Search Results (939)

Background: The aim of this study is to identify people with cystic fibrosis (pwCF) at risk for future pulmonary exacerbations (PEx) based on established and unestablished markers of chronic inflammation. There is currently no universal definition of PEx in cystic fibrosis (CF), but it is commonly characterized by clinical deterioration and a drop in FEV1 ≥10% with or without elevations in systemic inflammatory markers. PEx negatively affect clinical outcomes in pwCF; therefore, predicting and preventing PEx is a crucial goal in the treatment of pwCF. Methods: We retrospectively examined pwCF ≥18 years who had ≥2 pulmonary function tests per year for a 3-year period. The first year was marked as the baseline. The follow-up period (FU) was defined as the following two-year period after baseline. PEx were defined as a need for intravenous antibiotic treatment due to clinical deterioration. Various scoring systems and ratios (neutrophil/lymphocyte (NLR), lymphocyte/monocyte (LMR), CRP, CRP/albumin, Glasgow Prognostic Score (GPS), high-sensitivity modified Glasgow Prognostic Score (hs-GPS)) were compared in pwCF with and without PEx during the FU. Logistic regression models were used to determine the best marker for predicting PEx, considering factors such as age, sex, PEx at baseline, BMI, homozygote F508del mutation, diabetes mellitus, chronic bacterial infection, and CFTR (cystic fibrosis transmembrane conductance regulator)-modulator therapy. The results are reported as odds ratios (ORs) with p-values. Results: Out of 283 pwCF, 131 were included in the study. In total, 43.5% were female, and the mean age was 34.0 years. A total of 75 pwCF (57.3%) had PEx during FU. In the multivariate analysis, the following markers at baseline were significantly associated with having a PEx during FU: CRP(log) (OR = 7.29, p = 0.01), CRP/albumin (OR = 1.08, p = 0.006), decreased LMR (OR = 0.51, p = 0.02), increased NLR (OR = 1.52, p = 0.02), and GPS of 1 vs. 0 (OR = 2.75, p = 0.04). The results indicate that the CRP/albumin ratio was the best model for predicting PEx in pwCF during the FU, outperforming other models. Conclusions: While several inflammation-based scoring systems can predict PEx in pwCF, the easily calculated CRP/albumin proved to reliably identify pwCF with an increased risk for PEx, making it a promising tool in clinical practice. Full article

Digital twins (DTs), virtual replicas that integrate mechanistic modeling with real-time clinical data, are emerging as powerful tools in healthcare with particular promise in pediatrics, where age-dependent physiology and ethical considerations complicate infectious disease management. This narrative review examines current and potential applications of DTs across antimicrobial stewardship (AMS), diagnostics, vaccine personalization, respiratory support, and system-level preparedness. Evidence indicates that DTs can optimize antimicrobial therapy by simulating pharmacokinetics and pharmacodynamics to support individualized dosing, enable Bayesian therapeutic drug monitoring, and facilitate timely de-escalation. They also help guide intravenous-to-oral switches and treatment durations by integrating host-response markers and microbiological data, reducing unnecessary antibiotic exposure. Diagnostic applications include simulating host–pathogen interactions to improve accuracy, forecasting clinical deterioration to aid in early sepsis recognition, and differentiating between viral and bacterial illness. Immune DTs hold potential for tailoring vaccination schedules and prophylaxis to a child’s unique immune profile, while hospital- and system-level DTs can simulate outbreaks, optimize patient flow, and strengthen surge preparedness. Despite these advances, implementation in routine pediatric care remains limited by challenges such as scarce pediatric datasets, fragmented data infrastructures, complex developmental physiology, ethical concerns, and uncertain regulatory frameworks. Addressing these barriers will require prospective validation, interoperable data systems, and equitable design to ensure fairness and inclusivity. If developed responsibly, DTs could redefine pediatric infectious disease management by shifting practice from reactive and population-based toward proactive, predictive, and personalized care, ultimately improving outcomes while supporting AMS and health system resilience. Full article

