Failure Time Research Articles

Geographically weighted accelerated failure time model for spatial survival data: application to ovarian cancer survival data in New Jersey

BackgroundIn large multiregional cohort studies, survival data is often collected at small geographical levels (such as counties) and aggregated at larger levels, leading to correlated patterns that are associated with location. Traditional studies typically analyze such data globally or locally by region, often neglecting the spatial information inherent in the data, which can introduce bias in effect estimates and potentially reduce statistical power.MethodWe propose a Geographically Weighted Accelerated Failure Time Model for spatial survival data to investigate spatial heterogeneity. We establish a weighting scheme and bandwidth selection based on quasi-likelihood information criteria. Theoretical properties of the proposed estimators are thoroughly examined. To demonstrate the efficacy of the model in various scenarios, we conduct a simulation study with different sample sizes and adherence to the proportional hazards assumption or not. Additionally, we apply the proposed method to analyze ovarian cancer survival data from the Surveillance, Epidemiology, and End Results cancer registry in the state of New Jersey.ResultsOur simulation results indicate that the proposed model exhibits superior performance in terms of four measurements compared to existing methods, including the geographically weighted Cox model, when the proportional hazards assumption is violated. Furthermore, in scenarios where the sample size per location is 20-25, the simulation data failed to fit the local model, while our proposed model still demonstrates satisfactory performance. In the empirical study, we identify clear spatial variations in the effects of all three covariates.ConclusionOur proposed model offers a novel approach to exploring spatial heterogeneity of survival data compared to global and local models, providing an alternative to geographically weighted Cox regression when the proportional hazards assumption is not met. It addresses the issue of certain counties' survival data being unable to fit the model due to limited samples, particularly in the context of rare diseases.

Modelling of the time to death of breast cancer patients at Hiwot Fana Specialized University Hospital

Breast cancer is the most common cause of cancer death and is a frequently diagnosed cancer among women worldwide. It is becoming a challenging health condition in Ethiopia with a high rate of morbidity and mortality. The main aim of this study was to model the time to death in breast cancer patients at Hiwot Fana Specialized University Hospital. A retrospective cohort study was carried out from April 1st, 2020, to April 1st, 2023, and 296 women were included in the study. We used nonparametric methods and Bayesian accelerated failure time models (with Laplace approximation) to identify risk factors and choose a model fitting breast cancer patient data. Model comparison was performed using the marginal likelihood, deviance information criterion and Watanabe Akaike information criterion. From the total of 296 patients in the study, 56 (18.9%) died. The estimated median survival time was 33 months. The log-rank test showed that age group, stage, alcohol consumption, smoking habit, and comorbidity were potential risk factors associated with the time to death in breast cancer patients at the 5% level of significance. The Bayesian Weibull accelerated failure time model was found to be the best fitted model for predicting the survival time of patients with minimum DIC (520.39) and WAIC (521.59) values. The final Bayesian Weibull AFT model with the integrated nested Laplace approximation estimation technique revealed that age group, stage, alcohol consumption, smoking habit, and comorbidity were significantly associated with the time to death in breast cancer patients. Individuals older than 65 years, with stage IV disease, drinking alcohol, smoking cigarettes and having comorbidities had shortened survival times in patients with breast cancer. Hence, Hiwot Fana Specialized University Hospital and related bodies should work on awareness creation to reduce smoking habits and alcohol use as well as give due attention to elderly and stage IV breast cancer patients during intervention.

Unique exploration method of electronic component failure time and yield strengths of circular tubes under complete flexible model.

