Browse Articles

Hierarchical bayesian fusion of inspection and monitoring data for probabilistic bridge deterioration assessment

Abstract Bridges are susceptible to long-term deterioration due to environmental exposure and cyclic loading, making the accurate evaluation of crack evolution crucial for predictive maintenance and structural safety management. Traditional deterioration models that rely solely on periodic inspection data often fail to capture the dynamic and stochastic nature of crack propagation. To address this limitation, this study proposes a hierarchical Bayesian inference framework that integrates discrete inspection data with continuous crack monitoring data to achieve a unified probabilistic characterization of bridge deterioration. First, a Bayesian Accelerated Failure Time (AFT) model based on the Weibull distribution is developed to model the failure risk of bridge deck slabs. The model robustly handles right-censored data through sampling and incorporates multiple covariates, including crack number, type, damage category, bridge span, and geometric parameters. Posterior distributions of the shape and scale parameters, together with hazard ratio analysis, quantitatively reveal the influence of each factor on structural failure risk. Second, within the dynamic state layer of the proposed hierarchical framework, a Metropolis–Hastings-based regression model is constructed to estimate incremental crack growth, which is then aggregated monthly to form a continuous degradation trend series. This method effectively captures the response of crack development to environmental fluctuations and supports predictive analysis for on-site maintenance planning. Finally, the failure risk model and the crack evolution model are coupled within a hierarchical Bayesian framework to enable joint risk estimation. The proposed model integrates multi-source information with varying temporal resolutions and uncertainty levels and employs a Bayesian posterior updating mechanism to adaptively refine parameters as new monitoring data become available. Validation using real bridge monitoring datasets demonstrates that the posterior-updated model significantly outperforms traditional inspection-based approaches in capturing crack failure behavior and long-term deterioration trends.

Functional and cognitive correlates of typing speed in a large U.S. panel study

Abstract Typing has become increasingly integral to everyday functioning. To comprehensively understand people’s level of functioning, researchers may also want to consider typing performance in addition to performance of other classical activities of daily living like dressing, shopping, or managing a budget. Classical typing tests are too burdensome to administer in panel studies. We examined functional and cognitive correlates of a one sentence typing speed test to examine if this brief measure could provide meaningful information. A sample of 10,613 adult participants in the Understanding America Study (UAS), a nationally representative longitudinal online survey panel, completed surveys, cognitive tests, and the typing test. Intraclass correlation coefficients of 0.79 for computer typing speed and 0.63 for smartphone typing speed suggested high test–retest stability for the brief typing test across 2 years. Faster typing speed was generally associated with better cognitive functioning across domains, fewer reported difficulties with basic and instrumental activities of daily living, and a lower likelihood of several self-reported illnesses. After adjustment for demographics, the correlation magnitudes were attenuated but still generally in the expected directions. Typing speed as captured by a single sentence typing speed test appeared to be sensitive to several measures relevant to everyday functioning.

Mindful eating may help explain the association between psychological difficulties and food addiction in adolescents with obesity

Shadow fading prediction at 18 GHz through physics guided learning in vegetative corridors

Scalable DICOM 3D-printed phantoms mimicking marine mammal bone and soft tissue

A lightweight YOLO-based model for accurate detection of red pepper clusters in robotic harvesting

Group learning in recommendation systems: towards adaptive and implicit group modeling

Ultrasound-triggered amygdalin drug release across U-87 glioblastoma cell lines from gH625-tagged phase convertible nanodroplets

Geophysical assessment of seawater intrusion in Apapa-Ajegunle, coastal area of Lagos, Southwestern Nigeria

Towards standardisation of zinc slag as a sustainable fine aggregate substitute in concrete

Asterinides sp. an endemic stygobitic seastar from an anchialine cave and its interactions among prokaryotic communities

Co-occurrence of selenium and toxic elements and health risk assessment in commercial selenium-enriched rice from China

The significance of immunogenic cell death related prognostic gene markers in colorectal cancer prognosis and antitumor immunity

Correction: Comparative study of advanced hydrogen liquefaction using triple cascade mixed refrigerant cycles with integrated energy exergy economic and environmental analysis

A memorized multi-objective Sinh-Cosh optimizer for solving multi-objective engineering design problems

Abstract The Multi-Objective Sinh-Cosh Optimization Algorithm (MOSCHO) is presented in this article based on the memorized technique. MOSCHO is an extension version of the recently proposed Sinh-Cosh optimizer for multiple objective optimizations. The memorized local optimum is integrated with the global optimal solution to bound the search space and update positions of solutions for obtaining non-dominated solutions. The proposed method is tested on mathematical non-constrained functions, SRN constrained function, and three real-world design engineering applications, as a vital challenge to handle the difficulties of real-world engineering applications. The MOSCHO’s performance was evaluated by seven performance metrics compared to some of the most popular multi-objective optimization algorithms. The results demonstrate the ability of MOSCHO to achieve a high convergence and a good diversity. The results clarify that three functions have the best performance for all tested performance metrics: ZDT3, ZDT4, and MMF14. Five functions have the best performance for more than 75% of the performance metrics. Two functions have the best performance for more than 50% of performance metrics. The others have only the best values for more than 25% of performance metrics. However, SRN and real-world problems exhibit the best performance in more than 75% of the tested performance metrics.

