Failure Conditions Research Articles

AGE-RAGE Axis and Cardiovascular Diseases: Pathophysiologic Mechanisms and Prospects for Clinical Applications.

Advanced glycation end products (AGE), a diverse array of molecules generated through non-enzymatic glycosylation, in conjunction with the receptor of advanced glycation end products (RAGE), play a crucial role in the pathogenesis of diabetes and its associated complications. Recent studies have revealed that the AGE-RAGE axis potentially accelerated the progression of cardiovascular diseases, including heart failure, atherosclerosis, myocarditis, pulmonary hypertension, hypertension, arrhythmia, and other related conditions. The AGE-RAGE axis is intricately involved in the initiation and progression of cardiovascular diseases, independently of its engagement in diabetes. The mechanisms include oxidative stress, inflammation, alterations in autophagy flux, and mitochondrial dysfunction. Conversely, inhibition of AGE production, disruption of the binding between RAGE and its ligands, or silencing of RAGE expression could effectively impair the function of AGE-RAGE axis, thereby delaying or ameliorating the aforementioned diseases. AGE and the soluble receptor for advanced glycation end products (sRAGE) have the potential to be novel predictors of cardiovascular diseases. In this review, we provide an in-depth overview towards the biosynthetic pathway of AGE and elucidate the pathophysiological implications in various cardiovascular diseases. Furthermore, we delve into the profound role of RAGE in cardiovascular diseases, offering novel insights for further exploration of the AGE-RAGE axis and potential strategies for the prevention and management of cardiovascular disorders.

Two-Stage Locating and Capacity Optimization Model for the Ultra-High-Voltage DC Receiving End Considering Carbon Emission Trading and Renewable Energy Time-Series Output Reconstruction

With the load center’s continuous expansion and development of the AC power grid’s scale and construction, the recipient grid under the multi-feed DC environment is facing severe challenges of DC commutation failure and bipolar blocking due to the high strength of AC-DC coupling and the low level of system inertia, which brings many complexities and uncertainties to economic scheduling. In addition, the large-scale grid integration of wind power, photovoltaic, and other intermittent energy sources makes the ultra-high-voltage (UHV) DC channel operation state randomized. The deterministic scenario-based timing power simulation is no longer suitable for the current complex and changeable grid operation state. In this paper, we first start with the description and analysis of the uncertainty in renewable energy (RE) sources, such as wind and solar, and reconstruct the time-sequence power model by using the stochastic differential equation model. Then, a carbon emission trading cost (CET) model is constructed based on the CET mechanism, and the two-stage locating and capacity optimization model for the UHV DC receiving end is proposed under the constraint of dispatch safety and stability. Among them, the first stage starts with the objective of maximizing the carrying capacity of the UHV DC receiving end grid; the second stage checks its dynamic safety under the basic and fault modes according to the results of the first stage and corrects the drop point and capacity of the UHV DC line with the objective of achieving safe and stable UHV DC operation at the lowest economic investment. In addition, the two-stage model innovatively proposes UHV DC relative inertia constraints, peak adjustment margin constraints, transient voltage support constraints under commutation failure conditions, and frequency support constraints under a DC blocking state. In addition, to address the problem that the probabilistic constraints of the scheduling model are difficult to solve, the discrete step-size transformation and convolution sequence operation methods are proposed to transform the chance-constrained planning into mixed-integer linear planning for solving. Finally, the proposed model is validated with a UHV DC channel in 2023, and the results confirm the feasibility and effectiveness of the model.

Non-singular sliding mode control-based attitude control analysis of ELITE satellite with flywheel failures

Abstract This paper addresses the challenge of maintaining the attitude control of the ELITE satellite in the event of flywheel failures. Given the critical need for highly stable and precise attitude control during Time-Delay Integration (TDI) Imaging missions, a failure in any flywheels poses a significant threat to the satellite’s attitude control stability and accuracy. To tackle this issue, a Non-singular Sliding Mode Control (NSMC) method was proposed to ensure ELITE’s continued performance under flywheel failure conditions. The research involves a thorough analysis of ELITE’s attitude dynamics, followed by designing an NSMC tailored to its specifications. Theoretical validation of the NSMC’s finite-time stability was achieved using the Lyapunov function. Numerical simulations conducted demonstrate that the proposed NSMC method enables ELITE to maintain its attitude control stability and precision even in the presence of flywheel failures, thereby ensuring mission continuity and reliability.

