Search Results (26,342)

Aiming at the nonlinear and non-stationary characteristics of wind speed series, this study proposes a hybrid forecasting framework that integrates Variational Mode Decomposition (VMD), Sparrow Search Algorithm (SSA), and Long Short-Term Memory (LSTM) networks. First, VMD is employed to adaptively decompose the original wind speed series into multiple Intrinsic Mode Functions (IMFs) with distinct frequency features, thereby reducing the non-stationarity of the original sequence. Second, SSA is utilized to adaptively optimize key parameters of the LSTM network, including the number of hidden units, learning rate, and dropout rate, to enhance the model’s capability in capturing complex temporal patterns. Finally, the predictions from all modal components are integrated to generate the final wind speed forecast. Experimental results based on 10-min resolution measured data from a coastal wind farm in southeastern China in June 2020 show that the model achieves a Root Mean Square Error (RMSE) of 0.208, a Mean Absolute Error (MAE) of 0.161, and a Mean Absolute Percentage Error (MAPE) of 3.284% on the test set, with its comprehensive performance significantly surpassing benchmark models such as LSTM, VMD-LSTM, MLP, XGBoost, and ARIMA. The limitations of this study mainly include the use of only one month of data for validation, the lack of segmented performance analysis across different wind speed regimes, and a fixed prediction horizon of 10 min. The results indicate that the proposed hybrid forecasting framework provides an effective approach with practical engineering potential for ultra-short-term wind power prediction. Full article

Programmable mechanical metamaterials enable precise regulation of mechanical responses through geometric design, ushering in transformative paradigms for transformable structures. To systematically map the knowledge landscape and development trends in this field, this study employs knowledge mapping methods to analyze the current research status, core hotspots, and future directions of programmable mechanical metamaterials. During the research process, we expanded keywords using the litsearchr tool to optimize the retrieval strategy. Bibliometric tools, including CiteSpace 6.3.R3 and bibliometrix, were utilized to conduct multidimensional analyses on 2017 original papers related to mechanical metamaterials in transformable architecture from 2015 to 2025. These analyses encompass co-word analysis, co-citation clustering, and structural variation analysis. Key aspects include (1) identifying core journals and their attributes to clarify interdisciplinary dynamics, (2) mapping research themes and evolutionary trends through keyword analysis and clustering, and (3) pinpointing research hotspots and future directions based on citation networks and clustering results. The results reveal significant interdisciplinary characteristics, with core knowledge emerging from the intersection of materials science, mechanics, and civil engineering. Mathematical system theory provides a cross-scale modeling foundation for metamaterial microstructure design. The field is evolving from static structural design toward environment-adaptive intelligent systems. Future efforts should prioritize multi-physics collaborative regulation, engineering integration, and technical chain refinement. These findings offer a theoretical reference for the innovative development of transformable architecture. Full article

Wearable sensors generate continuous physiological and behavioral data at a population scale, yet wellness prediction remains limited by noisy measurements, irregular sampling, and subjective outcomes. We introduce HybridSense, a unified framework that integrates raw wearable signals and their statistical descriptors with large language model–based reasoning to produce accurate and interpretable estimates of stress, fatigue, readiness, and sleep quality. Using the PMData dataset, minute-level heart rate and activity logs are transformed into daily statistical features, whose relevance is ranked using a Random Forest model. These features, together with short waveform segments, are embedded into structured prompts and evaluated across seven prompting strategies using three large language model families: OpenAI 4o-mini, Gemini 2.0 Flash, and DeepSeek Chat. Bootstrap analyses demonstrate robust, task-dependent performance. Zero-shot prompting performs best for fatigue and stress, while few-shot prompting improves sleep-quality estimation. HybridSense further enhances readiness prediction by combining high-level descriptors with waveform context, and self-consistency and tree-of-thought prompting stabilize predictions for highly variable targets. All evaluated models exhibit low inference cost and practical latency. These results suggest that prompt-driven large language model reasoning, when paired with interpretable signal features, offers a scalable and transparent approach to wellness prediction from consumer wearable data. Full article