The aim of this study is to develop new exponential weighted moving average control charts based on a flexible model. These control charts created through least square and weighted least square estimators of the shape parameter of the new Kumaraswamy Pareto distribution. Exponential weighted moving average control charts based on least square and weighted least square estimators are compared for checking the performance of control charts. The results were not only explored through numerical values but also explored through half a dozen plots. The numerical results and plots exposed that the exponential weighted moving average control chart based on weighted least square estimator has better performance than the other proposed chart. Some key findings are discussed which are obtained from the comparative analysis of EWMA control charts. The simulation study of proposed charts is also reported in detail. The two data sets further demonstrate the effectiveness of the proposed charts. The reported results, for real data sets, are not only displayed in normal plots but also displayed in three-dimension plots. We recommend that the proposed method can be adapted for different types of distributions, and also suggest some future research directions. The concluding remarks are reported at the end of this manuscript.

Remaining Useful Life Prediction Method Based on Dual-Path Interaction Network with Multiscale Feature Fusion and Dynamic Weight Adaptation

In fields such as manufacturing and aerospace, remaining useful life (RUL) prediction estimates the failure time of high-value assets like industrial equipment and aircraft engines by analyzing time series data collected from various sensors, enabling more effective predictive maintenance. However, significant temporal diversity and operational complexity during equipment operation make it difficult for traditional single-scale, single-dimensional feature extraction methods to effectively capture complex temporal dependencies and multi-dimensional feature interactions. To address this issue, we propose a Dual-Path Interaction Network, integrating the Multiscale Temporal-Feature Convolution Fusion Module (MTF-CFM) and the Dynamic Weight Adaptation Module (DWAM). This approach adaptively extracts information across different temporal and feature scales, enabling effective interaction of multi-dimensional information. Using the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset for comprehensive performance evaluation, our method achieved RMSE values of 0.0969, 0.1316, 0.086, and 0.1148; MAPE values of 9.72%, 14.51%, 8.04%, and 11.27%; and Score results of 59.93, 209.39, 67.56, and 215.35 across four different data categories. Furthermore, the MTF-CFM module demonstrated an average improvement of 7.12%, 10.62%, and 7.21% in RMSE, MAPE, and Score across multiple baseline models. These results validate the effectiveness and potential of the proposed model in improving the accuracy and robustness of RUL prediction.

Deep Neural Network-Based Accelerated Failure Time Models Using Rank Loss.

An accelerated failure time (AFT) model assumes a log-linear relationship between failure times and a set of covariates. In contrast to other popular survival models that work on hazard functions, the effects of covariates are directly on failure times, the interpretation of which is intuitive. The semiparametric AFT model that does not specify the error distribution is sufficiently flexible and robust to depart from the distributional assumption. Owing to its desirable features, this class of model has been considered a promising alternative to the popular Cox model in the analysis of censored failure time data. However, in these AFT models, a linear predictor for the mean is typically assumed. Little research has addressed the non-linearity of predictors when modeling the mean. Deep neural networks (DNNs) have received much attention over the past few decades and have achieved remarkable success in a variety of fields. DNNs have a number of notable advantages and have been shown to be particularly useful in addressing non-linearity. Here, we propose applying a DNN to fit AFT models using Gehan-type loss combined with a sub-sampling technique. Finite sample properties of the proposed DNN and rank-based AFT model (DeepR-AFT) were investigated via an extensive simulation study. The DeepR-AFT model showed superior performance over its parametric and semiparametric counterparts when the predictor was nonlinear. For linear predictors, DeepR-AFT performed better when the dimensions of the covariates were large. The superior performance of the proposed DeepR-AFT was demonstrated using three real datasets.

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.

Novel machine learning technique further clarifies unrelated donor selection to optimize transplantation outcomes

