Search Results (1,619)

To address the issue of missing position in flight trajectory data collected by Automatic Dependent Surveillance-Broadcast (ADS-B) systems, a flight trajectory tensor completion model based on truncated Schatten p-norm minimization is proposed. First, the low-rank characteristics of the trajectory set are validated using Singular Value Decomposition (SVD); based on this, the data is transformed into a three-dimensional tensor structure. Next, a regularization strategy combining the Schatten p-norm with a singular value truncation mechanism is introduced to construct the trajectory tensor completion model, which suppresses noise and interference from minor components while preserving the main variation patterns of the trajectories. Finally, the model is optimized and solved using the Alternating Direction Method of Multipliers (ADMM) to obtain the completed trajectories. Taking historical ADS-B trajectory data from Orly Airport to Toulouse Airport as an example, the completion results of the proposed model under different missing patterns, missing rates, and flight phases are analyzed from both qualitative and quantitative perspectives. Experimental results show that compared with other representative models, the proposed model achieves the best completion performance under different missing patterns and missing rates; the completion performance during the cruise phase is better than during the ascent and descent phases. The proposed model can serve as a preprocessing technique for flight trajectory data in air traffic, providing more complete and reliable data support for various downstream applications. Full article

The cryptocurrency market has attracted significant attention from global investors, with Cardano (ADA) ranking among the top cryptocurrencies by market capitalization. However, predicting ADA returns remains challenging due to the complex, multi-scale dynamics influenced by Federal Reserve policies, geopolitical events, and high-frequency trading. This study proposes a “Sliding EMD–Multi Variables” framework for cryptocurrency return prediction, leveraging Empirical Mode Decomposition’s multi-scale fractal properties to capture nonlinear dynamics at different time scales. The sliding window decomposition method addresses data leakage issues while incorporating key economic and policy variables at the component level. The empirical results demonstrate that the Sliding EMD system significantly outperforms univariate and multivariate benchmarks. Compared to the univariate system, it improves MSE, RMSE, SMAPE, and DSTAT by 0.83%, 0.42%, 5.23%, and 0.43%, respectively, while enhancing investment metrics (maximum drawdown, Sharpe ratio, Sortino ratio, Calmar ratio) by 0.19, 0.36, 0.95, and 0.15. Against the multivariate system, improvements reach 5.52%, 3.14%, 5.74%, and 17.62% in prediction accuracy, with investment performance gains of 0.47, 1.69, 4.27, and 0.31. Incorporating economic variables at the component level yields additional improvements of 0.94%, 0.47%, and 0.78% in MSE, RMSE, and MAE. These findings offer valuable insights for cryptocurrency portfolio optimization using fractal-based decomposition methods. Full article

The recycling of Floating Production Storage and Offloading (FPSO) units has become an important economic and environmental challenge as a growing number of offshore assets reach end-of-life. This study evaluates the comparative economic, environmental, and social performance of alternative FPSO recycling scenarios evaluated using a stochastic Monte Carlo simulation, focusing on five FPSOs that operated in Brazil and were scheduled for recycling either domestically or in Denmark. Twelve performance indicators were aggregated into sustainability indices using a Monte Carlo simulation with 100,000 iterations, enabling analysis of robustness and variability across ten recycling scenarios. The results indicate that Brazilian recycling scenarios (P-32 and P-33) outperform the Danish scenarios in terms of global performance, with Global Sustainability Index values predominantly ranging from 0.59 to 0.75, compared to 0.37 to 0.61 for the Danish cases. Differences in performance are mainly associated with towing distance, cost structure, and emissions. Social indicators show limited variability and act as a stabilizing component across scenarios. Plasma cutting presents slightly better environmental and economic results than LPG cutting, although it does not alter the overall ranking of scenarios. These findings support decision-making on FPSO recycling scenarios by highlighting the role of uncertainty and contextual factors, particularly in emerging recycling markets. Full article

