Failure Classification Research Articles

Reliability Analysis on Truck Based on Weibull Distribution

The article addresses the shortcomings in reliability analysis and modeling between various subsystems in a certain type of truck. By organizing and analyzing the failure data of each subsystem, the reliability modeling of the entire vehicle and its subsystems is established based on the Weibull distribution. After that, using hypothesis tests: linear correlation coefficient tests and D-tests to evaluate the parameter estimation structure. Additionally, the paper discusses the importance of each subsystem within the overall vehicle model and conducts failure analysis and failure classification evaluation. The reliability analysis of this truck reveals that, in terms of importance, the frame has the greatest impact on the overall vehicle reliability. In terms of failure analysis, the clutch and the reducer present a higher degree of failure hazard to the vehicle system. The findings of this paper provide essential research methods and references for subsequent discussions and analyses of this type of truck.

Abstract 4121812: Longitudinal Trajectory-Based Classification of Heart Failure with preserved Ejection Fraction: A Machine Learning Approach

Background: Heart failure (HF) with preserved ejection fraction (HFpEF) is a complex syndrome characterized by heterogeneous pathophysiology and dynamic changes in cardiac structure and function. Current research predominantly focuses on left ventricular ejection fraction (LVEF) or left ventricular structures with predefined categories. This study hypothesized that applying unsupervised methodologies to longitudinal echocardiography will reveal distinct trajectory patterns in cardiac structure and function related to clinical outcomes in HFpEF patients. Methods: This retrospective cohort study, conducted at UC Davis Medical Center (2014 to 2022), consisted of adult patients with HFpEF. The inclusion criteria included ICD HF codes, a left ventricular ejection fraction (LVEF) ≥50%, and a Brain natriuretic peptide (BNP) ≥100 pg/ml or N-terminal-pro BNP (NT-proBNP) ≥225 pg/ml. Longitudinal data were analyzed using unsupervised machine learning, including UMAP for dimensionality reduction and Gaussian Mixture Models for clustering. Primary and secondary outcomes included all-cause mortality and changes in BNP levels, respectively. Results: The study included 1,626 patients. The cohort had a median age of 69 years, was 57.5% female, and 51.8% white. Three distinct echocardiographic trajectory patterns emerged (p<.05) among the HFpEF cohort. Trajectory #1 (n=568) exhibited a high-stable pattern with the highest mortality rates, Trajectory #2 (n=512) showed a high-decreasing pattern with high mortality rates, and Trajectory #3 (n=546) had a low-increasing pattern with the lowest mortality rates. Trajectories varied significantly in baseline characteristics and longitudinal echocardiographic changes, with Trajectory #3 having a lower baseline left ventricular mass index (p<.001) and a higher mean arterial pressure ( p =.001) compared to the other trajectories. Kaplan-Meier survival analysis indicated significant differences in survival among the trajectories ( p =.004). Conclusion: The study utilized unsupervised analysis of longitudinal echocardiographic data to identify three distinct HFpEF trajectories, each associated with unique clinical features and outcomes. These findings underscore the heterogeneity of HFpEF and highlight the potential trajectory-based patient stratification to improve targeted intervention strategies. Future validation in larger, multicenter cohorts is warranted to enhance HFpEF management and patient prognosis.

Abstract 4137991: Association of the 2022 AHA/ACC/HFSA Heart Failure Staging and Cardiovascular and Kidney Outcomes in Patients with Diabetes and Kidney Disease: A Secondary Analysis of the SCORED Trial