AbstractWe investigated the impact of donor characteristics on outcomes in allogeneic hematopoietic cell transplantation (HCT) recipients using a novel machine learning approach, the Nonparametric Failure Time Bayesian Additive Regression Trees (NFT BART). NFT BART models were trained on data from 10 016 patients who underwent a first HLA-A, B, C, and DRB1 matched unrelated donor HCT between 2016 and 2019, reported to the Center for International Blood and Marrow Transplant Research, then validated on an independent cohort of 1802 patients. The NFT BART models were adjusted based on recipient, disease, and transplant variables. We defined a clinically meaningful impact on overall survival (OS) or event-free survival (EFS; survival without relapse, graft failure, or moderate to severe chronic graft-versus-host disease) as >1% difference in predicted outcome at 3 years. Characteristics with <1% impact (within a zone of indifference) were not considered to be clinically relevant. Donor cytomegalovirus, parity, HLA-DQB1, and HLA-DPB1 T-cell epitope matching fell within the zone of indifference. The only significant donor factor that associated with OS was age, in which, compared with 18-year-old donors, donors aged ≥31 years old were associated with lower OS. Both donor age (≤32 years) and use of a male donor, regardless of recipient sex, improved EFS. We, therefore, recommend selecting the earliest available donor within the 18 to 30 years age range for HCT to optimize OS. If several donors in the 18 to 30 years age range are available, a male donor may be chosen to optimize EFS.

Determinants of Outcomes for Acute Myeloid Leukemia Patients Treated in a Community-Based Specialized Versus Non-Specialized Hospital Setting.

The treatment setting influences acute myeloid leukemia (AML) outcomes. Most cancer patients receive care in the community, yet few studies have evaluated the effectiveness of clinical programs outside of academic or National Cancer Institute (NCI)-designated cancer centers. This was a multi-level, case-controlled study of real-world outcomes for initial AML treatment in a community-based network for 1,391 patients with AML between 2011 and 2018. We benchmarked survival within our network against the Surveillance, Epidemiology, and End Results (SEER) database. Coarsened exact matching was performed against 17,186 chemotherapy-treated patients in the SEER database. Cox proportional and accelerated failure time multivariable modeling were performed to identify patient, disease, therapy and center characteristics associated with the risk of AML mortality. Within the network, 799 patients were treated at six specialized blood cancer centers and 592 at 63 other hospitals. Patients receiving high-intensity induction at specialized centers had improved median survivals of 31 months versus 18 months [P<0.001] at non-specialized centers. Median survivals were 13 for non-specialized centers versus 10 months for SEER [P<0.001], and 18 for the entire network versus 10 months for SEER [P<0.001]. Multivariable modeling showed significant impacts from age (HR = 1.025), high-intensity induction therapy (HR= .695), diagnosis year (HR= .937), neighborhood income (HR = .997; P<0.01), higher acuity (HR = 1.002) and Charlson comorbidity score (HR = 1.117). AML treatment may be effectively delivered in the community hospital setting, with specialized centers producing better outcomes for higher intensity treatments.

Prognostic factors for premature cardiovascular disease mortality in Malaysia: a modelling approach using semi-parametric and parametric survival analysis with national health and morbidity survey linked mortality data.

Cardiovascular disease (CVD) is the leading cause of premature mortality worldwide. Despite existing research on CVD risk factors, the study of premature CVD mortality in Malaysia remains limited. This study employs survival analysis to model modifiable risk factors associated with premature CVD mortality among Malaysian adults. We utilised data from Malaysia's National Health and Morbidity Survey (NHMS) conducted in 2006, 2011, and 2015, linked with mortality records. The cohort comprised individuals aged 18 to 70 during the NHMS interview. Follow-up extended to 2021, focusing on CVD-related premature mortality between ages 30 and 70. We employed six survival models: a semi-parametric Cox proportional hazard (PH) and five parametric survival models, which were Exponential, Weibull, Gompertz, log-normal and log-logistic distributions using R software. The age standardized incidence rate (ASIR) of premature CVD mortality was calculated per 1000 person-years. Among 63,722 participants, 886 (1.4%) experienced premature CVD mortality, with an ASIR of 1.80 per 1000 person-years. The best-fit models (based on AIC value) were the stratified Cox model by age (semi-parametric) and the log-normal accelerated failure time (AFT) model (parametric). Males had higher risk (Hazard Ratio, HR = 2.68) and experienced 49% shorter survival time (Event Time Ratio, ETR = 0.51) compared to females. Compared to Chinese ethnicity, Indians, Malays, and other Bumiputera had higher HR and lower survival times. Rural residents and those with lower education also faced increased HRs and reduced survival times. Diabetes (diagnosed: HR = 3.26, ETR = 0.37; undiagnosed: HR = 1.63, ETR = 0.63), hypertension (diagnosed: HR = 1.84, ETR = 0.53; undiagnosed: HR = 1.46, ETR = 0.68), and undiagnosed hypercholesterolemia (HR = 1.31, ETR = 0.80) increased risk and decreased survival times. Additionally, current smoking and abdominal obesity elevated risk (HR = 1.38, 1.60) and shortened survival (ETR = 0.81, 0.71). The semi-parametric and parametric survival models both highlight the considerable impact of socioeconomic status and modifiable risk factors on premature CVD mortality, underscoring the imperative for targeted interventions to effectively mitigate these effects.

