Real Failure Research Articles

Secondary erythrocytosis / polycythemia due to sodium glucose co-transporter 2 inhibitors: a real world heart failure cohort

Abstract Background Sodium glucose co-transporter 2 inhibitors (SGLT2-i) are one of the four pillars of guideline-directed medical therapy in heart failure with reduced ejection fraction and preserved ejection fraction according to the current ESC and AHA/ACC heart failure guidelines 1,2. As a result, there has been a significant increase in their use worldwide. Although SGLT2 inhibitors are known to increase hemoglobin and hematocrit levels, there is a lack of data on the burden of erythrocytosis or polycythemia in pivotal trials 3,4. Purpose In this retrospective cohort study, we investigated the prevalence and clinical outcomes of SGLT2-i related erythrocytosis in a tertiary heart failure outpatient clinic. Methods We reviewed clinical data from heart failure patients treated with an SGLT2 inhibitor at our tertiary centre between September 2019 and October 2023. Patients were included if they were taking SGLT2-i continuously for more than one month. Data were collected on Hb/Hct levels at baseline before initiation of SGLT2-i, at peak levels during therapy and at the last follow-up. Erythrocytosis was defined according to the 2017 WHO classification as Hb> 10.3 mmol/L and/or Hct> 49% in men and Hb> 10.0 mmol/L and/or Hct> 48% in females. Results A total of 173 patients with heart failure were included (median age 58 [49-66] years, 65% male). One hundred and fifty-six patients (90%) were using dapagliflozin, while 16 patients (9%) were using empagliflozin. The overall prevalence of SGLT2i related erythrocytosis was 39/173 (22.5%; median age 60 and 82% males). Eleven (6.3%) patients had pre-existing erythrocytosis at baseline. During a mean follow-up of 14±10 months, 28/173 (16.2%) patients developed new onset erythrocytosis. The median time to the peak of the Hb/Hct level was 6.5 months. Although the group with erythrocytosis had significantly higher Hb and Hct at baseline compared to the group without erythrocytosis (Hb 9.7 mmol/L ± 1.0 vs. 8.5 mmol/L ± 1.0; p<0.001 in men and 9.1 mmol/L ± 0.8 mmol/L vs. 8.1 ± 0.8; p=0.006 in females), they also experienced a significantly greater median increase in Hb from baseline to peak (1.05 mmol/L vs. 0.60mmol/L; p=0.01) (see Figure 1) There were significantly more males (82% vs 60%; p=0.01) in the erythrocytosis group compared to the non-erythrocytosis group. In addition, the prevalence of OSAS was significantly higher in the group with erythrocytosis compared to the group without erythrocytosis (31% vs. 8%; p<0.001). Conclusion Secondary erythrocytosis or polycythemia is common in patients with chronic heart failure. It is particularly common in men and in patients with obstructive sleep apnoea syndrome (OSAS). There were no excess thrombotic or thromboembolic events, probably because of the routine use of anticoagulants/antiplatelets and limited follow-up. To avoid unnecessary haematological referrals and potential clinical adverse events, increased awareness among clinicians is needed.Table 1.Baseline and clinical outcomesFig1.Hemoglobin levels over time

Smart Building, Nuisance Electrical Failure, Remote Monitoring and Fault Recovery System

Smart Buildings are the fastest growing buildings compared to conventional buildings and are expected to grow at a rate of 8.3% for the next decay. The global commercial building automation market was valued at US$32.96 billion in 2020 and is expected to reach US76.49 billion by the year 2031. The implementation of technological advancement, demand for energy-efficient construction, low operational cost, and occupant comfortability have helped Smart Building to grow. However, Smart Buildings are still facing issues, from various sensor network diversity, clear definitions of Smart Buildings, and continuous smartness during full or partial electrical internal fault occurrence. This indicates the building is smart as long as a continuous power supply is present. Thus, this research study investigated Smart Building's drawbacks and designed a circuit to be a solution to one of the drawbacks to rectify the issues. The design provides full monitoring for real and nuisance failures and enables the performance of fault recovery procedures remotely. The circuit interfaces with the safety Residual Current Devices (RCD) and the wireless smart Wi-Fi-controlled socket outlets. Food industries, hotels, and restaurants could be the beneficiaries of this circuit. This is due to cold stores and fridges, which can lead to significant production loss/reduction when failure time increases.