Introduction: The 2022 AHA/ACC/HFSA heart failure (HF) classification newly incorporates cardiac biomarkers to identify patients at risk of HF. Research Question: Given shared risk factors between HF and other cardiorenal events, the HF stages may also guide prognosis and management of cardiovascular (CV) and kidney-related events beyond HF. Aims: Examine the association of HF stage with CV and kidney events. Methods: SCORED was a randomized trial in diabetes with kidney disease comparing sotagliflozin (sota) v. placebo on CV death, hospitalizations for HF, and urgent HF visits. SCORED participants were grouped by HF stage post hoc . Stage A: No HF, normal cardiac biomarkers (NT-proBNP; <125 pg/mL, hs-cTnT; <14 ng/L) and normal cardiac structure/function by echocardiography. Stage B (pre-HF): No HF but elevated NTproBNP, hsTNT or abnormal echocardiography. Stage C/D: symptomatic HF. Endpoints of interest included the primary composite endpoint (CV death and HF-related events), major adverse CV events (MACE), and kidney-related composites (See Figure caption). Using Cox regression, we examined the association between HF stage and these endpoints, and whether the effect of sota v. placebo varied by HF stage. Results: There were 741 patients in stage A, 6560 in stage B (pre-HF) and 3283 in stage C/D (established HF). The mean NTproBNP and hs-cTnT increased with HF stage. Stepwise increases in HF stage were associated with a 2-4 fold higher incidence of the primary, MACE and kidney endpoints in the placebo group, although the incidence of the kidney-specific composite was similar in stage B and C/D (Figure). The relative risk reduction with sota v. placebo for all endpoints was similar irrespective of HF stage (p-interaction>0.05), with higher absolute benefit in each HF stage (Figure). The absolute benefit for the kidney-specific endpoint was comparable for stage B and C/D. Conclusions: Increasing HF stage is associated with higher risk of HF, MACE, and kidney events, but the risk of kidney events is similar in stage B (pre-HF) and stage C/D (established HF). Sota has similar relative benefits for all endpoints, irrespective of HF stage, with a corresponding increase in absolute benefits at higher stages.

Epidemiology, clinical characterization, and outcomes for heart failure with improved ejection fraction

Abstract Background Contemporary guidelines propose a new classification of heart failure with improved ejection fraction (HFimpEF). The prevalence of HFimpEF is expected to increase with the implementation of guideline-directed medical therapy (GDMT); however, there are limited data on the epidemiology of and outcomes for this novel clinical entity. Purpose We describe the prevalence, GDMT use and outcomes among patients with HFimpEF within a large, integrated healthcare delivery system. Methods We identified adults ≥18 years with incident HF with reduced EF (HFrEF ≤40%) between January 2013 and December 2018 from a large, integrated healthcare system serving >4.5 million members. HFimpEF was defined as known HFrEF with a follow-up EF measurement >40% within 12 months after incident HFrEF. Among HFimpEF patients, we examined GDMT use at discharge after incident HFrEF, incident HFimpEF, and at 6- and 12-months post-HFimpEF. From 12 months post initial HFrEF diagnosis, we calculated incidence rates of subsequent worsening HF (WHF) events (i.e., HF-related hospitalization, emergency department or urgent outpatient visits adjudicated using validated natural language processing-based algorithms) and all-cause death through December 31, 2019. We compared rates between patients with HFimpEF and those with persistent HFrEF (i.e., having all LVEF measurements within 12 months of incident HFrEF ≤40%) using Cox proportional hazards models adjusted for the EF value at incident HFrEF. Results Among 12,639 patients with incident HFrEF, 4,124 (33%) experienced HFimpEF within 12 months. Among patients with HFimpEF, use of β-blockers (82%) and angiotensin-converting enzyme inhibitor/angiotensin II receptor blockers (86%) was high, while angiotensin receptor-neprilysin inhibitor (1.2%) or mineralocorticoid receptor antagonist (28.2%) use was low. β-blockers, ACEi/ARB, and MRA use declined after incident HFimpEF (Figure, Panel A). Rates of WHF were 19.7 (95% Confidence Interval [CI]: 18.7 to 20.6) per 100 person-years among patients with HFimpEF vs. 40.0 (95% CI: 38.5 to 41.5) among patients with persistent HFrEF (hazard ratio [HR]: 0.55, 95% CI: 0.49 to 0.62; Figure, Panel B). Rates of death were 5.2 (95% CI: 4.7 to 5.7) and 8.4 (95% CI: 7.8 to 9.1) per 100 person-years among HFimpEF and persistent HFrEF, respectively (HR: 0.59, 95% CI: 0.52 to 0.67). Among patients with HFimpEF, those with an absolute EF improvement of ≥20% were less likely to experience WHF (HR: 0.82, 95% CI: 0.67 to 0.99) and death (HR: 0.85, 95% CI: 0.69 to 1.04) compared with those with <20% improvement. Conclusions One in three patients experienced improvement in EF within 12 months of initial HFrEF diagnosis. HFimpEF is associated with a reduced risk of WHF events and death, although these patients remain at significant clinical risk. Further research is necessary to assess the potential impact of sustained adherence to GDMT and therapeutic optimization on HFimpEF patient outcomes.