An Order Statistics Perspective for System Reliability

ABSTRACTStructural reliability integrates the design variables over the safety region characterized by a positive limit state function when the reliability of the entire system is of concern. Calculating the structure function can be challenging for high‐dimensional systems or intricate system architectures. In order to enhance the efficiency of time‐dependent system reliability assessment, we emulate the integral formulation in structural reliability. To elaborate further, we treat each individual unit's lifetime variable as a design variable and subsequently perform calculations involving multiple integrals. Given the ordered nature of unit failure times, we leverage the order statistics distribution to simplify the multiple integrals into a double integral, and then multiply this result by the survival signature to obtain reliability. A two‐terminal nine‐unit network system configuration is illustrated to assess the performance and effectiveness of the proposed method.

Limiting Behavior of Mixed Coherent Systems With Lévy‐Frailty Marshall–Olkin Failure Times

ABSTRACTIn this article, we show a limit result for the reliability function of a system—that is, the probability that the whole system is still operational after a certain given time—when the number of components of the system grows to infinity. More specifically, we consider a sequence of mixed coherent systems whose components are homogeneous and non‐repairable, with failure‐times governed by a Lévy‐Frailty Marshall–Olkin (LFMO) distribution—a distribution that allows simultaneous component failures. We show that under integrability conditions the reliability function converges to the probability of a first‐passage time of a Lévy subordinator process. To the best of our knowledge, this is the first result to tackle the asymptotic behavior of the reliability function as the number of components of the system grows. To illustrate our approach, we give an example of a parametric family of reliability functions where the system failure time converges in distribution to an exponential random variable, and give computational experiments testing convergence.

Incidence and predictors of unplanned 30-day hospital readmissions among heart failure patients in Ethiopia: a 5-year retrospective cohort study

The burden of heart failure increases over time and is a leading cause of unplanned readmissions worldwide. In addition, its impact has doubled in countries with limited health resources, including Ethiopia. Identifying and preventing the possible contributing factors is crucial to reducing unplanned hospital readmissions and improving clinical outcomes. The study aimed to assess the incidence and predictors of 30-day unplanned readmission among heart failure patients at selected South Wollo general hospitals in 2022. A hospital-based retrospective cohort study design was employed from January 1, 2016, to December 30, 2020. The data was collected from 572 randomly selected medical records using data extraction checklists. Data were entered in Epi-Data version 4.6 and analyzed with Stata version 17. The Kaplan–Meier and log-rank tests were used to estimate and compare the survival failure time. A Cox proportional hazard analysis was used to identify the predictors of readmission. The statistical significance level was declared at a p-value < 0.05 with an adjusted odds ratio and a 95% confidence interval. A total of 151 (26.40%) heart failure patients were readmitted within 30 days of discharge. Among the study participants, 302 (52.8%) were male, and 370 (64.7%) were rural residents. The mean age was 45.8 ± 14.1 SD years. In the multivariate Cox proportional hazards analysis being an age (> 65 years) (AHR: 3.172, 95% CI:.21, 4.55, P = 0.001), rural in residency (AHR: 2.47, 95%CI: 1.44, 4.24, P = 0.001), Asthma or Chronic Obstructive Pulmonary Disease (AHR: 1.62, 95% CI: 1.11, 2.35, P = 0.012), HIV/AIDS (AHR: 1.84, 95%CI: 1.24, 2.75, P = 0.003), Haemoglobin level 8–10.9 g/dL (AHR: 6.20, 95% CI: 3.74, 10.28, P = 0.001), and Mean platelet volume > 9.1 fl (AHR: 2.08, 95% CI: 1.27, 3.40, P = 0.004) were identified as independent predictors of unplanned hospital readmission. The incidence of unplanned hospital readmission was relatively high among heart failure patients. Elderly patients, rural residency, comorbidity, a higher mean platelet volume, and a low hemoglobin level were independent predictors of readmission. Working on these factors will help reduce the hazards of unplanned hospital readmission.