Rancang Bangun Miniatur Plant Pusat Simulator Pompa Air Kotor Menggunakan Panel Kontrol Relay Kontaktor

Water pumps play an important role in the management of wastewater, particularly in maintaining environmental cleanliness and health. This research aims to design and create a miniature plant simulator for wastewater pumps using a relay contactor control panel. This miniature plant is designed to simulate the operation of the wastewater pump system automatically, replacing the often inefficient manual methods. The research process begins by determining the main components needed, such as a three-phase induction motor, contactors, thermal overload relays, and related sensors. Subsequently, the design and construction of the simulator and its control panel are carried out. Implementation includes testing the functionality of each component and the integrity of the overall system, including individual component testing and testing under system failure conditions. The research results show that the miniature plant simulator built functions well as expected. The automatic control system using relay contactors has proven effective in operating the water pump efficiently and safely. This miniature plant can be used not only as a learning tool but also as a prototype for developing larger and more complex wastewater pump control systems

Effect of disabling biventricular pacing in patients with left ventricular assist device and cardiac resynchronization therapy

Abstract Introduction Taking into account the mechanism of the left ventricular assist device (LVAD) function, the cardiac resynchronization therapy (CRT) seems to be unnecessary and, according to the current reports, the left ventricular lead (LVL) should be disabled. However, observation of the patients suggests that deactivation of the left ventricle (LV) pacing leads to deterioration of the patients condition and progression of the heart failure (HF) symptoms. Purpose The study aimed to determine differences in survival of the patients with LVAD and maintained or disabled CRT, including patients subjected to heart transplantation, as well differences in left and right ventricle function, kidneys and liver condition, NTproBNP value, subjective exercise tolerance and frequency of HF exacerbation before and after CRT deactivation. Methods The study group involved 55 patients with LVAD and CRT devices, including 11 patients with disabled LV pacing. The statistical analysis concerned the comparison of groups with and without biventricular stimulation as well results within the group with disabled LVL before and after its deactivation. Analyzed data included laboratory results, echocardiographic parameters, number of HF exacerbation episodes and exercise tolerance by NYHA classification. Results Total CRT period did not differ in group with maintained and disabled CRT [24 (12-36)months vs. 30 IQR(12-48)months respectively; p=0,74]. The median period of CRT deactivation was 30 IQR(12-48) months after LVAD implantation. Time from LVAD implantation to heart transplantation differed significantly between patients with and without CRT [20 IQR(9-32)months vs. 49,5 IQR(46,5-62)months respectively; p=0,003]. LV disabling was connected with a significant increase of NTproBNP [3072 IQR(2287-6636) pg/ml vs. 2133 IQR(1618-3437) pg/ml before LVL deactivation; p=0,009), without an aggravation of the exercise intolerance by NYHA classification (p=0,11) or total number of HF exacerbation episodes (p=0,72). Lack of LV pacing was not related to change of concentration of creatinine (p=0,13), AST (p=0,89, ALT (p=0,5), bilirubin (p=0,06) and albumin (p=0,69) before and after LV deactivation. Comparison of echocardiographic parameters with and without CRT did not demonstrate significant differences in LV ejection fraction (p=0,37), LV end-diastolic diameter (p=0,33), TAPSE (p=0,72) and tricuspid regurgitation degree (p=0,39). Death-free survival from LVAD implantation has not differed in patients with and without CRT(p=0,08), similarly like death-free survival from heart transplantation (p=0,56), however with tendency to better outcome of patients with CRT till 12 months after the heart transplant. Conclusion CRT does not improve general health condition as well death-free survival in LVAD recipients, including patients subjected to heart transplantation, however with tendency to better outcome of patients with CRT till 12 months after the heart transplant.Death-free surival after LVADDeath-free survival after transplant

Signature of diabetic cardiomyopathy progression using high-fat diet and streptozotocin mouse model: from subclinical to clinical with three-hit mouse model