Sinus rhythm restoration as an independent predictor of left ventricular ejection fraction improvement in patients with the first hospitalization for heart failure and reduced ejection fraction

Abstract Background and Purpose Tachycardiomyopathy (TCM) is prevalent in heart failure with reduced ejection fraction (HFrEF). Despite its significance, a comprehensive understanding of its evolutionary trajectory and prognosis remains limited. This study aims to investigate the prognosis and left ventricular ejection fraction (LVEF) trajectory in patients with TCM compared to other etiologies within a cohort initially admitted for "de novo" HFrEF (LVEF < 40%). Methods A prospective registry was conducted across two University Hospitals, incorporating patients hospitalized with a diagnosis of HFrEF. The study encompassed 370 patients admitted between March 2021 and October 2023, with a median follow-up of 15 months (IQR 7-22). Two distinct groups were delineated based on etiology (TCM vs other etiologies). Within the TCM cohort, subgroups were established concerning LVEF improvement, following the criteria outlined in the "Universal Definition and Classification of Heart Failure" report. Categorical variables were presented as absolute frequencies and percentages, analyzed using the chi-square test. Continuous variables were expressed as mean +/- SD and compared through Student's t-test or Mann-Whitney U test. Hazard functions were estimated using the Kaplan-Meier method. Data analysis was executed using SPSS Statistics. Results In our cohort, both TCM and other etiologies groups exhibited improved LVEF post-neurohumoral blockage titration. Nonetheless, the TCM cohort demonstrated notably higher LVEF (49.1% vs. 43.5%, p = 0.005) [Figure 1]. Moreover, overall mortality, cardiovascular mortality, and the composite of CV mortality and hospitalizations for CV causes were notably lower in the TCM group compared to other etiologies [Figure 2]. Among TCM patients, those with improved LVEF during follow-up were younger (66 vs. 72), with no differences in treatment at discharge or after neurohumoral blockage titration. While achieving sinus rhythm before discharge did not correlate with LVEF improvement, attaining sinus rhythm during follow-up (at titration completion) significantly correlated with LVEF improvement [Table 1]. Multivariate analysis revealed that sinus rhythm restoration after up-titration emerged as an independent predictor of LVEF improvement, irrespective of sinus rhythm restoration at discharge or the attainment of quadruple therapy during follow-up, among other variables (Table 2). Conclusions Among patients initially admitted for "de novo" HFrEF, TCM presents a potentially more favorable clinical trajectory compared to other HF causes, showcasing reduced total and cardiovascular mortality, as well as combined cardiovascular mortality and hospitalizations for cardiovascular causes. Notably, in this patient subset, sinus rhythm restoration during follow-up emerged as an independent predictor of improved LVEF, irrespective of pre-discharge sinus rhythm restoration or adherence to comprehensive four-pillar HF management.

Sexual dysfunction and its associated factors among patients with heart failure in public hospitals in Gondar town, North and West Gondar, 2024

IntroductionSexual dysfunction is characterized by difficulties that interfere with achieving satisfying sexual activity, affecting desire, arousal, orgasm, and overall satisfaction. A recent study in Ethiopia identified heart failure as one of the most common cardiovascular diseases in the country. Sexual dysfunction significantly affects the quality of life in individuals with heart failure, yet it often goes underdiagnosed and underreported. Understanding the prevalence of sexual dysfunction and the factors influencing sexuality in these patients is essential. Therefore, the primary objective of this study was to determine the prevalence of sexual dysfunction among patients with heart failure in Gondar City and the northern and western zones of Gondar public hospitals.MethodThis study included 423 participants diagnosed with heart failure, selected through simple random sampling from January 3 to February 30, 2024. Data were collected using an interviewer-administered questionnaire covering socio-demographic, behavioral, and clinical information. Sexual dysfunction in males was assessed using the International Index of Erectile Function-5, while the Female Sexual Function Index-6 was used for females. Data were entered into Epidata version 3.6 and later exported to SPSS version 25 for analysis. Binary logistic regression was employed to identify factors associated with sexual dysfunction, with a significance level set at p ≤ 0.05.ResultA total of 423 heart failure patients participated in the study, achieving a 100% response rate. The results indicated a significant prevalence of sexual dysfunction, affecting 57.92% of participants (95% CI 54.76%–63.76%). Female participants reported a higher prevalence of sexual dysfunction, with 63% of the 138 female heart failure patients affected. Among the 285 male participants, 55.4% (158 patients) were identified as having erectile dysfunction. Multivariable logistic regression analysis revealed that factors such as body mass index, age, insufficient physical activity, and smoking were significantly associated with sexual dysfunction in females. In males, erectile dysfunction was significantly linked to diabetes mellitus, heart failure classification, age, and insufficient physical activity.Conclusion and recommendationThis study identified a high prevalence of sexual dysfunction, with females being more affected than males. Additionally, the research identified several factors influencing sexual dysfunction among patients with heart failure, including BMI, age, cigarette smoking, diabetes mellitus, and the classification of heart failure. The study recommends that healthcare providers and other stakeholders take proactive measures to alleviate the burden of sexual dysfunction in patients with heart failure. Strategies should focus on controlling the severity of heart failure symptoms, effectively managing comorbidities, and addressing factors such as body weight, psychological well-being, and behavioral patterns. By targeting these areas, healthcare providers can work toward minimizing the risk of sexual dysfunction and improving the overall quality of life for patients with heart failure.