Testing coverage based NHPP software reliability growth modeling with testing effort and change-point

This paper investigates a software reliability growth model based on non-homogeneous Poisson process that includes coverage testing and change-points. The model integrate effort spent into a model to evaluate software reliability based on testing coverage. The amount of testing effort spent follows the Weibull distribution, while the amount of coverage is modeled by logistic, delayed S-shaped and exponential functions. Additionally, we look into the cost requirement-based software release time for exponential functions with a reliability constraint. We introduce the genetic algorithm, which is a powerful tool for dealing with search and optimization issues. For the purpose of testing the model's goodness of fit, two real failure datasets are used, and the model’s performance is analyzed based on goodness-of-fit metrics. By comparing the proposed model to perfect debugging models that have been described in the literature, it is demonstrated that the proposed model demonstrates notable advancements in this regard. The results of our analysis indicate that a software reliability estimation model should include change-points in a testing process if they exist.

The Economics of Geographical Indications: An Update

We discuss the economics of geographical indications (GIs), a form of collective branding of products based on their geographic origin. GIs serve as essential tools in addressing market failures stemming from information asymmetries and supporting the provision of high-quality products in competitive settings. They are increasingly used in wine and food markets. We briefly review the institutional development of GIs, discuss the basic economic theory arguments that rationalize the use of GIs, and characterize their key functions. We then examine the empirical evidence from studies that have probed various features of GIs. We find consensus on the potential for GIs to address a very real market failure and the positive role that policies and supporting institutions can play. We also uncover limitations and unresolved problems. GIs have both efficiency and distributional consequences, their international implications remain controversial, and tensions between tradition and innovation emerge while confronting new challenges such as climate change.

Modeling the impact of noise and uncertain operational environment on software release decisions considering testing coverage-based SRGM

The operational environment of software differs from the debugging environment. Therefore, the study explores the impact of irregular consumption by diverse users on the development cost of software, reliability of software, and release decisions. To accomplish this, a release model has been formulated considering logistic testing coverage-based reliability growth model built as a stochastic process including the error generation. The stochastic nature has been captured by considering the noise factor due to irregular fluctuations occurring during testing while usage uncertainty has been captured by introducing a constant parameter in the cost function of the operational phase. It is assumed that the testing phase cost is affected by the noise and the operation phase cost is affected by the severity of noise which is the result of uncertain usage by users. The model was evaluated against a real failure dataset. The release model creates a trade-off between software development cost, release timing, and reliability aspirations. This study contributes to software reliability literature and provides insights to practitioners to make software release decisions. The sensitivity analysis results give information about various aspects during the operational phase that affect the overall development cost.

A hybrid predictive maintenance approach for ship machinery systems: a case of main engine bearings

A hybrid maintenance strategy is proposed, combining reliability-centered and condition-based maintenance strategies to prevent engine failures. A case study on the lubrication deficiency of a ship's main engine bearings was conducted. The reliability-centered maintenance phase is based on reliability-increasing rules, equipment facts, and a knowledge base. Then, a strategy is developed where system parameters are continuously monitored. These parameters are evaluated in the inference engine, and maintenance recommendations are provided to the ship operator. The investment cost of th developed system is discussed using a real engine failure case. The study shows that the strategy can improve maintenance on existing ships and system reliability at low costs. The methodology offers a cost-effective approach for the sustainability of the main engine, as demonstrated by a real crankshaft failure report. Unlike existing practices, it provides influential maintenance decisions based on evaluating system parameters within four operation ranges. Simulations show it detects insufficient lubrication faults at low engine loads, superior to current applications. The proposed approach suits different types of existing and newly built ships, engines like dual-fuel, and technologies such as semi-autonomous ships.