Leveraging External Aggregated Information for the Marginal Accelerated Failure Time Model.

It is becoming increasingly common for researchers to consider leveraging information from external sources to enhance the analysis of small-scale studies. While much attention has focused on univariate survival data, correlated survival data are prevalent in epidemiological investigations. In this article, we propose a unified framework to improve the estimation of the marginal accelerated failure time model with correlated survival data by integrating additional information given in the form of covariate effects evaluated in a reduced accelerated failure time model. Such auxiliary information can be summarized by using valid estimating equations and hence can then be combined with the internal linear rank-estimating equations via the generalized method of moments. We investigate the asymptotic properties of the proposed estimator and show that it is more efficient than the conventional estimator using internal data only. When population heterogeneity exists, we revise the proposed estimation procedure and present a shrinkage estimator to protect against bias and loss of efficiency. Moreover, the proposed estimation procedure can be further refined to accommodate the non-negligible uncertainty in the auxiliary information, leading to more trustable inference conclusions. Simulation results demonstrate the finite sample performance of the proposed methods, and empirical application on the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial substantiates its practical relevance.

Investigation of polyurea coating influence on dynamic mechanical properties of concrete

To investigate the influence of polyurea coating on the dynamic mechanical properties of concrete, this study explores the energy absorption characteristics, stress-time curves, and failure modes of polyurea-coated concrete composite specimens under dynamic loading. The variations in the dynamic mechanical properties of polyurea-coated concrete composite specimens under complex conditions, such as different polyurea coating thicknesses and locations, as well as different strengths of coated concrete, are analyzed. The results indicate that, under the same impact velocity, with the increase of polyurea coating thickness, the energy dissipation coefficient and the percentage increase in energy dissipation first gradually increase to a certain value and then decrease. Polyurea coating significantly increases the deformation capacity of concrete specimens and effectively delays the time of concrete failure. The cracks and fragments of concrete on both sides, the impact side and the back impact side, gradually decrease with the increase of polyurea coating thickness, and the impact side coated with polyurea is superior to the back impact side, but this trend becomes less significant when the coating thickness reaches around 5 mm. With the increase of concrete strength, the percentage increase in peak stress time for both the back impact side and the impact side of polyurea-coated concrete gradually decreases. Under the same impact velocity, the energy dissipation effect of polyurea-coated concrete composite specimens is superior to that of concrete specimens.The polyurea coating enhances the toughness of concrete and improves its brittleness.

A Study on the Mast Structure Vibration Limits considering the Operating Time of Naval Ships

The mast structure of a naval ship has a lot of expensive equipment attached and is easily exposed to strong vibration due to its structural characteristics, so it is necessary to manage vibrations well to prevent equipment failure and increase operating time. Since September 2015, the Navy has been managing the vibration of the ship's mast structure with a separate tolerance standard from the local vibration, but this standard is set at 1/2 of the vibration limits for mast-mounted equipment without any engineering basis. So the establishment of new vibration limits based on the operating conditions of naval ships is required. In this study, the mast structure vibration limits were reviewed considering the actual operating time of naval ships, and the results were compared with the standards of major overseas classification societies and the actual measurements of a ship that had mast structure vibration problems.