Mechanics-based machine learning for failure classification of load-bearing walls

This paper presents novel methodologies that combine analytical mechanics with artificial intelligence to classify the failure mode of reinforced concrete walls under lateral loading. The adequacy of existing approaches is examined, including design equations and established machine learning algorithms, against experimental datasets consisting of 330 specimens. Findings indicate that the machine learning algorithms outperform the empirical design equations in terms of failure mode classification; however, the former lacks physical interpretability. To address this limitation, a new concept is proposed by integrating analytical and computational platforms for identifying attributes that dominate the failure characteristics of the walls. Non-dimensional features are derived and substituted into the machine learning algorithms. The aspect ratio of the walls and the amount of vertical rebars in boundary elements are found to be salient factors when categorizing failure modes, followed by the amount of horizontal rebars in the web. Through the application of decision boundaries stemming from the mechanics-based machine learning protocol, design recommendations are suggested to effectively classify the failure mode of load-bearing walls.

The value of blood flow velocity and pressure gradient in differentiating patients with different types of heart failure.

Patients with different types of heart failure (HF) exhibit varying rates of blood flow through cardiac chambers and pressure gradients across the aortic valve, attributed to differing degrees of myocardial contractility. Assessment of these dynamics offers insights into early HF diagnosis. This study aimed to analyze left ventricular outflow tract (LVOT) blood flow parameters, specifically peak blood flow velocity and pressure gradient derived from four-dimensional flow cardiovascular magnetic resonance (4D flow CMR), and to evaluate 4D flow CMR's utility in distinguishing HF types. This prospective cross-sectional study recruited 115 HF patients from January 2019 to May 2022 at the General Hospital of Ningxia Medical University, classified by the New York Heart Association Cardiac Function Classification of Heart Failure as class II-IV, alongside a control group (n=30). Participants underwent cardiovascular magnetic resonance (CMR), including 4D flow. HF patients were categorized into heart failure with reduced ejection fraction (HFrEF, n=55), heart failure with mildly reduced ejection fraction (HFmrEF, n=30), and heart failure with preserved ejection fraction (HFpEF, n=30), based on ejection fraction. The cardiac functional parameters and aortic valve flow indices were measured using Circle Cardiovascular Imaging. LVOT 4D flow data were obtained 3 mm below the junction of the aortic valve leaflets, assessing peak velocities above and below the valve. Differences in cardiac function and blood flow parameters between groups were analyzed using one-way analysis of variance (ANOVA). The accuracy of these parameters in identifying subgroups was assessed using the receiver operating characteristic (ROC) curve. Analysis of conventional cardiac function parameters revealed that left ventricular ejection fraction (LVEF) was significantly lower in the HFrEF and HFmrEF groups compared to the HFpEF and control groups (P<0.01). Additionally, end-diastolic volume and end-systolic volume were significantly higher in the HFrEF and HFmrEF groups than in the HFpEF and control groups (P<0.01). However, there were no significant differences in cardiac function parameters between the HFpEF and control groups (P>0.05). Significant differences were observed in aortic valve peak pressure gradients (Supra-APGmax) among the four study groups (5.01±1.09 vs. 6.23±2.94 vs. 7.63±1.81 vs. 8.89±2.97 mmHg, P<0.05). Aortic valve peak velocities in the HFrEF group differed significantly from the HFpEF and control groups (111.31±12.05 cm/s vs. 137.2±16 vs. 147.15±24.55 cm/s, P<0.001). The ROC curve for the pressure gradient below the aortic valve had an area under the curve (AUC) of 0.728 [95% confidence interval (CI): 0.591-0.864, P=0.002], with an optimal threshold of 4.72 mmHg (sensitivity: 0.8, specificity: 0.7, Youden index: 0.5). HF patients exhibit reduced pressure gradients across the aortic valve during systole, indicative of altered intracardiac blood flow dynamics. Combining aortic valve velocities and pressure gradients can aid in distinguishing different types of HF, including HFpEF patients.