Background/Objectives: Chronic Kidney Disease (CKD) is a global public health burden with a rising prevalence driven by population aging. Existing prediction models, such as the Kidney Failure Risk Equation (KFRE), often lack generalizability across ethnicities and comprehensive systemic indicators. This study aimed to develop and validate a machine learning model for predicting CKD progression by integrating traditional risk factors with novel composite indicators reflecting systemic health. Methods: Data from the China Health and Retirement Longitudinal Study (CHARLS, n = 2500) was used for model training. External validation was performed using independent cohorts from the English Longitudinal Study of Ageing (ELSA, n = 1200) and the Health and Retirement Study (HRS, n = 1500). Multiple machine learning algorithms, including XGBoost, were employed. Feature engineering incorporated composite indicators such as the frailty index (FI), triglyceride–glucose (TyG) index, and aggregate index of systemic inflammation (AISI). Results: The XGBoost model achieved an area under the curve (AUC) of 0.892 in the training set and maintained robust performance in external validation (AUC 0.867 in ELSA, 0.871 in HRS), outperforming the KFRE (AUC 0.745). SHAP analysis identified the FI as the most influential predictor. Decision curve analysis confirmed the model’s clinical utility. Conclusions: This machine learning model demonstrates high accuracy and cross-ethnicity validity, offering a practical tool for early intervention and personalized CKD management. Future work should address limitations such as the retrospective design and expand validation to underrepresented regions. Full article

The maritime sector is under growing pressure to decarbonize, driving the adoption of alternative fuels such as methane, methanol, ammonia, and hydrogen. This study evaluates their thermal behavior and associated risks using Engineering Equation Solve software for heat transfer modeling and Areal Locations of Hazardous Atmospheres software for dispersion and explosion analysis in pipelines and storage scenarios. Results indicate that methane presents moderate and predictable risks, mainly from thermal effects in fires or Boiling Liquid Expanding Vapor Explosion events, with low toxicity. Methanol offers the safest operational profile, stable at ambient temperature and easily manageable, though it remains slightly flammable even when diluted. Ammonia shows the greatest toxic hazard, with impact distances reaching several kilometers even when emergency shutoff systems are active. Hydrogen, meanwhile, poses the most severe flammability and explosion risks, capable of autoignition and generating destructive overpressures. Thermal analysis highlights that cryogenic fuels require complex insulation systems, increasing storage costs, while methanol and gaseous hydrogen remain thermally stable but have lower energy density. The study concludes that methanol is the most practical transition fuel, when comparing thermal behavior and associated risks, while hydrogen and ammonia demand further technological and regulatory development. Proper insulation, ventilation, and automatic shutoff systems are essential to ensure safe decarbonization in maritime transport. Full article

Flood disasters are increasing worldwide due to climate change, posing growing risks to infrastructure and human life. Korea, where nearly 70% of annual rainfall occurs during the summer monsoon, is particularly vulnerable to extreme precipitation events intensified by El Niño and La Niña. This study investigates how terrain resolution influences flood simulation accuracy by comparing a 1 m LiDAR digital elevation model (DEM) with a DEM generated from a 1:5000 topographic map. Flood depth and velocity fields produced by the two DEMs show notable quantitative differences: for final flood depth, the 1:5000 DEM yields a mean absolute error of approximately 56.9 cm and an RMSE of 76.4 cm relative to LiDAR results, with substantial local over- and underestimations. Flow velocity and maximum velocity also show significant deviations, with RMSE values of 58.0 cm/s and 68.4 cm/s, respectively. Although the 1:5000 DEM captures the general inundation pattern, these discrepancies—particularly in narrow channels and urbanized floodplains—demonstrate that coarse-resolution terrain data cannot reliably reproduce hydrodynamic behavior. We conclude that while 1:5000 DEMs may be acceptable for reconnaissance-level hazard screening, high-resolution LiDAR DEMs are essential for accurate flood depth and velocity simulation, supporting their integration into engineering design, urban flood risk assessment, and disaster management frameworks. Full article