This study examined the combined effects of chloride ions and stray DC on reinforced concrete (RC) using electromigration and impressed-current methods under varying current densities (0.5, 3.0, 5.0 mA/cm²) and chloride concentrations (50, 1350, 5500 mg/kg). Chloride was identified as the dominant deterioration factor. At 3.0 mA/cm², cracking times in moderate and severe chloride environments decreased by 48.75% and 52.62%, respectively, compared to mild conditions. At 0.5 mA/cm² in severe conditions, the corrosion rate reached 1.317% after 20, 2.75 times that in moderate conditions. Electromigration specimens showed delayed cracking but deeper chloride penetration, while impressed-current specimens exhibited pronounced strip-shaped pitting corrosion. A quadratic polynomial model predicting cracking time based on current density and chloride concentration achieved high accuracy (R² = 0.95, mean relative error = 7.%). Actual corrosion mass loss was lower than theoretical Faraday values, with current efficiency increasing from 0.3–0.8% to 16.5–18.1% as current density and chloride content rose. These findings highlight the synergistic effect of stray current and chloride attack, emphasizing chloride concentration’s greater impact on service life. The model provides a scientific basis for RC durability design in urban rail transit and coastal engineering. Full article

The study estimated the shelf life of specialty coffee packaged in six types of packaging (Tocuyo bag (TB), Double-bilaminate foil and aluminuim bag (DFAB), Ecotac vacuum bag (EV), Pressed cardboard box (PCB), Double-laminated bag without valve, with opening and zipper (DBOZ), Double-laminated bag with degassing valve and zipper (DBDVZ) and Triple-laminated bag with degassing valve and zipper (TBDVZ)). The estimation of shelf life was conducted by means of cup scores provided by six coffee tasters for coffee stored at 40, 50, and 60 °C. The Arrhenius equation was employed to obtain accelerated models for predicting shelf life. It was determined that green coffee beans are most effectively preserved in DBOZ, maintaining their freshness for a period of up to 55.13 days. The second-best option was EV, which has a shelf life of up to 35.21 days. The sole packaging alternative that was subjected to testing for roasted coffee beans was found to allow for their preservation for a period of up to 32 days. However, for roasted and ground coffee, of the four alternatives evaluated, the TBDVZ proved to be the optimal alternative, at 12.18 days. However, the other alternatives (DBOZ and DBDVZ) allow for very similar storage times, at 11.99 and 11.48 days, respectively. PCB does not appear to be a viable packaging alternative for roasted and ground coffee (7.85 days). Finally, we found that coffee stored in DFAB and aluminum bags at 20 °C has been shown to retain its quality for up to 250 days. Furthermore, if the temperature is reduced to 10 °C, the coffee’s shelf life is extended to more than 600 days. The insights derived from this research are of significant value to industry stakeholders, consumers, and developers of specialty coffee packaging. Full article

Lakes, as key freshwater reserves and ecosystem cores, supply human water, regulate climate, sustain biodiversity, and are vital for global ecological balance and human sustainability. Lake Chaohu, as a crucial ecological barrier in the middle and lower reaches of the Yangtze River, faces significant environmental challenges to regional sustainable development due to water quality deterioration and consequent eutrophication issues. To address the limitations of conventional monitoring techniques, including insufficient spatiotemporal coverage and high operational costs in lake water quality assessment, this study proposes an enhanced Informer model optimized by the Whale Optimization Algorithm (WOA) for predictive analysis of concentration trends of key water quality parameters—dissolved oxygen (DO), permanganate index (CODMn), total phosphorus (TP), and total nitrogen (TN)—across multiple time horizons (4 h, 12 h, 24 h, 48 h, and 72 h). The results demonstrate that the WOA-optimized Informer model (WOA-Informer) significantly improves long-term water quality prediction performance. Comparative evaluation shows that the WOA-Informer model achieves average reductions of 9.45%, 8.76%, 7.79%, 8.54%, and 11.80% in RMSE metrics for 4 h, 12 h, 24 h, 48 h, and 72 h prediction windows, respectively, along with average improvements of 3.80%, 5.99%, 11.23%, 17.37%, and 23.26% in R² values. The performance advantages become increasingly pronounced with extended prediction durations, conclusively validating the model’s superior capability in mitigating error accumulation effects and enhancing long-term prediction stability. Spatial visualization through Kriging interpolation confirms strong consistency between predicted and measured values for all parameters (DO, CODMn, TP, and TN) across all time horizons, both in concentration levels and spatial distribution patterns, thereby verifying the accuracy and reliability of the WOA-Informer model. This study successfully enhances water quality prediction precision through model optimization, providing robust technical support for water environment management and decision-making processes. Full article