Failure monitoring and classification from acoustic emission tests on impact-damaged hydrogen composite pressure vessels

Composite overwrapped pressure vessels (COPV) for hydrogen will play an important role in emission free mobility and energy storage. The DELFIN project (2018 – 2022) was set up with the goal of developing improved COPVs that are lighter, cheaper and more durable. A key aspect of this project was to conduct full-scale impact tests with different gas pressure levels that were used to evaluate the vessels’ crash performance and to verify numerical impact simulations. These tests were followed by controlled pressure experiments until the burst pressure was reached. The procedure was monitored with acoustic emission testing (AT) and subsequent computer tomography (CT). The residual COPV stability and the AT results are compared to different scenarios regarding the impact energies and impact angles. The event localization from AT shows a clear correlation of the emitted energy with the damaged areas. We classify the recorded signals into groups that can be related to the failure mechanism and compare those to the pressure evolution before failure of the COPV. All tests indicate that large impact energies lead to a significant reduction of the burst pressure of COPVs, whereas a higher internal pressure can have a stabilizing effect that reduces the damaging effect.

The Management Outcome of Heart Failure Reduced Ejection Fraction with or without Angiotensin Receptor Neprilysin Inhibitor

INTRODUCTION: Heart failure is associated with recurrent admission, higher mortality and low quality of life. Angiotensin receptor neprilysin inhibitor (ARNI) is a novel agent that has been used for treating heart failure reduced ejection fraction (HFrEF) patients. Thus, it is interesting to evaluate the effect of ARNI on the reverse cardiac remodelling, rehospitalization, cardiac biomarker and quality of life in HFrEF patients. MATERIALS AND METHODS: A case controlled study was conducted to assess the treatment outcome of HFrEF with or without ARNI. During the study, the patients’ basic demography, co-morbidities, baseline echocardiography (ECHO) findings, NYHA classification, NT-pro BNP levels and KCCQ score were evaluated. The patients’ admission history within 90 days from initiation of ARNI or non ARNI were obtained retrospectively. A follow up ECHO was obtained after at least 3 months of intervention. RESULTS: A total of 81 patients were recruited in which 54 patients were on ARNI and 27 were on non ARNI treatment. There was a statistically significant improvement of ejection fraction, left ventricular internal diameter end diastole and systole, and left ventricular end-systolic volume in ARNI group. The NYHA class was also noted to improve after ARNI treatment. The NT-proBNP value was lower whereas the KCCQ score was higher in ARNI group compared to non ARNI group. CONCLUSION: HFrEF patients with ARNI treatment had better reverse cardiac remodelling effect, cardiac biomarker and quality of life compared to non ARNI treatment. Furthermore, patient received ARNI demonstrated improved heart failure classification after treatment

Enhancing Defect Detection in Circuit Board Assembly Using AI and Text Analytics for Component Failure Classification

Enhancing Defect Detection in Circuit Board Assembly Using AI and Text Analytics for Component Failure Classification

Advanced AIoT for failure classification of industrial diesel generators based hybrid deep learning CNN-BiLSTM algorithm

Health diagnosis for rotating machines significantly contributes to improving safety and reliability. This research studies an AIOT system for anomaly prognosis for diesel generators utilizing deep learning algorithms, the convolution neural network, and bidirectional long short-term memory (CNN-BiLSTM) algorithms. Firstly, the IoT systems are designed to collect the simulated anomaly condition data of industrial 125 kW/250 kW diesel generators in the laboratory, which are utilized as training label data. The CNN combined BiLSTM captures the mapping correlation between selected features through the training operations to prognosis the anomaly conditions of the industrial diesel generator. The superiority of the developed algorithm is verified with other deep learning approaches, including the recurrent neural network (RNN), the gate recurrent unit (GRU), the LSTM, and the CNN. An experiment verification is deployed to evaluate the effectiveness of the hybrid methodology, which proves the excellent ability and demonstrates the superiority in anomaly condition classification compared with other individual deep learning models. Through different experiment datasets, the proposed methodology’s improved performances demonstrated its contribution to a novel framework beyond other advanced algorithms. The application of the developed AIot-based CNN-BiLSTM algorithm has been successfully deployed for anomaly conditional classification in the industrial 125 kW/250 kW diesel generators in Taiwan.