Pulse-like ground motions can cause severe damage to buried cast iron (CI) pipelines, which necessitates the selection of optimal seismic intensity measures (IMs) to estimate pipeline repair rates. Such a selection is essential for mitigating uncertainty in the seismic risk assessment of buried CI pipelines. For the first time, this study systematically screens the optimal scalar and vector IMs for buried cast iron pipelines with lead-caulked joints under pulse-like ground motions by a symmetrical evaluation based on the criteria of efficiency, sufficiency, and proficiency, providing a new method for reducing uncertainty in pipeline seismic risk assessment. We initiate the study by selecting 124 pulse-like ground motions from the NGA-West2 database and identifying 19 scalar and 171 vector IMs as potential candidates. A two-dimensional soil–pipe model is introduced, incorporating variability in the sealing capacity of lead-caulked joints along the axial direction. CI pipeline repair rates are calculated across various scaling factors and apparent wave velocities, yielding 1116 datasets pertinent to CI pipeline damage. The repair rate is adopted as the engineering demand parameter (EDP) to evaluate the efficiency, sufficiency, and proficiency of candidate IMs. Through comprehensive analysis, peak ground velocity (PGV) and the combination of PGV and the time interval between 5% and 75% of normalized Arias intensity ([PGV, Ds_5–75]) are determined as the optimal scalar- and vector-IMs, respectively, for assessing the repair rate of buried CI pipelines under pulse-like ground motions. Full article

The flow around circular cylinders is a classic problem in fluid mechanics with significant implications for offshore engineering. While extensive numerical and experimental research has focused on the subcritical and critical Reynolds regimes, the supercritical and postcritical regimes remain challenging and relatively unexplored, primarily due to the complex nature of turbulence and the high computational requirements. In this study, we perform three-dimensional detached eddy simulations using the finite volume method in OpenFOAM v1906, employing Menter’s k-$\omega$ SST turbulence model, to systematically investigate the flow past an infinitely long smooth cylinder from the subcritical through the postcritical regimes. The numerical setup ensures accurate near-wall resolution and reliable representation of unsteady flow features. We present a detailed analysis of vortex shedding patterns, wake evolution, and statistical properties of lift and drag coefficients for selected Reynolds numbers representative of each regime. The simulation results are benchmarked against experimental data from the literature, demonstrating good agreement for Strouhal number and mean drag. Special emphasis is placed on the evolution of wake topology and force coefficients as the flow transitions from laminar to fully turbulent conditions. The findings contribute to the limited numerical literature on flow around circular cylinders across subcritical, critical, supercritical, and postcritical Reynolds number regimes, providing insights that are fundamentally relevant to the broader scope of understanding vortex shedding phenomena. Full article

With the increasingly stringent mandatory emission regulations for engines and the continuous growth of energy consumption, reducing energy consumption and emission pollution has become an inevitable choice for engine development. Against this backdrop, biodiesel and boot-shaped injection rates have attracted widespread attention. However, research results on the combination of boot-shaped injection and biodiesel applied to engines have not yet been reported. In order to provide direction for the optimal matching of the combustion system parameters of biodiesel engines under saddle-shaped injection conditions, this paper achieves boot-shaped injection using a dual solenoid valve control strategy for ultra-high-pressure fuel injection devices, establishes a simulation model of biodiesel engines under saddle-shaped injection conditions using software and validates the model based on experiments. Subsequently, the model is used to study the influence of nozzle numbers, swirl ratio and bore-to-stroke ratio on the performance of biodiesel engines under saddle-shaped injection conditions. The results show that under saddle-shaped injection conditions, appropriately increasing the nozzle hole can refine the fuel spray, which is beneficial for fuel–air mixing and combustion in the cylinder. However, too many nozzle holes can lead to interference between adjacent fuel sprays. When the swirl ratio is large, air flow accelerates, and the oxygen concentration in the cylinder increases, which can effectively control soot formation. When the bore-to-stroke ratio is large, the fuel spray is farther away from the combustion chamber side wall, facilitating sufficient contact between fuel and air, resulting in better fuel–air mixing and effectively reducing soot formation. However, the cylinder temperature also increases, leading to higher NOx formation. Full article