Reclaimed coastal areas are highly susceptible to uneven subsidence caused by the consolidation of soft marine deposits, which can induce differential settlement, structural deterioration, and systemic risks to urban infrastructure. Further, engineering activities, such as construction and loadings, exacerbate subsidence, impacting infrastructure stability. Therefore, monitoring the integrity and vulnerability of linear urban infrastructure after construction on reclaimed land is critical for understanding settlement dynamics, ensuring safe and reliable operation and minimizing cascading hazards. Subsequently, in the present study, to monitor deformation of the linear infrastructure constructed over decades-old reclaimed land in Mokpo city, South Korea (where 70% of urban and port infrastructure is built on reclaimed land), we analyzed 79 Sentinel-1A SLC ascending-orbit datasets (2017–2023) using the Persistent Scatterer Interferometry (PSInSAR) technique to quantify vertical land motion (VLM). Results reveal settlement rates ranging from −12.36 to 4.44 mm/year, with an average of −1.50 mm/year across 1869 persistent scatterers located along major roads and railways. To interpret the underlying causes of this deformation, Casagrande plasticity analysis of subsurface materials revealed that deep marine clays beneath the reclaimed zones have low permeability and high compressibility, leading to slow pore-pressure dissipation and prolonged consolidation under sustained loading. This geotechnical behavior accounts for the persistent and spatially variable subsidence observed through PSInSAR. Spatial pattern analysis using Anselin Local Moran’s I further identified statistically significant clusters and outliers of VLM, delineating critical infrastructure segments where concentrated settlement poses heightened risks to transportation stability. A hyperbolic settlement model was also applied to anticipate nonlinear consolidation trends at vulnerable sites, predicting persistent subsidence through 2030. Proxy-based validation, integrating long-term groundwater variations, lithostratigraphy, effective shear-wave velocity (V_s³⁰), and geomorphological conditions, exhibited the reliability of the InSAR-derived deformation fields. The findings highlight that Mokpo’s decades-old reclamation fills remain geotechnically unstable, highlighting the urgent need for proactive monitoring, targeted soil improvement, structural reinforcement, and integrated InSAR-GNSS monitoring frameworks to ensure the structural integrity of road and railway infrastructure and to support sustainable urban development in reclaimed coastal cities worldwide. Full article