Abstract Background Diabetic cardiomyopathy (DbCM) is a pre-heart failure (pre-HF) condition that usually exhibits early diastolic dysfunction and evolving systolic dysfunction, along with left ventricular (LV) remodelling. Although DbCM shows distinct pathophysiology, the transition from the subclinical to the symptomatic stage is not straightforward and usually involves multifaceted processes which are not fully understood. Purpose To define the signature of DbCM progression in a novel mouse model using longitudinal echocardiography and multi-omics approaches. Methods Male mice (C57BL/6) were fed with high-fat diet (HFD) or control diet (CD) for 24 weeks, with single dose of streptozotocin (STZ) or vehicle injection at 8 weeks (n=10 each). Serial functional and metabolic parameters were measured prior to endpoint single nuclei RNA sequencing (snRNA-seq) and mass spectrometry-based proteomics analysis with comparison to cardiac remodelling to define detailed mechanisms underlying DbCM progression. Results HFD/STZ mice developed type 2 diabetes mellitus (T2DM) based on higher fasting blood glucose and HbA1C levels, insulin resistance and decreased beta-cell function (HOMA-IR and HOMA-beta, respectively) compared to controls. Progressive diastolic dysfunction was evident in HFD/STZ mice compared to CD mice based on enlarged LVPW;d from 12 weeks (P<0.05), reduced MV E/A ratio (P<0.01) and IVRT (P<0.05) from 16 weeks, and increased cardiac filling pressure (Left atrial (LA) area and LA volume) at 24 weeks, with preserved systolic function. This was paralleled by elevated collagen deposition (Picrosirius red), myocyte cross-sectional area and Col1a1 expression in HFD/STZ versus CD mice (all P<0.05). snRNA-seq analysis indicated preferential monocyte migration into diabetic hearts (P<0.01) whilst inflammatory pathways were activated across all cardiac cell types. Proteomics analysis revealed elevated expression of the established inflammation marker, plasma C-reactive protein (CRP), in HFD/STZ versus CD mice (P<0.05), which was correlated with worsening diastolic function (MV E/A ratio: r=-0.839, P<0.01) and LV hypertrophy (LVPW;d: r=0.861, P<0.01). Conclusion Our HFD/STZ mouse model offers a ‘three-hit’ approach which effectively captures key stages of clinical DbCM progression: 1) T2DM with insulin resistance, 2) progressive diastolic dysfunction with elevated chronic filling pressure and cardiac remodelling, and 3) systemic inflammation. This improved HFD/STZ mouse model is therefore appropriate to support a detailed study of mechanisms underlying DbCM progression and subsequent translation.

Finerenone: A Third-Generation MRA and Its Impact on Cardiovascular Health-Insights from Randomized Controlled Trials.

Introduction: Finerenone, a third-generation non-steroidal mineralocorticoid receptor antagonist (MRA), offers a targeted approach to managing cardiovascular outcomes, particularly in patients with chronic kidney disease (CKD) and type 2 diabetes (T2D). Unlike traditional MRAs such as spironolactone and eplerenone, which can cause off-target hormonal side effects and hyperkalemia, Finerenone selectively binds to mineralocorticoid receptors, reducing these risks. Recent randomized controlled trials have demonstrated Finerenone's potential to improve cardiovascular outcomes, making it a promising alternative in the management of heart failure and other cardiovascular conditions associated with CKD and T2D. Methods: We conducted a scoping review using PRISMA guidelines. A search for "Finerenone" in the PubMed, Embase, and Cochrane Library databases included randomized controlled trials (RCTs), post hoc analyses, and relevant meta-analyses on cardiovascular outcomes. Data were synthesized narratively, assessing study quality through strengths and limitations. Discussion: Finerenone has shown significant benefits and a superior safety profile compared with traditional MRAs like spironolactone and eplerenone in managing CKD, T2D, and heart failure. It effectively reduces cardiovascular and renal events while minimizing risks such as hyperkalemia and hormonal side effects associated with steroidal MRAs. Future studies, including the REDEFINE-HF, FINALITY-HF, and CONFIRMATION-HF trials, will further explore Finerenone's potential across diverse heart failure phenotypes, including its role in heart failure with mildly reduced and preserved ejection fractions, potentially establishing it as a cornerstone therapy in heart failure management. Conclusions: Finerenone represents a significant advancement in MRA therapy, offering enhanced safety and efficacy in managing cardiovascular outcomes in CKD and T2D patients. The current evidence supports its use as a promising alternative to traditional MRAs, particularly in patients intolerant to steroidal MRAs. Further trials are needed to fully establish its potential across diverse patient populations, including those with varying heart failure phenotypes.