Implementation of machine learning methods in predicting failures in electrical submersible pump machines

The Electric Submersible Pump (ESP) is one of the artificial lift tools widely used in oil and gas wells. Approximately 25% of oil and gas wells have been equipped with ESP. The ESP unit excels in lifting fluid rates much larger than most other types of artificial lift. The purpose of this research is to develop a machine learning model that can predict the likelihood of ESP installation failure. By making predictions at the early stage, necessary actions can be prepared to prevent and anticipate these issues. Thus, the reduction in the amount of produced fluid can be minimized. The model in this study uses 8 input parameters, namely, motor Ampere, Frequency, Pump Intake Pressure, Temperature Motor, Output Volt, Pump Discharge Pressure, Input Voltage, and Motor Horse Power (HP). There are also 8 failure classifications for which the model will be created. The initial stage involves collecting and cleaning raw data and processing it to detect anomalies. Once the data is processed, it proceeds to the model formation and model validation stage. The method used for the model is based on the comparison of two methods, namely, decision tree and k-nearest neighbor. The output of the research is a dashboard that can display visualizations of data and the prediction results from the created model. Millions of data points are used to create a database consisting of 77 wells over the past 2 years. The model developed generates several types of failures that can be read. These failures have their own characteristic parameters. The model obtained has an accuracy rate above 90%. The output of this model includes the most likely failure prediction based on the latest input data, thus effectively addressing well problems, warning of impending failures, reducing failures, and assisting in scheduling ESP repairs and maintenance.

A Multidisciplinary Approach to the Classification and Management of Intestinal Failure: Knowledge in Progress.

Intestinal failure (IF) is a debilitating condition characterized by the insufficient function of the gastrointestinal tract to absorb nutrients and fluids essential for life. This review consolidates recent advancements and challenges in managing IF among adult and pediatric populations, highlighting differences in etiology, management, and outcomes. Over the recent years, significant strides have been made in the nutritional and medical management of IF, significantly reducing mortality rates and improving the quality of life for patients. Key advancements include the development and availability of glucagon-like peptide-2 (GLP-2) analogs, improved formulations of parenteral nutrition, and the establishment of specialized interdisciplinary centers. Short bowel syndrome (SBS) remains the predominant cause of IF globally. The pediatric segment is increasingly surviving into adulthood, presenting unique long-term management challenges that differ from adult-onset IF. These include the need for tailored nutritional support, management of IF-associated liver disease, and addressing growth and neurodevelopmental outcomes. The therapeutic landscape for IF continues to evolve with the development of new treatment modalities and better understanding of the condition's pathophysiology. However, disparities in treatment outcomes between children and adults suggest the need for age-specific management strategies. This review underscores the importance of a nuanced approach to IF, incorporating advancements in medical science with a deep understanding of the distinct needs.

Explainable ensemble learning graphical user interface for predicting rebar bond strength and failure mode in recycled coarse aggregate concrete

Novel study deploys robust machine learning algorithms using newly built comprehensive dataset to predict reinforcing rebar-to-recycled coarse aggregate concrete (RCA) bond strength and failure mode. Prior investigations have solely concentrated on bond strength, resulting in a limited comprehension of the bond failure pattern. Considering the increasing significance of sustainable construction methods, it is crucial to examine both the failure pattern and bond strength to expand the versatility of RCA in various reinforced concrete structures. Accordingly, XGBoost, CatBoost, Random Forest, and LightGBM were trained for this purpose. Model performance was appraised using various statistical metrics, while failure classification performance was assessed using accuracy, recall, and precision indicators. Model performance was ranked using Copeland's algorithm. Feature importance was quantified using SHAP. Coefficient of determination of 0.91 was achieved by XGBoost in predicting bond strength, outperforming other nine analytical models in literature. Failure mode was predicted with accuracy of 94% by CatBoost, XGBoost, and LightGBM. Embedment length and compressive strength features had greatest influence on bond strength and failure mode, respectively. User-friendly graphical interface was developed to harvest ML models in real-world engineering practice. Online free access accurately assigns to any given combination of input features corresponding accurate rebar bond strength and failure mode.

