Search Results (1,278)

Background: Value-based sarcoma care requires outcome measures that reflect the patient perspective; however, many sarcoma episodes begin with near-normal function and undergo necessary morbidity for oncologic control, making simple “improvement” an unreliable proxy of value. In routine care, patient-reported outcome data are often irregular and incomplete, limiting benchmarking and learning across institutions. We therefore developed a rule-based EQ-5D-5L (index and VAS) traffic-light framework and evaluated its feasibility and benchmarking signal in two institutions. Methods: We performed a retrospective, two-institution cohort analysis of 729 malignant and intermediate episodes, defined using a prespecified histology behavior mapping. PROM evaluation was anchored to a hierarchical T0 (index surgery date; if unavailable, radiotherapy start date; if unavailable, systemic therapy start date where a valid and interpretable start date was available). EQ-5D-5L index and EQ-VAS were assigned to prespecified locked windows: baseline (−90 to +14 days preferred; +15 to +90 days fallback), 12 months (180–365 days; target 270), and 24 months (660–820 days; target 730). A rule-based traffic-light classification was applied at 12 and 24 months (RED if index < 0.75 or VAS < 50; GREEN if index ≥ 0.85 and VAS ≥ 70; otherwise YELLOW). PROM evaluability was defined as the availability of at least one valid EQ-5D-5L index and/or EQ-VAS value within each window. Results: PROM evaluability in locked windows was feasible but incomplete. Baseline PROMs were available for 107/729 episodes (14.7%), 12-month PROMs for 119/729 (16.3%), and 24-month PROMs for 84/729 (11.5%). At 12 months, evaluable episodes included 75 from Institution A and 44 from Institution B; at 24 months, 56 and 28, respectively. Traffic-light outputs showed heterogeneity at both timepoints and clearer cross-institution difference at 24 months than at 12 months. At 12 months, the distribution was predominantly GREEN in both institutions (Institution A: 73.3% GREEN, 9.3% YELLOW, 17.3% RED; Institution B: 65.9% GREEN, 18.2% YELLOW, 15.9% RED; p = 0.373). At 24 months, Institution A maintained a high GREEN proportion with a low RED fraction (76.8% GREEN, 17.9% YELLOW, 5.4% RED), whereas Institution B showed a lower GREEN proportion and higher YELLOW/RED fractions (50.0% GREEN, 25.0% YELLOW, 25.0% RED; p = 0.014). Absolute EQ-5D-5L medians remained high overall, but the follow-up distributions showed a broader lower tail in Institution B. Conclusions: A prespecified EQ-5D-5L (index and VAS) traffic-light framework anchored by hierarchical T0 and evaluated in locked windows yields interpretable patient-perspective benchmarking signals in real-world sarcoma care. The approach was operationally feasible within the evaluable subset and appeared more discriminative at 24 months than at 12 months, while incomplete PROM capture remains a major implementation limitation for representative and reliable network-scale benchmarking and learning. Full article