Shaping of the risk of a series-parallel manufacturing structure maintenance according to quasi-coherence and the [formula omitted]-th survival value

The paper presents the authors’ model for determining the probability value of the survival function of the difference of two random variables: TP – the amount of time of correct operation and TB – the amount of time of failure. For this purpose, the concept of K-th survival value is introduced. The K-th survival value defines the probability that the total time of correct operation of the production system will be longer by at least k time units (for k≥0) than the total amount of time of failures. The model was exemplified by a real production system with a series–parallel structure. In the nesting manufacturing structure, the availability of machines determines the number and the type of flow streams of the processed material. To formalise the chains of relations at a given Δt, this paper utilises the quasi-coherence property. The paper identifies four possible cases of system operation: 1) a fully operational system in which every machine is capable of meeting the assumed production schedule; 2) a system which has lost the property of quasi-coherence (certain system objects are in an inoperable state) but it is possible to meet the assumed production schedule; 3) a system which has lost the property of quasi-coherence but the assumed task schedule is possible to meet after redefining the material flow streams; 4) a system which has lost the property of quasi-coherence and it is impossible to meet the assumed production schedule. Based on the definition of the survival function [1] S of the random variable T: ST:=PT>τ=1-FTτ,τ∈R, the concept of K-th survival value was introduced. For possible cases, K-th survival value was defined as: KT1,Ti=PT1-Ti≥k=1-FT1-Tik (for i=2,3,4), where T1 and Ti are random variables with given probability distributions. In turn, k is a deterministic value that determines the total number of time units of correct system operation in relation to the total failure time. The application of the developed algorithm enables the system to be analysed in terms of three aspects: (1) to determine the probability of on-time execution of tasks, taking into account historical values of machine failure and correct operation durations; (2) to determine the stability of the system based on empirical data; (3) to monitor the level of risk in real time. The paper validates the model by determining the stability of the analysed system for real machine failure data. The introduced criterion of stochastic stability of the production system has been verified for data in which distributions of correct operation times and failure times are random variables from the family of Gamma distributions. The presented model can be included in the group of multifactorial risk assessment methods.

(Invited) Towards the Next Generation of High Performance, Durable Water Electrolyzer Membranes

Proton exchange membrane water electrolysis (PEMWE) has emerged as one of the most promising methods to produce clean hydrogen at industrial scale. Incumbent membranes (such as Nafion™ N115) have proven to enable high durability in the PEMWE application. However, it is well understood that novel PEMWE stack designs require higher voltage efficiency and low effective gas crossover, which implies thin, low resistance membranes containing gas recombination catalysts (GRCs). Compared to Nafion™ N115, thin membranes are at enhanced risk of failure from both mechanical and (electro)chemical mechanisms. While promising examples of these membranes have been recently disclosed,1 a systematic demonstration of their durability has not yet been performed.In PEM fuel cells, decades of research have resulted in accelerated stress test (AST) protocols that are effective to mimic real failures while shortening the time to observe them. These ASTs have not yet been proven/implemented for PEMWE membranes. Progress toward engineering high performance, durable thin membranes will be presented, along with a perspective on how best to engineer ASTs for PEMWE. Park, A., Performance and Durability Investigation of Thin, Low Crossover Proton Exchange Membranes for Water Electrolyzers. DOE Annual Merit Review. 2020.

Fuzzy reliability algorithm for the shutdown system of research reactor

Abstract Probabilistic safety assessment (PSA) has been applied to evaluate the safety of nuclear reactor systems. The results of PSA are used by designers, utilities and regulatory body to confirm the reactor design, to change operation procedures, to enhance the reliability of safety systems, or to modify regulation. Fault tree analysis (FTA) has been used as a deductive means for PSA. System components are assumed to always have defined failure probabilities; however, in reality this assumption is questionable and sometimes there is insufficient data about the failure probability of some components. To solve this problem, fuzzy approaches have been proposed and implemented. The aim of this study is to apply a fuzzy reliability algorithm to estimate basic events failure probabilities for the shutdown system of a research reactor. The reactor is a 22 MW light water open pool type material test reactor and the shutdown system is responsible for the fast shutdown of the reactor whenever safety limits are exceeded. Then, to illustrate the ability of the algorithm, the results are compared with real basic event failure probabilities. The results verify the competence of the algorithm and confirm that it can be applied as an alternative method when quantitative data are not available. Also, a risk importance measure is done on the fault tree of the shutdown system to illustrate which components need to be focused for improvements to achieve the safe operation of shutdown system.

Evaluation of the Reliability of a Standby Redundancy System Under Real Conditions

This paper presents a novel analysis of a standby redundancy system under real conditions. The study considers the presence of a real commutator and failure detector in the system. Through a comprehensive failure mode analysis, mathematical relationships between different module characteristics are established. The results of investigation provide valuable insights for manufacturers, allowing them to evaluate the Mean Time To Failure (MTTF) of the system during the design phase and make informed decisions regarding the selection of failure rates for the detector and commutator. Overall, this work contributes to the effective operation of standby redundancy systems in practical applications.

