Search Results (448)

As a key part of the new power system, hydropower units (HPUs) are capable of maintaining the stability of system frequency through the flexible conversion of operating conditions. Fixed control parameters are generally adopted by existing HPU governors, which cannot meet the requirements of variable operating conditions, and the flexibility of hydropower regulation is thus restricted. Therefore, an adaptive optimal control strategy for units in frequency regulation mode is proposed for a large hydropower station in this paper. Firstly, a segmented linearized mathematical model for HPU frequency regulation is established. On this basis, objective functions under frequency and load perturbation are constructed. Control parameters under each operating condition are optimized via an improved multi-objective particle swarm optimization based on the objective functions. The nonlinear relationship between optimal control parameters and operating conditions is fitted to obtain the adaptive adjustment strategy. Comparative verification with the fixed-parameter strategy shows that the proposed strategy improves comprehensive performance (frequency adjustment and recovery time) under 48 operating conditions. The improvement rate exceeds 50% under large opening conditions, with an overall average of 51.01%, fully proving its superiority. Full article

We study the decomposition of large-scale Quadratic Unconstrained Binary Optimization Problems (QUBO) formulations for quantum and quantum-inspired annealers and propose two decomposition mechanisms. The first is one-way-one-hot (1W1H), which replaces a linear inequality with exactly one indicator bank and naturally decomposes the model into many small, parallel subproblems. The second is slack variable range search (SVRS), which introduces a binary-encoded slack and scans restricted windows to balance the number of subproblems and the per-subproblem variable count. Evaluation results using the P08 knapsack problem instance on the Compal Graphic Processing Unit Annealer (CGA) show that SVRS provides a favorable scalability–quality trade-off, while 1W1H remains attractive when the admissible range is small to medium and massive parallelism is available. These results motivate integrating both mechanisms into the National Central University Annealer (NCUA). Full article

Background/Objectives: Platelet-rich plasma (PRP)–based regimens are widely used in non-scarring alopecia, yet objective response is variable and clinic-ready predictors are lacking. We evaluated short-term trichoscopic outcomes in routine practice and tested whether baseline complete blood count–derived inflammatory status, quantified by the neutrophil-to-lymphocyte ratio (NLR), can stratify response under PRP-based therapy. Methods: We performed an ambispective observational cohort study (October 2024–October 2025) in an outpatient dermatology practice. The final analytic cohort included 129 patients allocated to four treatment groups: PRP alone (n = 54), PRP combined with microneedling-assisted Purasomes Hair & Scalp Complex (HCS50+, Dermoaroma; exosome-containing) (n = 33), PRP combined with microneedling-assisted Mesoaroma Hair Cocktail (scalp formulation; nutrient complex) (n = 24), and a nutrient complex alone (n = 18). Trichoscopy (FotoFinder ATBM; FotoFinder Systems GmbH, Bad Birnbach, Germany) was obtained at baseline (T1) and first follow-up (T2). Density response was defined as a ≥10% increase in total hair density and hair-cycle response as an anagen fraction increase ≥5 percentage points. Predictive analyses were prespecified and restricted to PRP-containing regimens, using logistic regression and a multilayer perceptron with repeated cross-validation for internal validation. Results: Across the full cohort (n = 129), total hair density and hair-cycle parameters improved from T1 to T2. In the PRP-containing subgroup (n = 111), baseline NLR strongly discriminated density responders (AUC 0.85, bootstrap 95% CI 0.77–0.91). In multivariable models, NLR remained independently associated with density response (OR 0.31 per 1-unit increase, 95% CI 0.20–0.48). Conclusions: In this cohort, baseline NLR was associated with discrimination of early trichoscopic response in PRP-based treatment of non-scarring alopecia. Using the Youden-derived cut-off (NLR = 2.202), patients with NLR > 2.202 had a higher risk of density non-response (72.1% vs. 4.7%), corresponding to a 15.49-fold increased failure risk in this cohort. These findings are exploratory and hypothesis-generating, and external validation and calibration are required before any routine clinical or decision-support use. Full article