Water loss during storage is a major cause of postharvest quality deterioration in cucumber, yet existing methods to monitor hydration are often destructive or require expensive instrumentation. We developed a low-cost, non-destructive approach for estimating fruit relative water content (RWC) using visible-light color imaging combined with an ensemble machine-learning model (Random Forest). A total of 1200 fruits were greenhouse-grown, harvested at market maturity, and equally divided between optimal and ambient storage temperature (10 and 25 °C, respectively). Digital images were acquired at harvest and at 7 d intervals during storage, and color parameters from four standard color systems (RGB, CMYK, CIELAB, HSV) were extracted separately for the neck, mid, and blossom regions as well as for the whole fruit. During storage, fruit RWC decreased from 100% (fully hydrated condition) to 15.3%, providing a broad dynamic range for assessing color–hydration relationships. Among the 16 color features evaluated, the mean cyan component (μC) of the CMYK space showed the strongest relationship with measured RWC (R² up to 0.70 for whole-fruit averages), reflecting the cyan region’s heightened sensitivity to dehydration-induced changes in pigments, cuticle properties and surface scattering. The Random Forest regression model trained on these features achieved a higher predictive accuracy (R² = 0.89). Predictive accuracy was also consistently higher when μC was calculated over the entire fruit surface rather than for individual anatomical regions, indicating that whole-fruit color information provides a more robust hydration signal than region-specific measurements. Our findings demonstrate that simple visible-range imaging coupled with ensemble learning can provide a cost-effective, non-invasive tool for monitoring postharvest hydration of cucumber fruit, with direct applications in quality control, shelf-life prediction and waste reduction across the fresh-produce supply chain. Full article

Urban water infrastructure faces increasing deterioration, necessitating accurate, cost-effective condition assessment. Traditional inspection techniques are intrusive and inefficient, creating demand for scalable machine learning (ML) solutions. This study develops a hybrid ML meta-model to predict underground pipe conditions using a comprehensive dataset of 11,544 records. The objective is to enhance multi-class classification performance while preserving interpretability. A stacked hybrid architecture was employed, integrating Random Forest, LightGBM, and CatBoost models. Following data preprocessing, feature engineering, and correlation analysis, the neural network-based stacking meta-model achieves 96.67% accuracy, surpassing individual base learners while delivering enhanced robustness through model diversity, improved probability calibration, and consistent performance on challenging intermediate condition classes, which are essential for condition prioritization. Age emerged as the most influential feature, followed by length, material type, and diameter. ROC-AUC scores ranged from 0.894 to 0.998 across all models and classes, confirming high discriminative capability. This work demonstrates hybrid architectures for infrastructure diagnostics. Full article

Pavement performance prediction serves as a core basis for maintenance decision-making. Although numerous studies have been conducted, most focus on road segments and aggregate indicators such as IRI and PCI, with limited attention to the daily deterioration of individual distresses. Subject to the combined influence of multiple factors, pavement distress deterioration exhibits pronounced nonlinear and time-lag characteristics, making distress-level predictions prone to disturbances and highly uncertain. To address this challenge, this study investigates the distress-level deterioration of three representative distresses—transverse cracks, alligator cracks, and potholes—with causal analysis and uncertainty quantification. Based on two years of high-frequency road inspection data, a continuous tracking dataset comprising 164 distress sites and 9038 records was established using a three-step matching algorithm. Convergent cross mapping was applied to quantify the causal strength and lag days of environmental factors, which were subsequently embedded into an encoder–decoder framework to construct a BayesLSTM model. Monte Carlo Dropout was employed to approximate Bayesian inference, enabling probabilistic characterization of predictive uncertainty and the construction of prediction intervals. Results indicate that integrating causal and time-lag characteristics improves the model’s capacity to identify key drivers and anticipate deterioration inflection points. The proposed BayesLSTM achieved high predictive accuracy across all three distress types, with a prediction interval coverage of 100%, thereby enhancing the reliability of prediction by providing both deterministic results and interval estimates. These findings facilitate the identification of high-risk distresses and their underlying mechanisms, offering support for rational allocation of maintenance resources. Full article