The increasing demand for platinum group metals (PGMs) and critical base metals (BMs) underscores the critical roles these metals play in renewable energy and advanced technologies, enabling more efficient, environmentally sustainable operations. A hydrometallurgical approach to Au, Pd, and Pt tailings, derived from the glycine leaching of low-grade nickel and iron sulfide flotation concentrates, is investigated. The proposed process evaluates two glycine-based systems: glycine combined with KMnO₄ and catalyzed by cyanide under starvation conditions. Leaching with glycine in the presence of KMnO₄ (72 h, 25% solids, 60 °C, pH 11, dissolved oxygen 10 ppm, 126.7 kg/t glycine, and 7 kg/t KMnO₄) achieved extraction efficiencies of up to 66.7% Au, 89.1% Pd, and 95.8% Pt. In comparison, the cyanide-starved glycine system (72 h, 30% solids, 60 °C, pH 11, dissolved oxygen 20 ppm, 98.5 kg/t glycine, and 3.3 kg/t cyanide) resulted in up to 80.8% Au, 78.3% Pd, and 14.3% Pt. Activated carbon and Amberlite resin demonstrated selective adsorption of Au and PGMs. For activated carbon, Au adsorption exhibited a non-linear dependence on carbon dosage, reaching a maximum of 77.61% at 20 g/L, then decreasing to 50.85% at 25 g/L, and finally increasing to 65.04% at 30 g/L, indicating variable adsorption behavior. In contrast, Amberlite resin exhibited more consistent, progressive adsorption with increasing dosage. Au adsorption remained high across all conditions, increasing from 88.06% at 10 g/L to 99.67% at 30 g/L. Similarly, Pd and Pt adsorption improved significantly with resin dosage, reaching maximum values of 81.32% and 83.36% at 25 g/L, respectively, followed by a slight decline at 30 g/L. Implementing a two-stage process using carbon + resin (30 g/L) increased PGM recovery, achieving 99.89% Au, 81.8% Pd, and 92.4% Pt. Elution tests showed that Au (61.97%) and Pd (60.55%) were desorbed efficiently using thiourea (2% w/v) and HCl (0.5 M), whereas Pt elution proved difficult and required alternative strategies. The findings confirm glycine-based technologies as a promising, environmentally friendly alternative to conventional methods and provide a basis for further process development and optimization. Full article

The depletion of natural river sand resources in the construction industry and the pollution caused by iron tailings storage in the steel industry are the two major challenges currently faced. The use of iron tailings in construction materials is widely regarded as one of the most sustainable and cost-effective approaches. Based on C30 concrete, 12 steel tube iron tailings sand (IOT) concrete columns with different IOT substitution rates were designed and fabricated in this paper, and axial compression test research was conducted on them; finite element simulations were conducted for comparison with the experimental results, focusing on the influences of IOT substitution rate (0–100%), steel pipe wall thickness (1–4 mm), and steel strength (Q235, Q355, Q390, Q420, Q460) on the bearing capacity of concreted steel tube columns were parametrically analyzed. By comparing the calculation methods of the bearing capacity of concrete-filled steel tube columns in five relevant standards, the calculation formula for the bearing capacity of IOT columns was corrected and obtained. The results show that the failure mode of the IOT column is similar to that of the ordinary column, and the steel tube wall has all undergone circumferential band shear buckling. As the replacement ratio of IOT increases, the load-bearing capacity of columns initially improves and then declines. The finite element analysis results show that the bearing capacity of the IOT column is directly proportional to the wall thickness of the steel pipe, and increasing the wall thickness of the steel pipe can effectively improve the bearing capacity of IOT columns. The discrepancy between the predicted and experimental bearing capacities of IOT columns obtained based on the revision of the “Technical Code for Concrete-filled Steel Tube Structures” (GB 50936-2014) is within 10%, which can effectively predict the load-bearing capacity of IOT columns within a certain range. Full article

Inter-basin water transfer projects (IBWTPs) play a pivotal role in alleviating the spatiotemporal imbalances of water resources. However, their operation is exposed to multiple, highly interdependent safety risks that can significantly undermine system stability and water supply reliability. Existing studies predominantly focus on isolated risk factors or rely heavily on subjective data, which limits their ability to capture the complex interrelationships among risks and reveal their underlying propagation mechanisms. To address these limitations, this study proposes a novel risk correlation analysis framework that integrates Interpretive Structural Modeling (ISM) with copula functions. ISM is first employed as a preprocessing tool to structure expert knowledge and develop an initial risk correlation framework. It is then used to hierarchically organize the complex interrelationships among risks. Subsequently, copula functions are utilized to model nonlinear dependencies and tail behaviors among risk variables. This enables a quantitative assessment of correlation strengths and facilitates the construction of a risk topological network. An empirical case study is conducted based on the Middle Route of the South-to-North Water Diversion Project. The results reveal 13 significant correlations among six second-level risk categories. Natural risks (e.g., floods and geological hazards) are identified as the primary driving factors. They exhibit a strong positive correlation (0.6155) with engineering risks and serve as the most critical nodes for proactive risk prevention and control. Engineering risks function as central intermediary hubs in the risk transmission process, whereas water quality and economic risks are characterized as terminal endpoints. Furthermore, three principal risk propagation pathways are identified: (1) natural risks → engineering risks → economic risks; (2) natural risks → operational scheduling risks → social risks; and (3) engineering risks → water quality risks → economic risks. The resulting risk topological network demonstrates significant small-world properties, indicating highly efficient risk transmission within the system. Ultimately, this study provides a robust quantitative approach for analyzing risk interactions in complex engineering systems and enriches the theoretical framework of engineering risk management. It also identifies critical nodes and key transmission pathways for risk prevention and control in IBWTPs, thereby offering significant practical implications for operational safety. Full article