Evaluating the antioxidant and anti-inflammatory effect of melatonin in pediatric hemodialysis patients: a randomized, placebo-controlled trial

Abstract Children undergoing chronic hemodialysis are exposed to persistent oxidative stress and systemic inflammation, contributing to long-term cardiovascular complications. Melatonin (MLT) is a pleiotropic hormone with potential antioxidant and anti-inflammatory effects. Due to scarcity of studies on pediatrics this study sought to investigate the effects of MLT on oxidative stress and inflammation in pediatric hemodialysis patients. This prospective, block-randomized, double-blinded, placebo-controlled study aimed at assessing the effect of 5 mg MLT on oxidative stress and inflammation in pediatric hemodialysis patients. Forty eligible patients were randomly allocated into either MLT or placebo group. Serum malondialdehyde (MDA), nuclear factor kappa B (NF-κB) levels and lipid profile were measured at baseline and at the end of the study after 12 weeks. MLT significantly reduced the median percent change of serum NF-κB − 5.404(− 58.25–129.7) with p-value = 0.027 in addition to reduction in median total cholesterol in the MLT group from 163.7(134.5–259.5) at baseline to 144(113–242) at the end of the study with p-value = 0.038 and reduction of low-density lipoprotein levels from 96(78–183) to 78.5(48–171) at the end of the 12 weeks with p-value = 0.002 while there was no significance in the placebo group. Although, there was no statistical significance in serum MDA levels in the MLT group but significant increase in MDA levels in the placebo group was detected. MDA levels increased from 11.79 ± 5.078 to 14.79 ± 4.257 at the end of the study in the placebo group with p-value = 0.048, supplementation appears to have beneficial effects on ameliorating inflammation and reducing serum lipids. Moreover, MLT may have a protective antioxidant effect by reduction and inhibition of elevation of serum MDA levels. Trial registration : The study was registered on ClinicalTrials.gov (identifier: NCT05570526 https://clinicaltrials.gov/study/NCT05570526 ), on 6th of October 2022

A proposed reinforcement learning approach via discrete control reformulation and multi-step double DQN for adaptive cruise control in electric vehicles

Early neurophysiological signatures of multi-digit number length encoding

Accurate modelling of intrabeam scattering and its impact on photoinjectors for free-electron lasers

Abstract Intrabeam scattering (IBS) is a fundamental effect that can limit the performance of high-brightness electron machines but has so far been neglected in standard modeling of RF photoinjectors. Recent measurements at SwissFEL show that the slice energy spread (SES) in the injector is significantly underestimated in standard beam dynamic simulations. In this paper, we employ a dedicated Monte Carlo simulation model that accurately predicts IBS-induced SES growth in the photoinjector of an X-ray free-electron laser. The simulations are benchmarked against SES measurements at the SwissFEL and are supported by a new analytical model. The results show that IBS-induced SES growth occurs throughout the injector, most prominently in the electron source, and must be included in performance assessments. We further demonstrate that while 5D brightness is largely conserved, the 6D brightness degrades with propagation, highlighting the need to account for IBS in accurate photoinjector design and optimization.

Taxonomical modeling and classification in space hardware failure reporting

Abstract NASA Johnson Space Center has collected more than 54,000 space hardware failure reports. Obtaining engineering processes trends or root cause analysis by manual inspection is impractical. Fortunately, novel data science tools in Machine Learning and Natural Language Processing (NLP) can be utilized to perform text mining and knowledge extraction. In NLP the use of taxonomies (classification trees) are key to the structuring of text data, extracting knowledge and important concepts from documents, and facilitating the identification of correlations and trends within the data set. Usually, these taxonomies and text structures live in the heads of experts in their specific field. However, when an expert is not available, taxonomies and ontologies are not found in data bases, or the field of study is too broad, this approach can enable and provide structure to the text content of a record set. In this paper an automated taxonomical model is presented by the combination of Latent Dirichlet Allocation (LDA) algorithms and Bidirectional Encoder Representations from Transformers (BERT). Additionally, the limitations and outcomes of causal relationship rule mining models, commercial tools, and deep neural networks are also discussed.