(1) Research Highlights: This study provides the first integrated assessment of Scots pine (Pinus sylvestris L.) growing in the urban forests of Karaganda, Kazakhstan, a city consistently ranked among the most air-polluted cities globally. We examined the adaptive phyto-chemical response of needles to extreme technogenic stress and evaluated their dual potential as biological filters and renewable sources of bioactive compounds. (2) Background and Objectives: Urban forests are critical for mitigating air pollution; however, the biochemical responses of trees in heavily industrialized environments remain poorly understood. Karaganda faces severe atmospheric pollution from mining, metallurgy, and energy sectors, with particulate matter (PM) levels exceeding permissible limits by up to 20-fold. This study aimed to evaluate the state of Pinus sylvestris, a key component of local protective plantations, by studying heavy metal accumulation, anatomical localization of secondary metabolites, and the phytochemical profile and biological activity of needle extracts obtained using different extraction techniques. (3) Materials and Methods: Needles were collected from 15 trees across three sites in Karaganda’s industrial green zones. Heavy metal content (Pb, Cd, As, and Hg) was determined using atomic absorption spectroscopy and voltammetry. Anatomical–histochemical analysis localizes major metabolite classes. Liquid extracts were prepared using four methods, percolation (PER), vortex-assisted (VAE), microwave-assisted (MAE), and ultrasound-assisted (UAE) extraction, and analyzed by GC-MS. Antimicrobial activity was tested against S. aureus, B. subtilis, E. coli, and C. albicans using the disk diffusion method. The antioxidant capacity (water- and fat-soluble) was measured amperometrically. Statistical analysis was performed using one-way ANOVA with Tukey’s HSD test (p < 0.05). Results: Despite extreme ambient pollution, heavy metal concentrations remained below pharmacopoeial limits (Pb < 0.1, Cd < 0.05, As < 0.01, Hg < 0.001 mg/kg), indicating effective biofiltration without toxic accumulation. Histochemistry confirmed the active synthesis of protective phenolics, flavonoids, and essential oils in the mesophyll, epidermis, and schizogenic cavities. GC-MS identified 72 compounds in the PER extract, 70 (the VAE), 72 in (MAE), and 46 in (UAE). The PER extract exhibited the highest relative abundance of bioactive terpenoids: α-cadinol (5.24%), α-muurolene (4.32%), and caryo-phyllene (2.20%). UAE extracts exhibited elevated 5-hydroxymethylfurfural (6.90%), indicating degradation. Antimicrobial testing revealed that PER produced the largest inhibition zone against S. aureus (15.0 ± 1.0 mm), significantly exceeding that of the other methods (p < 0.001). PER extract also demonstrated the highest water-soluble antioxidant capacity (3600 ± 0.40 mg quercetin equiv./dm³) and substantial fat-soluble activity (1633 ± 0.23 mg gallic acid equiv./dm³). (4) Conclusions: Pinus sylvestris in Karaganda exhibits remarkable adaptive resilience, maintaining safe heavy metal levels while accumulating a rich repertoire of stress-induced secondary metabolites. Classical percolation optimally preserves this native phytocomplex, yielding extracts with superior antimicrobial and antioxidant properties. These findings support a dual-use model wherein urban pine plantations simultaneously serve as living biofilters and renewable sources of standardized bioactive extracts, a concept with direct implications for circular bioeconomy strategies in industrial regions worldwide. This supports the strategic importance of coniferous plantations for bioremediation and sustainable resource use in industrial regions. Full article

