is introduced as a framework through which we review the crises. This concept can be split into successive and cooperative stages of anticipation, coping, and adaptation. Resilient organizations can identify potential threats, cope with unexpected crises, and recover swiftly to ensure future success. The author's experience of critical angiography unit failure, their response, and lessons learned are reviewed. We find these principles are broadly applicable to other medical imaging divisions and relevant to any system reliant on technology for healthcare delivery.