Education Empowerment : Transformation of Parenting Style with Incidence of Stunting. A Cross Sectional Analysis in One Island, Indonesia

Childhood Stunting is a condition of failure to thrive in children under five as a result of chronic malnutrition so that children become too short for their age. Parenting is an important component that should not be disregarded in the pursuit of better nutritional status, since it has an indirect impact on nutritional.This study aims to determine the relationship betweenfamily parenting and the incidence of stunting in Simeulue Island Aceh, Indonesia. This study applied a cross-sectional design involving 86 people who were taken by proportional sampling. Chi-square test showed a relationship between permissive parenting and experienced stunting (p-value, 0.001), a relationship between authoritarian parenting and experienced stunting (p-value, 0.000), and a relationship betweendemocratic parenting with experienced stunting (p-value, 0.002). Therefore,it is expected that respondents, namely families of toddlers, will provide good parenting to toddlers so that they will increase the growth and development of toddlers to prevent stunting.

Identification of non-circular profiles in hydrodynamic journal bearings

Hydrodynamic bearings, especially cylindrical radial plain journal bearings, are widely utilized in industry for their high load capacity and low friction energy losses. However, these bearings are prone to faults such as wear and ovalization, which can deform their circular profile and affect their vibrational response. Detecting these faults is essential to reduce their impact on production. This study introduces a methodology to identify hydrodynamic bearings with non-circular profiles. The bearing model and its numerical solution are implemented using the Finite Volume Method, with the effects of failures incorporated into a rotating system modeled by the Finite Element Method. A dataset is generated to reflect three common failure conditions in industrial applications: wear, ovalization, and a combination of ovalization with wear. The authors used this dataset to train a Multilayer Perceptron (MLP) neural network, which can identify the bearing profile shape based on specific attributes of the dynamic responses. The identification tests for the three fault conditions demonstrated high accuracy, particularly in distinguishing between ovalization and wear.

Grey Bayesian network model for electromagnetic pulse vulnerability assessment of engine system

Firstly, in view of the uncertainty of electromagnetic pulse vulnerability assessment of engine system caused by limited experimental data and incomplete information, a grey Bayesian network model is proposed by introducing interval grey numbers in grey system theory to characterize the uncertainty of sensitivity threshold and fault logic relationship of engine system components, so as to improve the processing ability of Bayesian network for uncertain information. Secondly, taking broadband high-power microwave as an example, the interval grey number failure probability of engine system and the posterior failure probability of sensor interval grey number are calculated. The former represents the survivability of the engine system as a whole under strong electromagnetic pulse, and the latter reflects the vulnerability order of each sensor under the condition of engine system failure. The evaluation conclusion can provide a reference for the design of vehicle electromagnetic protection.

Evaluation of the Antecedent Saturation and Rainfall Conditions on the Slope Failure Mechanism Triggered by Rainfalls

The stability analysis of rainfall-induced slope failures considers a number of factors including the characteristics of the rainfall, vegetation, geometry of the slope, unsaturated soil characteristics, infiltration capacity, and saturation degree variations. Amongst all these factors, this study aims to investigate the effects of the antecedent rainfall and saturation conditions. A numerical modeling study was conducted using finite difference code software on a representative slope geometry with two different soil types. Two scenarios were followed: The first involved the application of three different rainfall intensities for varying initial saturation levels between 40% and 60%, representing the antecedent saturation conditions. The second scenario involved modeling successive rainfalls for a typical initial saturation degree of 50%. The impact of antecedent rainfall was assessed by determining the time required for failure during the application of a main extreme rainfall after a preceding rainfall of varying durations. Consequently, a zone of susceptible time for failure was suggested for use as a criterion in hazard management, allowing for the tracking of rainfall and its duration through the proposed chart for potential failures. Once the anticipated critical rainfall intensities have been determined through a meteorological analysis, a risk assessment for a specific slope can be conducted using the proposed practical procedure. Accordingly, a control mechanism may be established to detect the potential for a natural hazard. Furthermore, the proposed procedure was applied to a case study, whose modeling insights were in harmony with the real conditions of the slope failure. Thus, this demonstrated the significance of the antecedent conditions in modeling landslides triggered by rainfalls.