In ground-based short-wave infrared (SWIR) astronomical observations, temperature drift in the detector readout circuit often introduces nonlinear, spatially non-uniform stripe noise together with Gaussian noise, making weak stellar targets easily submerged and difficult to detect. To address this challenge, we propose a structurally guided weighted low-rank denoising method for infrared star images. Going beyond traditional spatial filtering and standard low-rank decomposition, the proposed method integrates physical priors with mathematical optimization into a unified framework. First, the point spread function (PSF) characteristics of stellar targets are used to construct a hierarchical structural filter, which is further transformed into adaptive prior weights. This design preserves weak-target energy while suppressing noise during iterative optimization. Second, by exploiting the global spatial correlation of the image, residual stripes and the background are jointly modeled as a low-rank component for effective separation. Finally, Bilateral Random Projection (BRP) is introduced to accelerate the weighted soft-thresholding iterations. Experiments on real ground-based observation data, together with ablation studies and sensitivity analyses, demonstrate that the proposed method effectively suppresses structured stripe interference while preserving weak stellar targets under low-SNR conditions. In addition, the acceleration module further improves computational efficiency, making the framework more suitable for practical real-time processing. Full article

Public Irrigation Projects (PIPs) play a strategic role in water security and rural development in semi-arid regions. However, the absence of standardized and replicable tools to assess governance performance and integrate social, ecological and institutional dimensions remains a challenge for sustainable management. To bridge this gap, this study proposes a framework for the quantitative operationalization of the Social–Ecological Systems (SES) approach through the development of the SES Governance Index (SGI), a composite indicator designed to assess adaptive governance in PIPs. The SGI is constructed through a procedure that translates SES components into measurable indicators using a conditional weighting protocol based on correlation analysis and dimensional diagnostics. Five subindices corresponding to core SES dimensions are developed using geometric aggregation, with weights determined according to the statistical structure of each dimension. These subindices are integrated into the final SGI through weighted linear aggregation. The framework is applied to nine PIPs in Brazil to demonstrate suitability for comparative assessment. Rather than producing a fixed ranking, the SGI is presented as a flexible metric for diagnosing governance structures and identifying systemic imbalances. By quantitatively operationalizing the SES framework, this study contributes a methodological tool for governance assessment in PIPs and other resource-dependent contexts. Full article

This pilot study aimed to evaluate the early postpartum dynamics of lactoferrin concentration in bovine colostrum and to investigate its relationship with rapid on-farm quality indicators during the first 12 h after calving. Colostrum samples were collected from six multiparous cows immediately after calving (0 h) and again 12 h later. Colostrum specific gravity and % Brix values were measured on-farm, and lactoferrin concentration was determined using LC–MS/MS analysis. Temporal changes were assessed using the Wilcoxon signed-rank test, and relationships between variables were evaluated using linear regression and Spearman correlation analysis. Lactoferrin concentration decreased significantly between 0 and 12 h after calving (median: 3.350 vs. 2.175 mg/mL; p = 0.031). In parallel, statistically significant decreases were observed in both colostrum specific gravity and % Brix values over the same period (p = 0.031 for both indicators). Linear regression analyses showed positive slopes between lactoferrin concentration and specific gravity and between lactoferrin concentration and % Brix at both 0 and 12 h after calving; however, these relationships did not reach statistical significance (p > 0.05 for all models), with explained variance ranging from R² = 0.156 to 0.409. Spearman correlation analysis also revealed moderate positive correlation coefficients (p > 0.05), although none of the correlations reached statistical significance. These results indicate a rapid decline in lactoferrin concentration during the first 12 h after calving, occurring in parallel with significant decreases in widely used on-farm colostrum quality indicators. Despite the lack of statistically significant associations, the observed positive relationships indicate that lactoferrin may represent an additional component of colostrum composition that is not directly reflected by refractometric and density-based indicators. Full article