Antimicrobial peptides (AMPs) are natural bioactive peptides produced by all organisms—from plants to insects, microbes and animals—and constitute a first line of defense. As they exhibit a broad spectrum of activity (antibacterial, antiviral, antifungal, antiparasitic, anticancer), strong efforts are being made to integrate AMPs into clinical use. AMPs are also being investigated as anticancer agents to overcome the side effects and/or resistance associated with current chemotherapies. In this context, we identified the natural AMP chionodracine from a new biological source: an Antarctic fish. Starting from the fragmentation of a chionodracine mutant peptide, a rational modular design approach was applied to develop three very short peptides (Pep-A, Pep-B and Pep-C), which were further modified with an N-terminal myristic acid lipid tail. The anticancer activity of the three N-myristoylated short peptides (Myr-A, Myr-B and Myr-C) was explored against the human cervical cancer HeLa cell line. The rationale behind this study is based on the previously reported antifungal activity of these myr peptides and on their ability to interact selectively with biological membrane-mimicking synthetic phospholipids without being particularly hemolytic or cytotoxic towards normal cells. We first demonstrated that myr peptides had cytotoxic activity against HeLa cells (IC₅₀ from 32 to 47 μM) but spared healthy primary human fibroblasts, whereas the corresponding non-myr peptides failed to kill cancer cells. The peptide with no hemolytic activity and a low IC₅₀, labeled Myr-B, was selected for subsequent analyses. Lactate dehydrogenase (LDH) assay and scanning electron microscopy (SEM) analysis revealed membrane damage and predominantly necrotic cell death in HeLa cells exposed to IC₅₀ doses of the Myr-B peptide, compared with cells treated with Pep-B. To thoroughly investigate the molecular effects of Myr-B in HeLa cells, we employed high-resolution label-free shotgun quantitative proteomics coupled with bioinformatics. Our results showed that exposing HeLa cells to Myr-B led to the under-expression of proteins belonging to the “apoptosis- and splicing-associated protein complex”, potentially influencing the alternative splicing process and consequently leading to a possible susceptibility to programmed cell death. These findings indicate that modifying natural AMPs may be a promising strategy for developing selective anticancer drugs and pinpoint Myr-B as an interesting target for future studies. Full article

The complexity of physicochemical properties in iron ore tailings has led to extensive and varied study avenues. Moreover, changes in these features resulting from source discrepancies have complicated the identification of consistent patterns in study findings, thereby hindering the standardization and advancement of resource exploitation technologies. This paper provides a comprehensive analysis of the utilization pathways for iron tailings. It identifies the mainstream recovery processes for rare earth minerals, a relatively less-researched direction. It also describes research progress on the use of iron tailings for the preparation of fertilizers and soil conditioners, as well as their application as cementitious materials or aggregates in building materials and mine backfilling engineering. It incorporates various activation methods for the preparation of cementitious materials from iron tailings into a unified comparative framework and quantifies the key performance indicators of different activation pathways through a summary table. It also summarizes studies on the ecological reclamation of tailings ponds based on bioremediation techniques. The essential physicochemical properties of iron deposits are meticulously analyzed, and this is followed by a specialized overview of the principal treatment techniques, critical performance indicators, and their foundational mechanisms. The current application of various technical approaches is examined to identify key problems, and future development opportunities are outlined. Full article