Fracture Criteria for Matrix and Fibers in Unidirectional Polymeric Composites at Static Loadings

When analyzing the strength of structures made of layered fibrous polymer composite materials; the criteria for failure of a monolayer – a unidirectional reinforced composite – are used. A criterion of strength according to the conditions of matrix fracture is formulated, corresponding to conical limiting surfaces and the lowest destructive loads. The criterion of strength according to the condition of fiber failure, which does not allow the paradox of increasing strength in the region of transition from fiber destruction to matrix failure, is given. Experimental verification of the criteria for volumetric, planar, and one-dimensional loads is carried out. Their better correspondence to empirical data is shown and their advantages in comparison with known criteria are marked. A small number of easily detectable parameters of these criteria contribute to their reliability and stability in strength calculations of composite structural elements.

Opportunities for digitally-enabled personalization and decision support for pediatric growth hormone therapy.

Smart technologies and connected health are providing opportunities for improved healthcare for chronic conditions. Acceptance by healthcare professionals (HCPs) and patients is crucial for successful implementation. Evidence-based standards, technological infrastructure and regulatory processes are needed to integrate digital tools into clinical practice. Personal health records provide continuity and aid decision-making, while machine-learning algorithms may help in optimizing therapies and improving outcomes. Digital healthcare can negate geographical barriers, enabling patients in remote areas to access specialist endocrine expertise. We review available and developing digital tools to manage care for patients requiring growth hormone (GH) therapy for growth failure conditions. GH is most often administered via daily injections over several years; continuous adherence is necessary but may become insufficient. Future development and integration of electronic platforms for GH therapy requires involvement of all stakeholders in design-thinking approaches and human-factor testing. Growzen Connect is an innovative digital ecosystem designed to increase the management and monitoring of GH therapy, comprising the easypod device and connected mobile apps. It provides a real-time overview of a patient's therapy, including adherence and growth response, which aids decision-making by HCPs and empowers patients to engage in their therapy journey. Incorporating prediction models for adherence and growth in the ecosystem helps patients build treatment habits and allows issues to be addressed in a timely fashion. A connected ecosystem for GH therapy can enhance outcomes and empower patients, fostering a collaborative and patient-centered approach that is more proactive, beyond the traditional clinic-based approach.

Real collapse responses of an RC flat plate structure under extreme overloading condition: Reversing load redistribution

Progressive collapse research of concrete flat plate structures, typically investigated through the alternate path (AP) method with a focus on column failure (removal), has often neglected the facts of real-life collapse events which are caused by slab overloading, leading to initial punching shear failure at slab-column joints. Progressive collapse of the structural systems triggered by slab-column joint failures exhibits distinct failure modes and resistance mechanisms from those triggered by column failures. Building upon our initial tests on the overloaded slab specimen under quasi-static loading regimes, this paper presents an innovative experimental study that closely mirrors the load-resistant mechanisms of the most common collapse incidents in flat plate structure systems in the real world. By successively stacking up gravity loads without additional interventions, the test accurately simulates the spontaneous failure conditions typical of flat plate structures. A key discovery of this research is the identification of a unique reversing load redistribution mechanism, a phenomenon not previously observed in our slab overloading tests or in studies focused on column removal scenarios. This discovery highlights the necessity of enhancing the post-punching capacity at the slab-column joints as a critical factor in preventing collapses due to slab overloading. In addition, this study provides an in-depth examination of the dynamic effects inherent in the progressive collapse of flat plate structures. Evaluating the dynamic increase factor and load amplification factor offers supplementary data pertinent to real-world collapses, providing essential insights for improving the resilience of flat plate structures against initial joint failure due to slab overloading.