The acid number is widely recognized as one of the most essential and frequently used indicators for evaluating the degradation state of lubricants. Changes in acid number serve as a direct reflection of the oil’s oxidative deterioration. Conventional prediction methods, however, often neglect the coupling effects among multiple physical factors and lack sufficient dynamic adaptability. Therefore, this study proposes a method for predicting the variation trend of lubricating oil acid number by integrating an Adaptive Neuro-Fuzzy Inference System (ANFIS) with Subtractive Clustering (SC), establishing an SC-ANFIS-based predictive model. The subtractive clustering technique automatically determines the number of fuzzy rules and initial parameters directly from the dataset, thereby eliminating redundant rules and simplifying the model architecture. The SC-ANFIS model further optimizes the parameters of the fuzzy inference system through the self-learning ability of neural networks. Lubricant aging tests were conducted using a laboratory oxidation stability tester. Regular sampling was carried out to acquire comprehensive lubricant performance degradation data. The input variables of the model include the current acid number, carbonyl peak intensity, metal element concentrations (Fe and Cu), viscosity, and water content of the lubricating oil, while the output variable corresponds to the rate of change in the acid number of the lubricating oil relative to the previous time step. The proposed model demonstrates effective prediction of the lubricating oil acid number variation trend. Posterior difference tests confirmed its high predictive accuracy, with all three evaluation metrics—RMSE, MAE, and MAPE—outperforming those of the BP model. Full article

(1) Background: Chronic ankle instability (CAI), a common outcome of ankle sprains, involves recurrent sprains, balance deficits, and gait impairments linked to both peripheral and central neuromuscular dysfunction. Dual-task (DT) demands further aggravate postural control, especially during stair descent, a major source of fall-related injuries. Yet the biomechanical mechanisms of stair-to-ground transition in CAI under dual-task conditions remain poorly understood. (2) Methods: Sixty individuals with CAI and age- and sex-matched controls performed stair-to-ground transitions under single- and dual-task conditions. Spatiotemporal gait parameters, center of pressure (COP) metrics, ankle inversion angle, and relative joint work contributions (Ankle%, Knee%, Hip%) were obtained using 3D motion capture, a force plate, and musculoskeletal modeling. Correlation and regression analyses assessed the relationships between ankle contributions, postural stability, and proximal joint compensations. (3) Results: Compared with the controls, the CAI group demonstrated marked control deficits during the single task (ST), characterized by reduced gait speed, increased step width, elevated mediolateral COP root mean square (COP-ml RMS), and abnormal ankle inversion and joint kinematics; these impairments were exacerbated under DT conditions. Individuals with CAI exhibited a significantly reduced ankle plantarflexion moment and energy contribution (Ankle%), accompanied by compensatory increases in knee and hip contributions. Regression analyses indicated that Ankle% significantly predicted COP-ml RMS and gait speed (GS), highlighting the pivotal role of ankle function in maintaining dynamic stability. Furthermore, CAI participants adopted a “posture-first” strategy under DT, with concurrent deterioration in gait and cognitive performance, reflecting strong reliance on attentional resources. (4) Conclusions: CAI involves global control deficits, including distal insufficiency, proximal compensation, and an inefficient energy distribution, which intensify under dual-task conditions. As the ankle is central to lower-limb kinetics, its dysfunction induces widespread instability. Rehabilitation should therefore target coordinated lower-limb training and progressive dual-task integration to improve motor control and dynamic stability. Full article

Turnouts are critical components of railway infrastructure, ensuring operational flexibility but also representing a significant share of track maintenance costs. The frog, as the most vulnerable part of a turnout, is subject to severe wear and degradation, requiring frequent inspection and maintenance. Traditional manual inspection methods are costly, labour-intensive, and susceptible to subjectivity. This study explores a data-driven approach to condition monitoring of fixed-turnout frogs using standard track recording car measurements. By leveraging over 20 years of longitudinal level and rail surface signal data from the Austrian track-recording measurement car, we assess the feasibility of using existing measurement data for predictive maintenance. Six complementary approaches are proposed to evaluate frog condition, including track geometry assessment, ballast condition analysis, rail surface irregularity detection, and axle box acceleration-based monitoring. Results indicate that data-driven monitoring enhances maintenance decision-making by identifying deterioration trends, reducing reliance on manual inspections, and enabling predictive interventions. The integration of standardised measurement data with advanced analytical models offers a cost-effective and scalable solution for turnout maintenance. Full article