Background: Cardiometabolic diseases are a leading cause of premature mortality globally, yet longitudinal national mortality patterns remain insufficiently characterised in Gulf Cooperation Council settings. This study examines national trends in cardiometabolic mortality alongside health system financing, capacity, and utilization in Kuwait between 2010 and 2022. Methods: A national ecological time-series analysis used Ministry of Health administrative data covering mortality, cardiac care unit (CCU) capacity and discharges, cardiovascular procedural volumes, and MOH expenditure. Cause-specific outcomes included circulatory disease, ischaemic heart disease (IHD), cerebrovascular disease, hypertensive disease, and diabetes mellitus. Ordinary least squares regression estimated annual trends; pre-COVID restricted models (2010–2019) separated secular from pandemic-period effects. Results: All-cause deaths rose significantly from 5448 (2010) to 8041 (2022; β = +373.5/year; p = 0.001), peaking at 10,938 in 2021. Circulatory disease mortality rates increased over the full series but not pre-COVID, indicating pandemic-era acceleration. IHD death counts rose significantly in both models (β = +68.4 and +67.0/year; p < 0.01); IHD rates showed no significant trend, implicating demographic growth. Diabetes demonstrated the strongest signal: significant increases in death counts (β = +36.5/year; p < 0.001) and mortality rates (β = +0.689/100,000/year; p = 0.002), rising progressively across all time blocks. Hypertensive mortality declined significantly (β = −0.113/year; p = 0.002). MOH expenditure, CCU capacity, and CCU discharges increased significantly, demonstrating sustained structural expansion of cardiovascular services. Conclusions: Rising cardiometabolic mortality—driven prominently by diabetes—occurred alongside sustained health system expansion in Kuwait, indicating that tertiary capacity growth alone is insufficient to offset underlying epidemiological pressures. These findings underscore the urgency of strengthening upstream cardiometabolic prevention, integrated diabetes surveillance, and long-term metabolic risk control as central pillars of sustainable NCD policy. Full article

Both frequentist and Bayesian approaches are presented in this paper for a multiple step-stress accelerated life test. It is assumed that the lifetime distributions of experimental units under each stress level conform to a two-parameter exponentiated Rayleigh distribution. Additionally, the distributions corresponding to each stress level are related via the cumulative exposure model. In a step-stress experiment, with the applied stress level on the rise, the failure process of experimental units is accelerated, which gives rise to a reduction in their expected lifetime. This order restriction is explicitly incorporated into the statistical inference. Under the classical framework, via reparameterization, the order-restricted maximum likelihood estimates (MLEs) of unknown parameters are provided, and asymptotic confidence intervals are constructed based on the observed Fisher information matrix. In the Bayesian framework, we conduct the Bayesian analyses and obtain credible intervals using the importance sampling techniques. Extensive simulation studies are conducted, and a real dataset is analyzed for illustrative purposes. Full article