Three-dimensional convolutional neural network for leak detection and localization in smart water distribution systems

Smart meters such as advanced metering infrastructure (AMI) can significantly improve identifying realistic sized leaks in water distribution networks (WDNs). However, to date, detection/localization methods for AMI systems are extremely limited. In this study, to examine the benefits of using AMIs for leak detection within distribution network, a three-dimensional (3D) convolutional neural network (CNN) deep learning (DL) model is proposed that can account for temporally and spatially distributed information of pressures. The 3D CNN is tested for a real WDN in Austin using the realistic sized leaks (e.g., 3 L/s for 150-mm pipes) that are generated from hydraulic simulations. The model's performance is evaluated using detection probability, false alarm rate, and localization pipe distance metrics. In addition, the strength of using DL for leak identification is examined by comparing the CNN results with those from an optimization-based model. The 3D CNN performed better than the optimization model indicating that DL has advantages over conventional tools such as optimization methods. However, its adaptability may limit its use in some cases. Since DL can be significantly impacted by hydraulic simulation model, a way to handle modelling error must be determined. In addition, as network changes occur, retraining is required that may be time consuming and have difficulty with the number of failure conditions.

Shear Band Formation with Split Hopkinson Bar Experiments

The essence of dynamic failure is closely linked to dramatic shear deformations which often lead to the formation of adiabatic shear bands (ASB). Under high loading velocities and the subsequent rapid temperature increase, the localization of shear strain is crucial in view of safety issues of systems in mechanical and aircraft engineering, especially with respect to fast rotating components and diverse crash scenarios. In this research, we perform high speed impact tests at the split Hopkinson pressure bar (SHPB) setup and use particular hat-shaped specimen geometries that resemble the stresses and failure conditions at the component level.In the first step, we specify a notched specimen geometry using finite element (FE) simulations to ensure pure shear. Further, quasi-static compressive tests and a series of impact tests at high strain rates of 103−104s−1 are conducted on specimens manufactured from a fine-grain structural steel with the properties of S355. Optical microscopy and electron backscatter diffraction (EBSD) of the sheared zones unveil significant localization to maximal shear strains of about 0.9 accompanied by grain refinement by factors 5 to 14. The displacements across the surface of the specimens are captured with subset-based local digital image correlation (DIC) during the impact time, and serve as an objective to validate a viscoplastic constitutive relationship. More precisely, the deformation distribution is accurately reproduced by the widely recognized Johnson-Cook (JC) model, which features an enhanced description of damage evolution. Thus, combining experimental and characterization techniques, continuum mechanics and reasonable optimization strategies for the identification of model parameters provides an efficient approach for comprehensive insights into the strain localization behaviour and its impact on the mechanical performance of S355 under extreme strain rates and deformations.

Web-Based Maintenance Prediction of Machine Conditions and Failure Modes Using Machine Learning

Machine failure may result in unplanned downtime, production losses, safety risks, and increased costs. Hence, predicting machine failures is a significant challenge faced by many industries. Predictive Maintenance (PdM) techniques can help mitigate these risks by predicting machine failures before they occur. This research, therefore, develops two supervised Machine Learning (ML) algorithms to predict failure conditions and the associated failure modes for a milling machine. Six machine features were investigated. The ML models were then developed using a four-stage process including data pre-processing, training, evaluation, and deployment. Several ML algorithms were applied then the results were evaluated using accuracy, recall, precision, and [Formula: see text]1-score. The results showed that the Random Forest algorithm was the most effective in predicting machine conditions and failure modes of the milling machine. A web application for PdM was finally developed and tested for the PdM of the milling machine. In conclusion, the developed web application including ML algorithms can support the effective PdM for the machine, which leads to enhancing its availability and improving productivity. Future research considers adopting ML algorithms for predicting machine conditions and failure types of other machines.