High-frequency volatility and extreme tail risks in offshore wind power pose severe challenges to grid stability and economic operation. Conventional storage planning often relies on deterministic profiles or static allocation rules, failing to capture the non-stationary temporal dynamics of marine wind resources. To bridge this gap, this paper proposes a closed-loop framework that integrates ultra-short-term probabilistic forecasting with dynamic hybrid energy storage optimization. A novel Dual-Channel Residual Network is developed to provide well-calibrated predictive uncertainty quantification, which explicitly drives a Prediction-Guided Dynamic Hybrid Storage Optimization Framework. By dynamically coordinating lithium-ion batteries and liquid air energy storage based on evidential predictive variance, the proposed approach achieves superior synergy between short-term power response and long-duration energy shifting. Case studies on an offshore wind farm validate that the framework significantly reduces the Levelized Cost of Energy and loss-of-load risks while enhancing frequency regulation capabilities compared to state-of-the-art benchmarks. Full article

This paper addresses the problem of nonparametric regression for functional time series, a setting complicated by the infinite-dimensional nature of the covariates, temporal dependence, and potential for outliers. We propose a new robust estimator that combines three powerful ideas: (i) k-nearest neighbors (kNN) for adaptive localization in the functional space; (ii) local linear smoothing to reduce bias; and (iii) $\mathcal{M}$-estimation to ensure resilience against atypical observations. The key theoretical contribution establishes the almost-complete convergence of the proposed estimator under mild conditions that account for the functional geometry, weak dependence (via quasi-association), and robustness constraints. The obtained rate of convergence explicitly reveals the interplay between the functional concentration, dependence strength, and local smoothness of the model. A simulation study demonstrates that this method offers superior stability and predictive accuracy compared to classical alternatives, particularly under heavy-tailed errors and data contamination. The practical relevance of the approach is further illustrated through a one-step-ahead prediction application to a real-world environmental dataset of hourly NOx measurements. Full article

This study develops a risk-informed pricing framework for maintenance contracts in the trucking industry. We apply a comprehensive methodology combining statistical segmentation, cost analysis, and Value at Risk (VaR) modeling to a dataset of nearly 2000 contracts. Contracts were grouped by duration and truck usage, and distributions were fitted to estimate costs and compute risk premiums. Two pricing models are proposed: a traditional VaR-based approach and an adaptive model that incorporates distribution tail heaviness. Results show that the adaptive model resolves the counterintuitive decline in prices at higher risk levels and yields more stable, flexible premiums. These findings underscore the importance of tail-risk metrics in contract pricing to better capture cost uncertainty. The approach supports more accurate risk management and sustainable pricing strategies for maintenance services. Full article

Effect-size estimation for Mood’s median test has received relatively little methodological attention despite the test’s widespread use in robust and nonparametric analysis. This study evaluates four candidate effect-size estimators: the median absolute deviation-based estimator (Delta–MAD), the probability of superiority (PS), Cramér’s V, and a newly proposed bootstrap-standardized median difference (Delta-Boot) across simulation settings involving normal data with equal variances, log-normal skewness, and heteroscedasticity with a twofold variance difference. Under equal variances, PS achieved the highest classification accuracy for moderate and large effects, with Delta–MAD and Delta–Boot close behind and Cramér’s V performing worst. Performance under log-normal skewness was nearly unchanged, demonstrating the robustness of median- and rank-based methods to heavy right-tailed distributions. Notably, Delta–Boot began to show improved performance for moderate effect sizes in the log-normal setting. Under heteroscedasticity, estimator behaviour diverged sharply: PS remained highly effective for distinguishing no and large effects but showed reduced accuracy for moderate effects due to its sensitivity to spread differences; Cramér’s V degraded substantially across all effect sizes; and the two median-standardized estimators—especially Delta–Boot—were more resilient, stabilizing more rapidly with increasing sample size and achieving the highest accuracy for moderate and large shifts at larger n. These patterns indicate that PS (or Delta–MAD) is most appropriate when variances are equal or nearly so, whereas Delta–Boot provides the most reliable performance in settings where variance imbalance is likely. Finally, a real-world application to fasting glucose data from the 2024 WHO STEPS survey in Trinidad and Tobago illustrates the practical utility of these approaches. Full article