Improved general unit hydrograph model for dam-break flood hydrograph

Dam breaks pose significant risks, necessitating simple and effective predictive models for disaster management. This study aims to enhance the understanding of dam-break flood hydrographs, an aspect less emphasized in traditional research focusing mainly on peak discharge and flow depth values. Employing a series of experiments, an improved general unit hydrograph model was used to analyze both discharge and flow depth hydrographs. This model is then critically compared with the standard gamma and log-normal asymmetric distribution models. The results reveal that the improved unit hydrograph model significantly outperforms these traditional models, particularly in predicting the rising stage of discharge hydrographs, evidenced by a 65 % reduction in root mean square error. Comparison of this model with real dam failures in natural river valleys shows its potential to be applied to changes in river slope and different dam-break rates. These advancements present a more precise methodology for predicting dam-break flood hydrographs, offering benefits for disaster management strategies.

On the prediction of power outage length based on linear multifractional Lévy stable motion

In the power system, failure interruption hugely compromises the power system reliability. Therefore, an efficient method to correctly predict how long the power outage last would significantly improve the efficiency. To address this problem, we propose a method based on a suitable stochastic motion. First, we introduce linear multifractional Lévy stable motion (LMLSM). Then, by computing the global and local fractal characteristics we show that the LMLSM is multifractal and possesses the long-range dependence (LRD) property. Besides, the non-Gaussian characteristics of the LMLSM are analyzed, which means it can better describe the constant peak values in the stochastic time series. Furthermore, a discrete iterative prediction model is derived with fractional Black-Scholes differential equation. At last, a case study is provided based on the real failure interruption duration (FID) dataset in the power grid, by illustrating the validity of the proposed prediction method for power grid reliability.

Reliability analysis of wind turbines considering seasonal weather effects

The failure rate of wind turbines shows obvious fluctuation due to seasonal environmental factors. However, few efforts have been devoted to modeling the seasonal failure rate. This paper develops a novel model that consists of a baseline failure rate function, seasonal indices, and a residual term to describe the monthly failure rate of wind turbines. A two-stage procedure is developed to estimate the 16 unknown parameters in the model. The first stage explores the relationship between the parameters in the baseline function and the monthly coefficients by maximum likelihood estimation and then integrates the properties into the genetic algorithm to estimate the main parameters. In the second stage, the variance of the residual term is estimated based on the analysis of the differences between the observed and predicted failure rates. The failure history of 48 months has been used to illustrate the proposed approach. The results show that the monthly failure rate function can well fit the real failure history of wind turbines, and it outperforms the traditional reliability model.

Understanding the Fallout: The Impact of Failed Real Estate Developments on the Property Market in Lagos State, Nigeria

This study aims to investigate the effects of failed real estate developments on the property market in Lagos State, Nigeria. Given the rapid development and high demand for housing and other assets in Lagos, the potential for project failures can cause uncertainty and disruption. The research seeks to understand the various consequences of project development failures on the property market by examining stakeholder views. The study involves analyzing the fundamental factors and outcomes of project failures and their impact on the real estate market in Lagos State. The study sampled 384 Estate Surveyors and Valuers (ESV) and 225 staff from the Real Estate Developers Association of Nigeria (REDAN). Regression analysis (OLS) was used to examine the impact of real estate development failure on the market. The study found that technical factors, also known as technical failures, had a significant negative influence on the real estate market in Lagos State. This conclusion was based on negative regression coefficients (-0.68 for ESV and -0.68 for REDAN) and significant t-statistics values (-8.18 for ESV and -4.90 for REDAN). Furthermore, factors such as inadequate project finance, lack of management support, failure to adhere to required regulatory guidelines, and socio-political factors also had negative regression coefficients. They were found to influence the real estate market in Lagos state. In conclusion, these factors could affect the quality of real estate development, leading to a decrease in the real estate market and patronage.