Background/Objectives: Breast milk is recommended as primary enteral nutrition for preterm infants, but the quantitative association between early breast milk feeding proportion and short-term growth remains unclear. We examined the relationship between early breast milk feeding proportion and discharge weight z-score in preterm infants. Methods: This single-center retrospective cohort study included preterm infants admitted to a neonatal intensive care unit between January 2024 and December 2025. Early breast milk feeding proportion was defined as the percentage of breast milk intake among total enteral nutrition during the first 14 days of life. The primary outcome was discharge weight z-score based on the Fenton growth reference. Linear regression, restricted cubic spline analysis, and exploratory mediation analysis were performed. Results: Among 1174 preterm infants, a higher early breast milk feeding proportion was independently associated with a higher discharge weight z-score in the primary multivariable model adjusted for gestational age, sex, and initial mechanical ventilation. A 10% increase in breast milk feeding proportion was associated with an increase of 0.18 in discharge weight z-score (β = 0.18; 95% CI, 0.09–0.26; p < 0.001). Restricted cubic spline analysis showed an approximately linear association. In sensitivity analyses additionally adjusted for late-onset sepsis and necrotizing enterocolitis, the association was no longer statistically significant. Exploratory mediation analysis suggested that the association may be partly explained through pathways involving late-onset sepsis, whereas the mediating role of necrotizing enterocolitis appeared to be more limited. Conclusions: In baseline-adjusted analyses, a higher early breast milk feeding proportion was associated with a higher discharge weight z-score; however, this association was attenuated and no longer statistically significant after additional adjustment for major neonatal complications. These findings should be interpreted cautiously and should not be considered evidence of a causal relationship, given the substantial potential for residual confounding by prematurity and illness severity. Full article

To enhance the positioning accuracy of the inertial measurement unit (IMU)-based pedestrian localization, this study proposes an adaptive ensemble extended Kalman filter (EnEKF) that incorporates a distance constraint (DC). This study first introduces a dual foot-mounted IMU-based pedestrian localization system that employs two IMUs to measure the target human’s position. Second, an augmented data fusion model is developed by incorporating attitude quaternions from the inertial navigation system (INS) into the conventional INS error-state vector. Based on this new data fusion model, a DC-based EnEKF is designed. In this method, the EnEKF employs ensemble factors to address nonlinear and non-Gaussian characteristics inherent in the data fusion process. Then, the colored measurement noise (CMN) is considered, and the method is modified to form an EnEKF under CMN (cEnEKF). Moreover, the DC is employed to further restrict the INS-derived position estimates of the left and right feet obtained from the EnEKF algorithm. Finally, validation in two real-world scenarios confirms the effectiveness and superior performance of the proposed approach. Full article

Human induced pluripotent stem cell (iPSC)-derived brain organoids have emerged as powerful three-dimensional (3D) platforms for modeling human neurodevelopment and neurological disorders. However, the absence of a functional vascular network remains a critical limitation, restricting oxygen and nutrient delivery, impairing metabolic stability, and constraining long-term maturation. Conventional extracellular matrix (ECM) mimetics, such as Matrigel and other static synthetic hydrogels, provide biochemical support but fail to recapitulate the dynamic remodeling that characterizes the developing neurovascular niche. Recent advances in stimuli-responsive hydrogels offer spatiotemporal control over matrix stiffness, degradability, viscoelasticity, and biochemical cue presentation. In this review, we discuss dynamic hydrogel systems within a structure–property–function framework, highlighting how network chemistry and architecture may regulate endothelial sprouting, lumen formation, vascular stabilization, and neurovascular unit maturation in vascularized brain organoid models, based on evidence from both organoid studies and related biomaterial or vascular systems. Photoresponsive, enzyme-cleavable, thermo-responsive, supramolecular, bio-orthogonal click-based, and bioprinted platforms are discussed with emphasis on mechanotransduction, angiocrine signaling, and barrier specialization. Functional outcomes, including trans-endothelial electrical resistance, selective permeability, transporter expression, electrophysiological integration, and sustained perfusion, are discussed alongside translational challenges such as cytocompatibility, oxidative stress, scalability, and regulatory feasibility. Collectively, dynamic hydrogels provide a versatile biomaterial strategy for improving vascularization and aspects of functional maturation in brain organoid models with enhanced physiological relevance. Ultimately, stimuli-responsive hydrogel systems may serve as enabling platforms for engineering vascularized brain organoids and advancing human-relevant neurovascular disease modeling. Full article