Harnessing machine learning technique for improved detection and classification of heart failure

Artificial Intelligence (AI) performs exercises recently performed by people utilizing AI and profound learning, Right now simulated intelligence is changing cardiovascular medication identifying problems, therapeutics, risk appraisals, clinical consideration, and medication advancement. The death rates in medical clinics for patients with cardiovascular breakdown display a scope of 10.6% at 30 days, 23.0% at 1 year, and 43.3% at 5 years. Cardiovascular breakdown (HF) patients need customized restorative and careful treatment, in this way early finding is pivotal. The 85% precise Brain Organization (NN) archetypal made this conceivable. By applying our calculation, simulated intelligence can assist with examining crude cardiovascular imaging information from echocardiography, processed tomography, and heart attractive reverberation imaging and EKG accounts. Unpleasant Sets (RS) and strategic relapse (LR) choice trees to analyze congestive cardiovascular breakdown and computerized reasoning to identify future impermanence and destabilization incidents have further developed cardiac illness results. This examination inspects how computer- based intelligence has changed pretty much every area of HF determination, avoidance, and the executives

Abstract 20: Orthostatic Hypotension in Patients with Heart Failure taking Carvedilol or Metoprolol

Background: Metoprolol succinate and carvedilol are widely prescribed beta-blockers for the treatment of heart failure (HF). Both can reduce mortality, but unlike the β-1 selective metoprolol, carvedilol has α-1 blocking effects. Since orthostatic hypotension (OH) is a common comorbidity in HF patients, we conducted a case-control study to assess if the vasodilatory effect of carvedilol, compared to metoprolol, is associated with a higher risk for newly diagnosed OH. Methods: A deidentified retrospective database of all HF patients seen at the Vanderbilt University Medical Center who were taking metoprolol or carvedilol (n=272,019) was used. Cases of newly diagnosed OH were defined as those who had a new diagnosis of OH after the first mention of either drug (n=3,006) and controls as those who did not have a diagnosis of OH (n=269,013). Demographic information, presence of other comorbidities, and concomitant use of other drugs known to cause hypotension were obtained. One-way analysis of variance (ANOVA) and Pearson’s chi-square tests were used to compare continuous and categorical variables between both groups, respectively. Results: OH was diagnosed in 1% of all patients and metoprolol was more commonly prescribed than carvedilol (81% vs. 19%). Compared to controls, OH patients were older (58 vs. 66 yr) and had a higher rate of hypertension (22% vs. 96%), Parkinson disease (1% vs. 3%) and concomitant use of diuretics, calcium channel blockers, other α-1 blockers and other drugs associated with OH (Table). The prevalence of OH cases was higher in the carvedilol group compared to the metoprolol group (1.96% vs. 0.91%), but only age differed between the OH cases in both groups (metoprolol: 64 vs. carvedilol: 71 yr). We found that patients were more likely to be diagnosed with OH after taking carvedilol vs. metoprolol (unadjusted odds ratio 2.2078, 95% CI 2.0448 – 2.3838, p &lt; 0.001). Conclusions: The prevalence of OH among patients with heart failure was higher in patients taking carvedilol compared to metoprolol. Given that both drugs have similar beneficial effects on mortality, metoprolol appears to be preferable in the treatment of heart failure in patients at risk of developing OH. Studies to further assess the relationship between heart failure classification and orthostatic hypotension are needed.

Biomarkers of lung congestion and injury in acute heart failure.

Acute heart failure (AHF) classification and management are primarily based on lung congestion and/or hypoperfusion. The quantification of the vascular and tissue lung damage is not standard practice though biomarkers of lung injury may play a relevant role in this context. Haemodynamic stress promotes alveolar and vascular derangement with loss of functional units, impaired lung capillary permeability and fluid swelling. This culminates in a remodelling process with activation of inflammatory and cytokines pathways. Four families of lung surfactant proteins (i.e., SP-A, SP-B, SP-C, and SP-D), essential for the membrane biology and integrity are released by alveolar type II pneumocites. With deregulation of fluid handling and gas exchange pathways, SPs become sensitive markers of lung injury. We report the pathobiology of lung damage; the pathophysiological and clinical implications of alveolar SPs along with the newest evidence for some classical HF biomarkers that have also shown to reflect a vascular and/or a tissue lung-related activity.

Assessment of UHF Frequency Range for Failure Classification in Power Transformers.