Atrial fibrillation (AF) is the most common sustained heart rhythm disorder. It is estimated that AF affects over 52 million people worldwide, with its prevalence expected to double in the next four decades. AF significantly increases the risk of stroke and heart failure, contributing to 340,000 excess deaths annually. Beyond these life-threatening complications, AF results in limitations in physical, emotional, and social well-being causing significant reductions in quality of life and resulting in 8.4 million disability-adjusted life-years per year, highlighting the wide-ranging impact of AF on public health. Moreover, AF is increasingly recognized for its association with cognitive decline and dementia. AF is a chronic and progressive disease characterized by rapid and erratic electrical activity in the atria, often in association with structural changes in the heart tissue. AF is often initiated by triggered activity, often from ectopic foci in the pulmonary veins. These triggered impulses may initiate AF via: (1) sustained rapid firing with secondary disorganization into fibrillatory waves, or (2) by triggering micro re-entrant circuits around the pulmonary venous-LA junction and within the atrial body. In each instance, AF perpetuation necessitates the presence of a vulnerable atrial substrate, which perpetuates and stabilizes re-entrant circuits through a combination of slowed and heterogeneous conduction, as well as functional conduction abnormalities (e.g., fibrosis disrupting tissue integrity, and abnormalities in the intercalated disks disrupting effective cell-to-cell coupling). The re-entry wavelength, determined by conduction velocity and refractory period, is shortened by slowed conduction, favoring AF maintenance. One major factor contributing to these changes is the disruption of the extracellular matrix (ECM), which is induced by atrial fibrosis. Fibrosis-driven disruption of the ECM, especially in the heart and blood vessels, is commonly caused by conditions such as aging, hypertension, diabetes, smoking, and chronic inflammatory or autoimmune diseases. These factors lead to excessive collagen and protein deposition by activated fibroblasts (i.e., myofibroblasts), resulting in increased tissue stiffness, maladaptive remodeling, and impaired organ function. Fibrosis typically occurs when cardiac fibroblasts are activated to myofibroblasts, resulting in the deposition of excessive collagen and other proteins. This change in ECM interferes with the normal electrical function of the heart by creating irregular, fibrotic regions. AF and atrial fibrosis have a reciprocal relationship: AF promotes fibrosis through fibroblast activation and extracellular matrix buildup, while atrial fibrosis can sustain and perpetuate AF, contributing to higher rates of AF recurrence after treatments such as catheter ablation or cardioversion. Full article

Over the past decades, global air traffic has increased continuously, with passenger kilometers roughly doubling every fifteen to twenty years, and this trend is estimated to continue, with some adjustments due to COVID-19 impact. In response to the resulting environmental challenges, the European initiatives Flightpath 2050 and Clean Sky serve as central drivers of technological development aimed at achieving ambitious sustainability goals. Flightpath 2050 targets, relative to a reference engine from the year 2000, include a 75% reduction in CO₂ emissions per passenger kilometer, a 90% reduction in NO_x emissions, and a 65% reduction in noise emissions. These objectives highlight the urgent need for emission reduction strategies across all manufacturing domains, including turbine component production. This study evaluates the environmental impacts of the preturning and roughing operations employed in turbine disk production. The analysis focuses on these specific processes rather than the entire product, as the approach of process-level Life Cycle Assessments (LCA) are more universally applicable across different products, and their systematic combination can ultimately form a comprehensive product-level LCA. Operational data, such as energy usage, cooling lubricants, and compressed air, were gathered and processed from the equipment involved in manufacturing. The collected data were analyzed and modeled in Spheras life cycle assessment software LCA for Experts (version 10.9.0.20) to quantify the environmental effects of each process. The findings of the current research emphasize notable patterns of resource utilization and their respective environmental impacts. Furthermore, the Industrial Digital Twin Association (IDTA) Product Carbon Footprint (PCF) template was utilized to present the findings in a standardized manner, enabling effective data transfer between stakeholders. The results demonstrate the critical need to leverage machine data for sustainability analysis, providing inputs for industry practice enhancement and progress toward better environmental performance. Full article