As the development of the ocean extends to the deep and open seas, the application of multi-hull floating systems is becoming increasingly widespread, covering offshore oil and gas transfer and material replenishment operations. In multi-body floating systems, the hydrodynamic interactions between adjacent floating bodies significantly affect the overall motion response and load distribution. However, there is currently a lack of systematic experimental research on systems involving three or more units under the combined action of wind, waves, and currents. This study presents a 1:50 scale model experiment on a five-body offshore replenishment station, comprising a central transfer platform and four surrounding vessels. Absolute six-degree-of-freedom motions and relative displacements between the transfer platform and neighboring vessels were measured. The results indicate distinct differences among the units. The peripheral vessels have greater horizontal and yaw motions, while the central units are more restricted. The relative motions are substantially increased for beam and oblique wave conditions, implying increased interaction effects in the gaps between neighboring bodies. Moreover, the combined oblique environmental loading and asymmetric mooring stiffness result in increased global drift and yaw motions. These findings provide benchmark data for numerical validation and practical guidance for the design and operation of multi-body floating systems. Full article

Objective: The objective of this study was to map and critically synthesize empirical evidence on ML/AI applications across surface mining unit operations, and to characterize models, validation practices, and evidence gaps. Eligibility criteria: Our eligibility criteria comprised peer-reviewed studies (2020–2025) applying ML/AI to surface mining activities, training/validating models on empirical datasets, and reporting quantitative performance metrics. Information sources: Scopus, ScienceDirect, Dimensions, and Web of Science were our information sources, last searched December 2025 and supplemented by website and citation snowballing. Risk of bias: Risk of bias was assessed using an adapted domain-based approach based on PROBAST, used to interpret findings without excluding studies. Synthesis method: Our research employed a narrative synthesis (no meta-analysis due to heterogeneity in datasets, algorithms, contexts, and metrics), grouped by application domain. Results: From 5317 records, 57 studies were included, concentrated in blasting (43), followed by load and haul (6), post-dismantling management (4), extraction (2), and overall exploitation (2). Studies predominantly reported statistical metrics (e.g., R², RMSE, and MAE), with limited operational performance indicators; validation was frequently site-specific. Dataset sizes were not reported consistently across studies. Limitations: This study’s limitations were database coverage, restricted timeframe, and incomplete reporting (e.g., software/tooling). Conclusions: ML/AI shows strong potential, especially in blasting, but scalable deployment is constrained by site specificity, inconsistent reporting, and heterogeneous validation; standardized reporting and operational indicators are priorities. Registration: The systematic review protocol was registered in OSF with DOI 10.17605/OSF.IO/5UMKB. Funding: EU Erasmus+ STRIM project (1010832727). Full article

Biomass combustion, as a key technology for achieving a low-carbon transformation of the energy system, faces multiple challenges in its efficient and clean utilization, including the high heterogeneity of fuels, the complex multi-scale coupling of the combustion process, and the attainment of ultra-low emissions. Traditional research methods have significant disconnections between microscopic mechanism understanding, macroscopic performance prediction of reactors, and end-of-pipe pollution control, which restricts the improvement of system performance. This review presents recent advances in advanced numerical simulation, pollutant control strategies, and bioenergy with carbon capture and storage (BECCS) pathways targeting ultra-low emissions in biomass combustion. This work synthesizes progress across three interconnected domains. First, methodologies are examined for integrating detailed chemical kinetics, particle-scale models, and reactor-scale simulations to develop high-fidelity predictive tools. Second, low-nitrogen combustion and synergistic pollutant control strategies for primary furnace types (e.g., grate, fluidized bed) are evaluated, alongside process optimization from fuel pretreatment to flue gas purification. Third, the potential for integrated design of biomass energy systems with carbon capture is assessed, emphasizing that system efficiency hinges on holistic “fuel-combustion-capture” chain optimization rather than isolated unit improvements. Future research directions are highlighted, including the development of physics-informed AI modeling paradigms, deeper co-design of multiple processes, and the establishment of robust life-cycle assessment frameworks. This review aims to provide a structured reference to inform both fundamental research and the practical development of next-generation clean biomass combustion technologies. Full article