Advanced Characterization Development for Metal Anodes in Aqueous Batteries

Renewable penetration of the electric grid necessitates inexpensive and safe energy storage solutions. With these priorities, aqueous batteries with metal anodes (Zn, Al, Mg, Sn, etc.) are an exciting area of development as they have low material costs, inherent safety, and high theoretical energy density. Understanding and quantifying the faradaic and chemical reactions occurring in aqueous alkaline batteries with metal anodes necessitates probing the solid, liquid, and gaseous phases that evolve during cycling. The interplay of plating, stripping, precipitation, disproportionation, and parasitic gases generation complicates our understanding of metal anode cycling behavior and often muddle the real failure mechanisms in aqueous batteries.In our work, we have developed a robust toolbox of direct characterization tools to investigate metal anodes in aqueous batteries. Our investigation focuses on tin (Sn) as a largely unexplored metal anode and characterizes its performance and speciation during cycling. We will present a variety of methods to probe the charge/discharge speciation and efficiency losses to parasitic side reactions, including operando XRD, in-situ gas detection, RRDE, EQCM, and liquid 119Sn NMR. These techniques have allowed us to develop a mechanistic understanding of the Sn metal anode system and construct a high coulombic-efficiency, high utilization, and long cycle-life aqueous battery.

Group Maintenance for Numerical Control Machine Tools: A Case Study

The high-reliability requirement of numerical control (NC) machine tools emphasizes the effectiveness of preventive maintenance (PM). However, most existing PM decisions are made by treating them as single-component systems, resulting in the problems of “excessive maintenance” or “insufficient maintenance.” Moreover, real failure data has rarely been utilized for maintenance decision-making, which leads to idealized policies far from practice. This article presents a framework to derive the optimal group maintenance (GM) policy from real failure histories for an NC machine tool comprised of heterogeneous components. Criticality analysis is first conducted to identify high-risk and low-risk components. The lifetime of each high-risk component is characterized by a two-parameter Weibull model or by a Weibull competing-risk model according to whether its failures are independent of the other components. High-risk components with strictly increasing or bathtub-shaped failure rates are selected for PM decision-making. This article introduces a multilevel PM decision-making approach: 1) a repair-replacement model is developed at the component level to derive the optimal PM interval and the maximum number of PM actions; and 2) at the system level, a GM procedure is presented to obtain the optimal PM interval of the system. The failure histories of an NC machine tool with 13 components are used to illustrate the effectiveness of the proposed approach. A maintenance plan is finally made based on optimization results to guide maintenance implementation.

Clinician perceptions and reasons for non-use of guideline-directed medical therapy for heart failure with reduced ejection fraction in the United States

Abstract Background Despite evidence that guideline-directed medical therapy (GDMT) improves outcomes in heart failure with reduced ejection fraction (HFrEF), many eligible patients remain untreated in the United States (US). Purpose To explore reasons why patients are not receiving GDMT and physician perceptions in management of their patients. Methods We conducted a cross-sectional survey of HF treatment practices (Adelphi Real World Heart Failure Disease Specific Programme) between August 2022 and February 2023, following publication of the 2022 US HF guidelines recommended that patients with HFrEF receive: angiotensin receptor/neprilysin inhibitors (ARNI), beta-blocker, mineralocorticoid receptor antagonists (MRA), and SGLT-2 inhibitors (SGLT2i). A total of 63 cardiologists and 39 primary care physicians in the US provided clinical and treatment patterns data on adult patients with HFrEF (ejection fraction ≤ 40%) and completed an attitudinal survey detailing reasons for treatment initiation. Results The study group included 323 patients with HFrEF (median age of 67 years [IQR 56-74]; 39% females) of which 59.4% were White, 19.8% African American and 9.6% Hispanic; half of the sample was insured by Medicare (50.2%), followed by commercial insurance (36.5%). GDMT prescription rates are presented in Table 1. Overall, 57.3% were not prescribed ARNI (185/323), 16.4% (53/323) were not prescribed a beta-blocker; 71.5% (231/323) were not prescribed MRA, and 65.3% (211/323) were not prescribed SGLT2i. Among patients not receiving GDMT, the most frequently reported physician reason for not prescribing ARNIs (54.1%), beta-blockers (43.4%), MRAs (54.5%) or SGLT-2is (53.6%) was physician deemed the patient to be clinically stable (Table 2). Most physicians (93.5%) reported being satisfied with treatment for patients with HFrEF and most (80.3%) believed that their patients with HFrEF were fully compliant with their treatments. Most physicians (91.2%) stated that they based their treatment decisions on both clinical experience and laboratory tests, as opposed to laboratory tests/imaging only (9.8%) or personal judgement only (16.7%). Conclusions In this cohort of patients with HFrEF from the US, the most common primary reason physicians reported for their patients not receiving GDMT was that the patient was clinically stable. Patient medication costs, contraindications, and proven intolerance were each reported as primary reasons for non-treatment in only a small minority of cases. These data suggest a strong culture of clinical inertia and lack of therapeutic urgency as the dominant driver of large gaps in use of GDMT in the US.GDMT prescriptions in HFrEF (n = 323)Physician-reasons for no GDMT