Ultrahigh-frequency (UHF) sensing is one of the most promising techniques for assessing the quality of power transformer insulation systems due to its capability to identify failures like partial discharges (PDs) by detecting the emitted UHF signals. However, there are still uncertainties regarding the frequency range that should be evaluated in measurements. For example, most publications have stated that UHF emissions range up to 3 GHz. However, a Cigré brochure revealed that the optimal spectrum is between 100 MHz and 1 GHz, and more recently, a study indicated that the optimal frequency range is between 400 MHz and 900 MHz. Since different faults require different maintenance actions, both science and industry have been developing systems that allow for failure-type identification. Hence, it is important to note that bandwidth reduction may impair classification systems, especially those that are frequency-based. This article combines three operational conditions of a power transformer (healthy state, electric arc failure, and partial discharges on bushing) with three different self-organized maps to carry out failure classification: the chromatic technique (CT), principal component analysis (PCA), and the shape analysis clustering technique (SACT). For each case, the frequency content of UHF signals was selected at three frequency bands: the full spectrum, Cigré brochure range, and between 400 MHz and 900 MHz. Therefore, the contributions of this work are to assess how spectrum band limitation may alter failure classification and to evaluate the effectiveness of signal processing methodologies based on the frequency content of UHF signals. Additionally, an advantage of this work is that it does not rely on training as is the case for some machine learning-based methods. The results indicate that the reduced frequency range was not a limiting factor for classifying the state of the operation condition of the power transformer. Therefore, there is the possibility of using lower frequency ranges, such as from 400 MHz to 900 MHz, contributing to the development of less costly data acquisition systems. Additionally, PCA was found to be the most promising technique despite the reduction in frequency band information.

Eligibility Criteria for Different Platinum-Based Chemotherapy Regimens in Metastatic Urothelial Carcinoma.

Introduction Bladder cancer is one of the most prevalent cancers worldwide, with significant morbidity and mortality rates. Treatment options for metastatic urothelial carcinoma (mUC) primarily include platinum-based chemotherapy. Cisplatin-based chemotherapy is conventionally used for treating mUC, but many patients are ineligible due to various factors such as poor performance status, creatinine clearance, neuropathy, and cardiac function. Carboplatin-based therapy is another alternative, which typically yields less favorable outcomes. Some centers use split-dose cisplatin for treating patients with comorbidities and impaired renal function, broadening cisplatin's spectrum. While eligibility criteria for full-dose cisplatin are well-established, those for split-dose cisplatin and carboplatin lack strong evidence. This study aims to assess the recommended criteria for full-dose cisplatin, split-dose cisplatin, and carboplatin regimens in real-world settings, including hematological parameters for patients with mUC. Methods A cross-sectional web-based survey was conducted among 136 oncologists from 21 countries, assessing criteria such as creatinine clearance, Eastern Cooperative Oncology Group (ECOG) performance status (PS), neurotoxicity, hearing loss, heart failure classification, and hematological parameters. Results The survey revealed diverse preferences among 113 oncologists treating mUC, regarding the eligibility criteria for each chemotherapy regimen with 81% prioritizing full-dose cisplatin, 21% split-dose cisplatin, and 14% carboplatin regimens. Criteria for all three regimens included specific thresholds. For full-dose cisplatin, the preferred criteria included creatinine clearance ≥60 mL/min, ECOG PS ≤1, grade 1 neuropathy, grade 1 deafness, New York Heart Association (NYHA) heart failure ≤class II with ≥50% cardiac ejection fraction, and normal blood parameters. Split-dose cisplatin criteria were creatinine clearance ≥40 mL/min, ECOG PS ≤2, grade 1 neuropathy, grade 1 deafness, NYHA heart failure ≤class II with ≥50% cardiac ejection fraction, and normal blood parameters. Carboplatin eligibility criteria were creatinine clearance ≥30, ECOG PS ≤2, grade ≤2 neuropathy, grade ≤2 deafness, NYHA heart failure ≤class II with ≥50% cardiac ejection fraction, and normal blood parameters. Hematological parameters were deemed crucial for all regimens, particularly stringent for carboplatin-based chemotherapy. Conclusion The study underscores the importance of renal function and hematological parameters in determining chemotherapy eligibility for patients with mUC. It highlights the importance of precise treatment criteria in mUC management, with hematological factors playing a significant role. Standardized criteria and further research are warranted to optimize treatment outcomes and minimize adverse events associated with chemotherapy regimens. Understanding the preferences of oncologists globally can facilitate tailored treatment approaches and improve patient care in the management of mUC.