Reactive transport model of the long-term geochemical evolution in a HLW repository in granite at the disposal cell scale: Variants, sensitivities, and model simplifications

The assessment of the long-term performance of the engineered barrier systems of high-level radioactive waste (HLW) repositories requires the use of reactive transport models. Montenegro et al. (2023) presented a non-isothermal reactive transport model of the long-term geochemical evolution of a HLW disposal cell in a granitic host rock corresponding to a generic reference concept. The model accounted for the vitrified waste, the carbon-steel canister, the bentonite buffer and the reference granitic rock. Here we extend their model by considering model variants (V), sensitivity cases (SC) and model abstractions (MA). Variants V1, V2 and V3 consist of considering MX-80 bentonite instead of FEBEX bentonite (V1), a larger groundwater flux through the granite (V2) and the Czech reference crystalline rock as a host rock (V3). Cases SC1 and SC2 consider a decrease of the silica concentration threshold value in the glass dissolution rate (SC1) and an earlier canister failure time (SC2), respectively. Runs MA1 to MA4 consider smectite as an unreactive mineral phase (MA1), the porosity feedback effect on chemical and transport parameters (MA2), a time-varying corrosion rate (MA3), and a coarser finite element grid (MA4), respectively. Model results of V1 show a larger pH, a smaller precipitation of magnetite, siderite and greenalite and a slightly smaller dissolution of ISG and smectite than the base run of Montenegro et al. (2023). Model predictions are very sensitive to the increase in the groundwater flow through the granitic host rock (V2). However, predictions are not sensitive to the chemical composition of the granite porewater (V3). The decrease in the silica saturation threshold from 1·10−3 to 5·10−4 mol/L in SC1 leads to a significant decrease in glass dissolution. Glass dissolution after 50,000 years in SC2 (earlier canister failure) is much larger than that of the base run. Model results are not sensitive to considering smectite as an unreactive mineral phase (MA1). However, model results are very sensitive to the porosity feedback effect (MA2). A 60% volume fraction of Fe(s) remains uncorroded after 50,000 years when a variable corrosion rate is considered in MA3. In this case the precipitation of corrosion products is much smaller than that of the base run. The general patterns of the numerical results in MA4 (coarser grid) are similar to those of the base case.

Pairwise Accelerated Failure Time Regression Models for Infectious Disease Transmission in Close-Contact Groups With External Sources of Infection.

Many important questions in infectious disease epidemiology involve associations between covariates (e.g., age or vaccination status) and infectiousness or susceptibility. Because disease transmission produces dependent outcomes, these questions are difficult or impossible to address using standard regression models from biostatistics. Pairwise survival analysis handles dependent outcomes by calculating likelihoods in terms of contact interval distributions in ordered pairs of individuals. The contact interval in the ordered pair is the time from the onset of infectiousness in to infectious contact from to , where an infectious contact is sufficient to infect if they are susceptible. Here, we introduce a pairwise accelerated failure time regression model for infectious disease transmission that allows the rate parameter of the contact interval distribution to depend on individual-level infectiousness covariates for , individual-level susceptibility covariates for , and pair-level covariates (e.g., type of relationship). This model can simultaneously handle internal infections (caused by transmission between individuals under observation) and external infections (caused by environmental or community sources of infection). We show that this model produces consistent and asymptotically normal parameter estimates. In a simulation study, we evaluate bias and confidence interval coverage probabilities, explore the role of epidemiologic study design, and investigate the effects of model misspecification. We use this regression model to analyze household data from Los Angeles County during the 2009 influenza A (H1N1) pandemic, where we find that the ability to account for external sources of infection increases the statistical power to estimate the effect of antiviral prophylaxis.

Can satellite InSAR innovate the way of large landslide early warning?