Dynamic mechanical properties of different types of rocks under impact loading

To study the mechanical properties of different types of rocks under impact loading, static mechanical parameter tests and split-Hopkinson pressure bar (SHPB) dynamic impact experiments were conducted on five typical rock specimens. The mechanical properties and failure modes of different rock specimens under the same static and dynamic loading were investigated. The differences between numerical simulation results and laboratory test results under different constitutive models in LS-DYNA were also compared and analyzed. The results show that with the increase of SHPB impact pressure (0.5–0.8 MPa), the stress peak values of granite, marble, and limestone also increase, while gypsum and reef limestone follow no particular trend. At the same time, both HJC and RHT constitutive models can simulate the laboratory impact test results of granite, marble, and limestone, however, the gypsum and reef limestone are not modelled by the HJC constitutive model, while the RHT constitutive model can describe the deformation-damage-failure process of rock specimens with different strengths. Therefore, the RHT model can better reflect the real deformation and failure of rocks.

Sensitivity analysis on mechanical parameters of continuum models for simulating in-plane tests on existing masonry panels

Understanding and modeling the structural behavior of existing masonry buildings is a fundamental task in view of their management and conservation, especially in seismic-prone areas. Among the different modeling approaches for the computational analysis of unreinforced masonry structures, this work investigates the ability of two material continuum models, the modified masonry-like material and Mazars’ material, to simulate real in-plane failure modes of masonry panels, considering the influence of the variability of mechanical parameters on the results. As the first part of a broader benchmark, this work focuses on the study of two recurring test patterns for masonry panels subject to both horizontal and vertical loads, given the availability of experimental data. To account for the parameter variability that typically affects masonry materials, uncertainty propagation is performed in a probabilistic framework, and a gPCE-based proxy model makes it possible to turn time-consuming numerical runs into significantly faster analytical evaluations. Sensitivity analyses are carried out in terms of Sobol’ indices to evaluate the influence of input variability on the ultimate horizontal load. The most relevant masonry parameters for each model are thus highlighted and the issue of model calibration is finally addressed by relying on Bayesian inverse methods. This work also underlines the pros and cons of the different hypotheses, which underlie the two nonlinear constitutive laws.

Application of Machine Learning Coupled with Stochastic Numerical Analyses for Sizing Hybrid Surge Vessels on Low-Head Pumping Mains

In surge protection, low-head profiles are deemed a challenge in pump failure events since they are prone to severe negative pressure surges that require an uneconomical surge vessel volume. A hybrid surge vessel with a dipping tube can provide required protection with reasonable economic volume. This work presents novel analyses for the hybrid surge vessel and develops a simple model for its optimum sizing using a stochastic numerical approach coupled with machine learning. Practical ranges for correct sizing of vessel components, such as ventilation tube, inlet/outlet air valves, and compression chamber, are presented for optimal protection and performance. The water hammer equations are iteratively solved using the hybrid surge vessel’s revised boundary conditions within the method of characteristics numerical framework to generate 2000 cases representing real pump failures on low-head pipelines. Genetic programming is utilized to develop simple relations for prediction of the hybrid vessel initial and expanded air volumes in addition to the compression chamber volume. Moreover, the developed model presented a classification index for low-head pipelines on which the hybrid vessel would be most economical. The developed model yielded good prediction error statistics. The developed model proves to be more accurate and easier to use than the classical design charts for the low-head pumping mains. The model clearly showed the relation between various hydraulic and pipe parameters, with pipe diameter and static head as the most influencing parameters on compression chamber volume and expanded air volume. The developed model, together with the classification indices, can be used for preliminary surge protection sizing for low-head pipelines.