In this study, we explored the evolution of mechanical properties in hydroxyl-terminated polybutadiene (HTPB) propellants under fatigue loading by performing fatigue tests with varying maximum stresses and cycle numbers, followed by uniaxial tensile tests on post-fatigue specimens. Residual elongation was used as a key parameter to characterize mechanical behavior, while scanning electron microscopy (SEM) provided insights into the mesostructural morphological changes that occur under different loading conditions, revealing the mechanisms responsible for variations in mechanical properties. The results show that, as the number of loading cycles increases, residual elongation decreases, with three distinct phases of decline—slow change, gradual decline, and rapid deterioration—depending on the stress levels. SEM analysis identified damage mechanisms such as “dewetting” and particle fragmentation at the mesostructural level, which compromise the material’s structural integrity, leading to reduced residual elongation. A novel aspect of this study is the application of Williams–Landel–Ferry (WLF) theory to construct a master curve describing residual elongation decay. This approach enabled the development of a generalized model to predict the material’s degradation under fatigue loading, with experimental validation of the fitted evolution model, offering a new and effective method for assessing the long-term performance of HTPB propellants. Full article

Background/Objective: The growing volume and complexity of cases presented to emergency departments underline the urgent need for effective clinical-risk-management strategies. Increasing demands for quality and safety in healthcare highlight the importance of predictive tools in supporting timely and informed clinical decision-making. This study aims to evaluate the performance and usefulness of predictive models for managing the clinical risk of people who visit the emergency department. Methods: A systematic review was conducted, including primary observational studies involving people aged 18 and over, who were not pregnant, and who had visited the emergency department; the intervention was clinical-risk management in emergency departments; the comparison was of early warning scores; and the outcomes were predictive models. Searches were performed on 10 November 2024 across eight electronic databases without date restrictions, and studies published in English, Portuguese, and Spanish were included in this study. Risk of bias was assessed using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies as well as the Prediction Model Risk-of-Bias Assessment Tool. The results were synthesized narratively and are summarized in a table. Results: Four studies were included, each including between 4388 and 448,972 participants. The predictive models identified included the Older Persons' Emergency Risk Assessment score; a new situation awareness model; machine learning and deep learning models; and the Vital-Sign Scoring system. The main outcomes evaluated were in-hospital mortality and clinical deterioration. Conclusions: Despite the limited number of studies, our results indicate that predictive models have potential for managing the clinical risk of emergency department patients, with the risk-of-bias study indicating low concern. We conclude that integrating predictive models with artificial intelligence can improve clinical decision-making and patient safety. Full article

Due to its ecological functions, huge economic benefits, and excellent nutritional and physiological activities, Lentinula edodes is a very popular edible fungus in Asia, especially in China. Changes in the distribution and population of wild L. edodes play an important role in conservation, variety improvements, and breeding. This investigation detected wild L. edodes in 28 provinces and municipalities in China, encompassing approximately 300 regions and natural reserves. MaxEnt analysis of 53 effective distribution locations indicated that host plants, Bio19 (precipitation in the coldest quarter), Bio10 (mean temperature of the warmest quarter), and Bio17 (precipitation in the driest quarter) made the most critical contributions to this model. The areas of suitable and highly suitable habitats were 55.386 × 10⁴ km² and 88.493 × 10⁴ km², respectively. Under four climate change scenarios, the L. edodes distribution was predicted to decrease and the suitable habitat area shifted to the north and west of China. The decrease in highly suitable habitat area ranged from 21.155% in the 2070s under the ssp1-2.6 scenario to 90.522% in the 2050s under the ssp3-7.5 scenario. This sharp reduction in habitat areas suggests that we should take measures to prevent the deterioration of the environment and climate and thus to ensure the survival of L. edodes. Full article