We introduce the Normalising Flow GARCH (NF-GARCH), a two-stage hybrid framework that enhances traditional GARCH models by replacing restrictive parametric innovation distributions with learned densities via normalising flows. Our approach preserves the interpretability of standard variance dynamics while addressing the common issue of innovation misspecification. In the first stage, we estimate standard GARCH variants (sGARCH, TGARCH, and gjrGARCH) to extract standardised residuals. In the second stage, a Masked Autoregressive Flow learns the underlying residual distribution, with samples from the flow subsequently driving the GARCH recursion for out-of-sample forecasting. Evaluated on 13 daily financial series (six FX pairs and seven equities), NF-GARCH demonstrates systematic, statistically significant improvements in forecast accuracy for skewed-t baselines. Wilcoxon signed-rank tests confirm superior performance specifically for gjrGARCH-sstd and sGARCH-sstd specifications. While the framework offers enhanced flexibility and generative realism, we observe that computational overhead is increased, and the log-variance specification of eGARCH exhibits instability when paired with flow-based innovations. These results suggest that while NF-GARCH effectively captures empirical tail behaviour in univariate settings, future research should explore conditional flow architectures and multivariate extensions to account for time-varying innovation shapes. For risk management, gains are most relevant where skewed-t baselines are used and where closer residual realism supports scenario analysis; effect sizes remain modest relative to model risk and implementation cost. Full article

Quantitative trading systems require predictive models that simultaneously deliver accurate forecasts, calibrated uncertainty quantification, and actionable risk measures. This paper proposes an information-theoretic semiparametric regression framework combining a convolutional neural network–Transformer (CNN–Transformer) network for nonlinear temporal dependencies with a Gaussian process (GP) prior for residual autocorrelation and calibrated predictive distributions. Three theoretical results are established: an identifiability theorem guarantees joint recoverability of the nonparametric and GP components; a consistency theorem showing that the penalised maximum likelihood estimator converges at a rate $n^{-1/(2+d_{\mathrm{eff}})}$; and a coverage theorem proving asymptotic nominal coverage of the GP’s credible intervals. The framework enables an entropy-regulated trading module where predictive differential entropy informs position sizing via an uncertainty-penalised Kelly criterion, Kullback–Leibler divergence quantifies model uncertainty, and CVaR-constrained optimisation controls the tail risk. Simulations show the method outperforms the CNN, long short-term memory (LSTM), Transformer, XGBoost, random forest, least absolute shrinkage and selection operator (LASSO), and standard GP regression approaches. Backtesting on four Chinese A-share stocks yielded annualised returns of 15.9–22.4% with Sharpe ratios of 0.49–0.62, maximum drawdowns below 15%, and daily 95% CVaR reductions of 28–31% relative to a full-Kelly baseline, confirming both predictive accuracy and risk management effectiveness. Full article

Constructing rapid aerodynamic shape optimization frameworks based on high-fidelity reduced-order models (ROMs) has become a mainstream solution for alleviating the excessive computational cost of full-order simulation-based design, especially for complex configurations with high-dimensional design spaces. In this study, we propose the concept of transfer optimization, where a low-fidelity, decoupled auxiliary submodule is used to guide the high-fidelity optimization of the full complex system. Building upon our previously proposed reduced-order-model based framework for efficient aerodynamic shape design, a transfer optimization framework is further developed to improve the efficiency of aerodynamic shape design for complex multi-component configurations. A novel auxiliary submodule method is introduced to address the “curse of dimensionality” in sampling over high-dimensional design parameter spaces. By reducing system complexity, this method significantly lowers the cost of individual samples. Based on the transfer optimization assumption, perturbation-based sampling around the low-fidelity solution overcomes the limitations of traditional data augmentation approaches. Moreover, the auxiliary submodule optimization results are used to construct a physically meaningful initial configuration, further accelerating convergence. The framework is validated on two transonic aerodynamic optimization test cases using the three-dimensional undeflected Common Research Model (uCRM) wing–body–tail configuration (with a wing aspect ratio of 9), with and without horizontal tail deflection. Results show that the proposed framework achieves accuracy comparable to full-order optimization while reducing computational cost by up to 69.8%. Compared to traditional ROM-based frameworks, efficiency is further improved by 18.5% and 24.1% in Case 1 and Case 2, respectively. Full article