Investigation of diesel particulate filter performance under typical failure conditions

Diesel particulate filter (DPF) is one of the most effective particulate reduction components in diesel engine aftertreatment systems. However, prolonged utilization of a DPF might result in a deterioration in performance or even failure, such as ash-clogging, melting, breakage, and catalyst deactivation. It is crucial to conduct research on DPF performance degradation across a series of partially failed conditions. In this study, the morphology and chemical composition of the ash in the DPF samples were characterized. The effects of degree-varying clogging and unplugged-breakage failure on exhaust characteristics as well as DPF temperature, filtration and pressure drop characteristics were investigated. Finally, the sensitivity differences between DPF performance parameters and failure index parameters were examined using a canonical correlation analysis (CCA). When four factors were considered, the ash loading index and breakage index had the greatest impact on DPF temperature difference and filtration efficiency, while the exhaust mass flow rate had the greatest impact on pressure drop. Besides, the influence of the DPF inlet temperature on the pressure drop, filtration efficiency, and temperature difference was not significant. These findings contribute to the prediction of DPF performance in partially failed conditions and the development of DPF failure diagnosis methods.

Feasibility of a digital palliative care intervention (Convoy-Pal) for older adults with heart failure and multiple chronic conditions and their caregivers: a waitlist randomized control trial

BackgroundAlthough older adults with heart failure (HF) and multiple chronic conditions (MCC) frequently rely on caregivers for health management, digital health systems, such as patient portals and mobile apps, are designed for individual patients and often exclude caregivers. There is a need to develop approaches that integrate caregivers into care. This study tested the feasibility of the Social Convoy Palliative Care intervention (Convoy-Pal), a 12-week digital self-management program that includes assessment tools and resources for clinical palliative care, designed for both patients and their caregivers.MethodsA randomized waitlist control feasibility trial involving patients over 65 years old with MCC who had been hospitalized two or more times for HF in the past 12 months and their caregivers. Descriptive statistics were used to evaluate recruitment, retention, missing data, self-reported social functioning, positive aspects of caregiving, and the acceptability of the intervention.ResultsOf 126 potentially eligible patients, 11 were ineligible and 69 were deceased. Of the 46 eligible patients, 31 enrolled in the trial. Although 48 caregivers were identified, only 15 enrolled. The average age was 76.3 years for patients and 71.6 years for caregivers, with most participants being non-Hispanic White. Notably, 4% did not have access to a personal mobile device or computer. Retention rates were 79% for intervention patients, 57% for intervention caregivers, and 60% for control participants. Only 4.6% of survey subscales were missing, aided by robust technical support. Intervention patients reported improved social functioning (SF-36: 64.6 ± 25.8 to 73.2 ± 31.3) compared to controls (64.6 ± 27.1 to 67.5 ± 24.4). Intervention caregivers also reported increased positive perceptions of caregiving (29.5 ± 5.28 to 35.0 ± 5.35) versus control caregivers (29.4 ± 8.7 to 28.0 ± 4.4). Waitlist control participants who later joined the Convoy-Pal program showed similar improvements. The intervention was well-rated for acceptability, especially regarding the information provided (3.96 ± .57 out of 5).ConclusionsRecruiting informal caregivers proved challenging. Nonetheless, Convoy-Pal retained patients and collected meaningful self-reported outcomes, showing potential benefits for both patients and caregivers. Given the importance of a patient and caregiver approach in palliative care, further research is needed to design digital tools that cater to multiple simultaneous users.Trial registrationClinicalTrials.gov Identifier NCT04779931. Date of registration: March 3, 2021.

Modeling hard rock breakage behavior influenced by the tipped hob cutter's tooth structure using the 2D discrete element (DE) model

A deep understanding of the mesoscopic damage evolution mechanism of tipped hob cutter in hard rock, as influenced by tooth structure parameters and working parameters, can assist in optimizing the tooth of tipped hob cutter and uncovering the failure conditions and mechanism of rock. In this paper, a series of numerical simulations of rock breaking by tipped hob cutter's tooth were modelled, calibrated, and analyzed to investigate the failure conditions and mechanism of rock by tipped hob cutter, as well as the effects of tooth profiles and working parameters on the fracture evolution of hard rock. The results show that the most efficient combination of tooth profile and Dp differs depending on the rock types. D-type teeth with a penetration depth of Dp = 3 mm are optimal for crushing granite, while A-type teeth with a penetration depth of Dp = 4 mm are suitable for crushing sandstone.