Predicting landslide failure times is an essential component in landslide risk management. Although in-situ sensor-supported landslide early warning systems are still predominantly used, their high cost makes it impractical to monitor all the landslides, thereby posing a major challenge for the effective landslide risk management. Hence, this study investigated this problem from an earth observation perspective and proposed a probabilistic landslide failure time prediction framework integrating Interferometric Synthetic Aperture Radar (InSAR) monitoring information. Accordingly, 30 historical landslides that occurred between 2016 and 2021 in central and western China were collected to evaluate the feasibility of the aforementioned framework. Based on the landslide dataset, the performance of the satellite InSAR technology for landslide failure time prediction is evaluated systematically from an application perspective. It was evident that eleven landslides (36.67 %) were captured by InSAR with accelerated deformation signals before failure, and monitoring data from eight (26.67 %) of them provided enough information for their failure time prediction. Further, a probabilistic method integrating the conventional inverse velocity model and sequential Bayesian updating was proposed to dynamically predict the most likely failure time and related confidence interval. Case studies showed that the proposed method could successfully predict the failure time of the eight landslides, thus demonstrating the feasibility of the framework. Although the current long revisit period of satellites constrains their performance practically, this problem can be solved by advancements in future satellite missions. Thus, we believe that the InSAR era is imminent and will bring substantial values for large landslide early warning.

Predicting the Timing and Cause of Microvascular Free Flap Failure: An Australian Study of 1569 Free Flaps.

The aim of the study is to assess the possible predictors of microvascular free flap failure and determine the critical postoperative timing of flap failure, thereby minimizing this adverse outcome. This is a retrospective single-institutional review of 1569 free flap operations. All free flaps with outcome status recorded were analyzed for possible predictors in the development of microvascular compromise. Compromised cases were then analyzed for differences in time to compromise and time to theater takeback between salvaged versus failed free flaps. Of the assessable 1569 free flaps, 31 developed microvascular compromise (2.0%); the salvage rate was 20.0%, and overall failure rate was 1.5%. Osteocutaneous free flaps in head and neck had increased risk of developing free flap failure compared to other flaps (odds ratio = 3.8, 95% confidence interval: 1.2-12.7). Among breast patients, previous radiotherapy had a significant association with flap failure ( P < 0.001). Free flap salvage rates dropped from 38.5% to 7.7% for compromises detected after 24 hours ( P = 0.160), and from 57.1% to 11.1% for free flaps taken back to theater greater than 3 hours after compromise detection ( P = 0.032). Delays in compromise detection and flap takeback to theater resulted in reduced salvage rates, with the critical timing of takeback being under 3 hours. Standardized flap monitoring protocols and incorporation of newer technologies could see faster detection and improved salvage rates. Additional care is required when reconstructing: osteocutaneous head and neck free flaps and previously irradiated breast patients.

Failure signal of high orientation Poly(L-lactic acid) monofilament for self-expanding stent during lifetime: Disappeared amorphous connections

The premature failure of bioresorbable self-expanding stents for peripheral arterial disease could lead to form thrombosis and threaten patients′ lives. Predicting the time and cause of mechanical and structural failure is crucial to prevent premature failure of implanted stents. Herein, we report a situ accelerated hydrolysis to investigate the degradation properties of self-expanding stents braided by high-performance Poly(L-lactic acid) (PLLA) monofilaments in an accelerated term over a formal long one. Degradation, microstructure, and mechanical properties were comprehensively evaluated to identify key factors of the failure during lifetime. The results show that the upper temperature limit for accelerated hydrolysis is 50 °C. The mechanical properties undergo multiple stages of functional holding, decreasing, and eventual failure. It is attributed to uneven degradation and distribution of crystalline and amorphous regions. Specifically, the disappearance of amorphous chains between fibrils at 6 months leads to mechanical failure. Further disappearance of amorphous chains within fibrils at 18 months results in structural disintegration. Additionally, the degradation behavior of the stents and potential effects of degradation products on vascular tissues are validated for predictive accuracy. These insights provide valuable experimental references for optimizing design and clinical application of PLLA-based self-expanding stents.