Background: Despite well-established racial/ethnic disparities in cancer outcomes, little is known about the extent to which race/ethnicity influences adverse pregnancy outcomes (APOs) among women with early onset cancer. We evaluated racial/ethnic disparity in the occurrence of cancer during pregnancy and APOs among women with cancer in the United States. Methods: Data consisted of 17.6 million singleton deliveries among females aged 18–49 years from the National Inpatient Sample. Logistic regression models were used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs). Results: From 2000 to 2022, the prevalence of births among women with cancer increased more than 225%, from 120.4 to 391.8 per 100,000. After accounting for sociodemographic and behavioral/lifestyle factors and comorbidity index among women with cancer (n = 49,824, mean age = 33.4 years), non-Hispanic Black women had the highest odds for hypertensive disorders of pregnancy (OR = 1.67, CI: 1.54–1.82), preterm birth (OR = 1.44, CI: 1.26–1.64) and fetal death (OR = 3.04, CI: 1.99–4.63). Asian or Pacific Islander and Native American women had the highest odds for gestational diabetes (OR = 2.48, CI: 2.17–2.85) and fetal growth restriction (OR = 1.92, CI: 1.00–3.69), respectively. Among racial/ethnic minority women, the odds for maternal mortality and several APOs were significantly higher among those with cancer than those without cancer, with the odds for APOs being highest for breast cancer (OR = 1.39, CI: 1.23–1.56). Conclusions: This large population-based study showed significant racial and ethnic disparities in APOs among women with a concurrent cancer diagnosis at delivery. Targeted management of APO risk factors during pregnancy among racial/ethnic minority populations with cancer may help reduce adverse maternal and neonatal outcomes. Full article

Hydrogen energy is pivotal to the global energy transition, and the development of high-efficiency, safe hydrogen storage technologies constitutes a prerequisite for its large-scale commercialization. Kinetic bottlenecks including slow reactions, delayed front propagation, and marked spatial heterogeneity driven by strong thermal–mass transfer coupling restrict the engineering application of solid-state metal hydrides. However, the current research mainly focusing on overall performance lacks a systematic understanding of the spatiotemporal evolution mechanisms and their intrinsic links to internal structural control. In this work, a 3D multiphysics model of a LaNi₅-based reactor is developed to systematically elucidate spatiotemporal evolution patterns, facilitating the proposal of a structural decoupling framework based on synergistic thermal–mass resistance reconfiguration. Both absorption and desorption show distinct three-stage evolution, shifting from kinetic dominance to transfer limitation: absorption causes core self-inhibition via heat-hydrogen supply mismatch, leading to much lower core than surface storage capacity; desorption results in significant inner-layer lag due to endothermic cooling-driven pressure drops. Thermal–mass coupling-induced inverted spatiotemporal evolution is identified as the root cause of spatial heterogeneity. Quantitative comparison of straight-pipe, spiral-tube, and honeycomb structures reveals that internal architectures achieve effective thermal–mass decoupling through expanded heat-exchange areas, reconstructed diffusion pathways, and optimized heat source distribution. Notably, the honeycomb structure with a parallel micro-unit network achieves 89.1% and 86.6% reductions in absorption and desorption times, respectively, showing superior dynamic performance and field uniformity. This study provides a theoretical basis for the mechanism-driven design and synergistic performance optimization of high-efficiency solid-state hydrogen storage reactors. Full article