The study is dedicated to the accuracy of engineering analyses of virtual prototypes. In particular, it aims to quantify the importance of material models and data consistent with physical tests. The focus is set on the stress–strain material characteristic that is the basis for correct simulation results, and the deviations of its parameters—elasticity module and yield stress—that are assessed. This is performed in three main steps: laboratory measurement of the material properties of a sample material (aluminum alloy), followed by an engineering analysis of a component produced from the same material, using the determined mechanical characteristics. The third step involves physical tests used to validate the virtual prototyping results by comparing them with the physical test results. The material properties used in the virtual prototype are subjected to a sensitivity analysis to determine their influence on the design’s elastic and plastic behavior. The main conclusions of the study are the importance of these material characteristics for achieving an adequate result. A general recommendation is formed that shows the importance of laboratory testing of material properties before virtual prototyping to avoid any influence of factors as production technology or geometry (specimen thickness). Full article

Background: Poly(ADP-ribose) polymerase inhibitors (PARPi) have transformed cancer therapy for patients harbouring homologous recombination repair (HRR) deficiencies, notably BRCA1/2 mutations. However, resistance to PARPi remains a clinical challenge, with restoration of BRCA1 function via hypomorphic variants representing an understudied scenario. Methods: Here, we engineered a doxycycline-inducible BRCA1 expression system in the BRCA1-mutant, triple-negative breast cancer cell line MDAMB436, permitting controlled analysis of functionally distinct BRCA1 hypomorphs in vitro and in vivo. Results: Among multiple BRCA1 variants generated—including RING, coiled-coil, and BRCT domain mutants—only overexpression of the ∆exon11 hypomorph robustly conferred resistance to olaparib and carboplatin, with drug sensitivity correlating to ∆exon11 expression levels. While ∆exon11 BRCA1 mediated HRR restoration, its efficiency was consistently lower than full-length BRCA1, as measured by RAD51 foci formation and interaction with repair partners such as PALB2. In vivo, tumours expressing Δexon11 BRCA1 exhibited only partial resistance to olaparib compared to those expressing full-length BRCA1. Importantly, the combination of olaparib and the ATR inhibitor, ceralasertib, overcame ∆exon11-mediated resistance, impairing RAD51 foci formation in ∆exon11-expressing cells. Conclusions: Our findings identify a dose-dependent, hypomorphic HRR restoration by ∆exon11 BRCA1, help explain the variable resistance observed in BRCA1-mutant pre-clinical models expressing this hypomorph, and propose ATR inhibition in combination with PARPi as a clinical strategy to counteract therapeutic resistance mediated by ∆exon11 BRCA1 hypomorphs. Full article

High-voltage equipment imposes increasingly stringent demands on polymeric insulating materials, particularly in terms of dielectric strength, space charge suppression, thermo-electrical stability, and interfacial reliability. Conventional polymers are prone to critical failure modes under high electric fields, including electrical treeing, partial discharge, interfacial degradation, and thermo-oxidative aging. This review systematically summarizes recent advances in polymer modification strategies specifically designed for high-voltage applications, covering nanofiller reinforcement, plasma surface engineering, and the development of self-healing insulating polymers. Multi-scale structural control and interface engineering, aligned with the specific requirements of high-voltage environments, have emerged as pivotal approaches to enhance insulation performance. Moreover, the integration of artificial intelligence-driven materials design, digital characterization, and application-oriented modeling holds significant promise for accelerating the development of next-generation high-voltage polymeric systems, thereby offering robust materials solutions for the reliable long-term operation of high-voltage equipment. Full article

The ITISE 2025 (11th International conference on Time Series and Forecasting) seeks to provide a discussion forum for scientists, engineers, educators and students about the latest ideas and realizations in the foundations, theory, models and applications for interdisciplinary and multidisciplinary research encompassing disciplines of computer science, mathematics, statistics, forecaster, econometric, etc [...] Full article