Selective inhibition of the tumour-associated carbonic anhydrase (CA) isoforms IX and XII, which are overexpressed in hypoxic tumours, has emerged as a promising strategy for the development of novel anticancer agents. Among the diverse CA inhibitors reported to date, coumarins have attracted particular attention. These chromenone derivatives, widely distributed in phytochemicals, display a broad range of biological activities and are known to act as suicide inhibitors of CAs. Following the tail approach, we designed a series of hybrid compounds combining a coumarin core with an N-arylthioureido scaffold located at the C-7 position and investigated how structural variations—including substituents on the coumarin and aromatic moieties, tether length, and urea/thiourea isosterism—influence their biological properties (CA inhibition and antiproliferative activity). Substituted coumarins at C-3 and C-4 were efficiently prepared via Pechmann condensation, while the thioureido motif was introduced using various aryl isothiocyanates as key synthetic intermediates. The lead compound, featuring a dimethylated coumarin, a pentyl linker, and an N-(p-tolyl)thioureido residue, inhibited the target enzymes in the low- to mid-nanomolar range (K_i = 6.0 and 49.9 nM, respectively), displaying selectivity indexes (S.I.s) surpassing those of the reference drug acetazolamide (AAZ). Moreover, it exhibited potent antiproliferative activity, with GI₅₀ values in the low micromolar range (1.9–3.5 µM) against both drug-sensitive and multidrug-resistant cancer cell lines. Label-free three-dimensional holotomographic microscopy revealed that this compound triggers slow apoptosis, leading to cell death after approximately 20 h of exposure. Full article

Accurately characterizing the statistical fluctuations of vehicle radar cross sections (RCSs) across polarization states and azimuthal sectors is essential for evaluating detection performance, conducting probabilistic simulations, and analyzing target features in millimeter-wave radar systems. Existing one-dimensional RCS statistical models, including Weibull, Chi-square, Lognormal, Rice, and Gaussian distributions, are often limited by their restricted functional expressiveness, making it difficult to simultaneously capture skewness, tail thickness, and azimuthal dependence under narrow angular-domain conditions. In addition, purely nonparametric approaches tend to produce spurious modes under finite-sample conditions and lack interpretable structural priors. To address these limitations, this paper proposes a Unimodal RCS Semiparametric Density Estimator (URCS-SDE) tailored for ground-vehicle targets. The proposed approach adopts kernel density estimation (KDE) as a data-driven baseline representation and incorporates physically plausible structural constraints through unimodal shape projection. Then a beta-type tail template is further introduced in the normalized amplitude domain to regulate boundary decay behavior. Finally, weighted least-squares calibration is performed on the histogram grid of the empirical probability density function (PDF), achieving a balanced trade-off between fitting accuracy and stability in both the peak and tail regions. Using multi-azimuth RCS measurements of two representative ground vehicles, the URCS-SDE is systematically compared with five classical parametric distributions and a representative regularized mixture density network (MDN) baseline. Performance is evaluated under both full-azimuth and directional-window conditions using the sum of squared errors (SSE), root mean squared error (RMSE), coefficient of determination (R-square) and held-out negative log-likelihood (NLL). The results show that the URCS-SDE consistently provides the most accurate and stable density estimates, especially in narrow angular windows. In addition, a threshold-based detection-support example derived from the fitted PDFs demonstrates that the advantage of the URCS-SDE transfers from density reconstruction to a directly engineering-relevant downstream quantity. Full article