The upscaling of turbines in the offshore wind industry has been unprecedented, as compared to 5–6 MW rated turbines 10 years ago. A typical 20–26 MW rated turbine in modern commercial applications (MingYang MySE 18.X-20 MW installed in 2025 and 26 MW prototype by Dongfang Electric tested in 2025) has been demonstrated. This scaling has been made possible by increasing rotor diameters (>250 m) and hub heights (>150–180 m) to achieve capacity factors of up to 55–65%, annual energy generation of more than 80 GWh/turbine, and significant decreases in levelised cost of energy (LCOE) to current values of up to 63–65 USD 2023/MWh globally averaged in 2023 (with minor variability in 2024 due to market changes and new regional areas). The paper analyses turbine upscaling over three levels of hierarchy, including turbine scale—rated capacity and physical aspect, project scale—multi-gigawatts of farms, and market scale—the global pipeline > 1500 GW level, and combines techno-economic evaluation, structural evaluation of loads, and infrastructure needs assessment. The upscaling has the advantage of reducing the number of turbines dramatically (e.g., 500 to 67 turbines in a 1 GW farm, as turbine size is increased to 15 MW) and balancing-of-plant (BoP) CAPEX (turbine-to-turbine foundations and cables) by some 20 to 30 percent per unit of capacity, and serial production learning rates of between 15 and 18% per doubling of capacity. But the problems that come with the increase in ultra-large designs are nonlinear increments in mass and load (i.e., blade-root and tower-bending moments), logistical constraints (blades > 120 m, nacelle up to 800–1000 tonnes demanding special vessels and ports), supply-chain issues (rare-earth materials, vessel shortages increase day rates by 30–50%), and technology limitations (aeroelastic compounded by numerical differences between reference 5 MW, 10 MW, and 15 MW models), it becomes evident that there is a significant increase in deflections of the tower and blades and platform surge/pitch responses with continued increases in power levels, but without a correspondingly mature infrastructure. The regional differences (mature ports of Europe vs. U.S. Jones Act restrictions vs. scale-up of vessels/manufacturing in China) lead to the necessity of optimisation depending on the context. The analysis concludes that, to the extent of mature markets with adapted logistics, continuous upscaling is an effective business strategy and can result in 5 to 12 percent further reductions in LCOE, but beyond that point, gains become marginal or even negative, as risks and costs increase. The competitiveness of the future depends on multi-scale/multi-market-based approaches—modular-based families of turbines, programmatic standardisation, vibration control innovations, and industry coordination towards supply-chain alignment and standards. Its major strength is that it transcends mere size–cost relationships and shows how nonlinear structural processes, aero-hydro-servo-elastic interactions, and bottlenecks in logistical systems are becoming more determinant of the efficiency of ultra-large turbines. The study demonstrates that upscaling turbines has LCOE benefits through the support of associated improvements in installation facility, supply-chain preparedness, and structural vibration control potential, based on the comparisons of quantitative loads, techno-economic scaling trends, and regional market differentiation. Full article

Knowledge Tracing (KT) is a fundamental task in intelligent education systems, designed to track students’ evolving knowledge states and predict their future performance. While Deep Learning-based Knowledge Tracing (DLKT) models have advanced the field, they often face significant limitations in jointly capturing short-term performance fluctuations and long-term knowledge retention, which restricts their predictive precision in complex learning trajectories. This paper proposes the Extended Deep Knowledge Tracing (xDKT) model, which integrates the Extended Long Short-Term Memory (xLSTM) architecture to enhance multi-scale temporal learning representations. Specifically, through rigorous ablation studies over extended learning sequences (up to 1000 steps), our analysis indicates that the exponential gating and advanced scalar memory of sLSTM units are the primary drivers of performance. This architecture effectively captures both short-term performance shifts and long-term knowledge retention without the vanishing gradient degradation inherent to standard LSTMs. We evaluate xDKT across six diverse benchmark datasets, including Synthetic, Algebra2005–2006, Statics2011, and the ASSISTments series, covering over 22,000 learners. Experimental results show that xDKT yields improved Area Under the ROC Curve (AUC) scores on Statics2011 (0.8562) and ASSISTments2009 (0.8318) compared to baseline models such as DKT, DKVMN, and AKT. Finally, through extensive validation, these findings suggest that xDKT architecture provides a robust and promising framework for accurate and adaptive learning environments. Full article