Understanding how climate and land use interact to shape freshwater quality remains challenging across heterogeneous river basins. This study analysed monthly citizen-science measurements of nitrate (NO₃), phosphate (PO₄), and turbidity, collected between 2016 and 2024, across seven Italian river basins representing contrasting climatic and land-use contexts. A non-parametric analytical framework combining Kruskal–Wallis tests, aligned rank transform analyses, principal component analysis (PCA), and basin-specific Somers’ D statistics was applied to ordinal concentration data. Significant differences among basins revealed persistent spatial structuring of water-quality regimes. PCA identified two largely independent gradients: a dominant nutrient axis defined by NO₃ and PO₄, and a secondary turbidity axis. Urban and industrial land use aligned with higher nutrient categories, while vegetated landscapes were associated with lower concentrations. Climatic effects were basin specific. Precipitation showed opposing relationships with NO₃, suggesting both mobilisation and dilution processes, whereas temperature was positively associated with PO₄ in several basins and negatively related to NO₃. Turbidity displayed variable links with precipitation and temperature, reflecting hydrological and seasonal controls. Overall, results indicate that land use represents the primary structural driver of nutrient variability, while climatic factors modulate basin-specific responses. The integration of citizen science observations with robust non-parametric approaches provides a scalable framework for detecting environmental drivers and supporting the targeted management of Mediterranean river systems. Full article

Manual single-slice lumbar quantitative computed tomography (QCT) depends on operator-driven slice selection and trabecular region-of-interest (ROI) placement. We developed a fully automated single-slice workflow for vertebral trabecular Hounsfield unit (HU) measurement that combines unsuitable-slice prescreening, dual-purpose segmentation, intra-patient slice-quality ranking, and a deterministic inner ROI rule. The pipeline includes an Eligibility Gate, QC-Envelope segmentation for broad, vertebral- and usability-preserving delineation, PairRank-Swin for best-slice selection, and dedicated trabecular segmentation for final quantitative analysis. In the independent external cohort, 4 cases were considered non-evaluable by both manual review and the pipeline, and 2 additional borderline-quality cases were manually measured but rejected by the pipeline; therefore, paired HU agreement analysis included 44 evaluable cases. Agreement remained high, with Pearson’s r = 0.987, Lin’s CCC = 0.985, mean bias −0.44 HU, and limits of agreement from −14.88 to +13.99 HU. Coverage was 84.1% within ±10 HU and 97.7% within ±15 HU. Ablation analysis showed that slice ranking and ROI erosion were the most critical components. In an open module-level baseline comparison, QC-Envelope segmentation substantially outperformed TotalSegmentator. This workflow provides high agreement with expert HU measurement while preserving reviewable intermediate outputs. Full article

Educational success is a critical component of societal development, yet increasing student dropout rates present ongoing challenges. While supervised learning models are commonly used for dropout prediction, they rely on manually labeled data, a process that is time-consuming and dependent on expert annotation. Unsupervised learning models, clustering approaches, have been explored as an alternative; however, existing methods typically group students based on activity patterns without generating binary outcome labels such as dropout or success. Furthermore, their effectiveness often depends heavily on the quality of the selected features, and most current solutions utilize only limited or pre-structured subsets of institutional data. This paper addresses these challenges and proposes EHCFE (Enhanced Hierarchical Clustering with Feature Engineering), to automatically generate binary labels from unlabeled educational datasets. EHCFE applies feature engineering by generating new features from the top-ranked features identified during feature selection while retaining the original feature set, thereby improving the quality of the labeling outcomes. The approach is evaluated on three datasets and compared with current and state-of-the-art models using several evaluation metrics, including F₁ score, area under the receiver operating characteristic curve (AUC), and silhouette coefficient. Experimental results show that EHCFE achieves the highest F₁ score (0.709 and 0.28) and AUC values (0.766 and 0.81) on two datasets. A ranking analysis across six evaluation metrics demonstrates that EHCFE outperforms existing models, achieving the highest average ranks of 1.50 and 1.83 on two datasets and a competitive rank of 1.92 on the third. Full article

The physicochemical properties and volatile composition of fruits are critical determinants of fruit quality and processing performance. This study evaluated major green plum cultivars from Sichuan and Yunnan Provinces by analyzing fruit morphology, nutritional composition, bioactive compounds, and volatile profiles. Multivariate statistical analyses, including orthogonal partial least squares discriminant analysis (OPLS-DA), principal component analysis (PCA), and cluster analysis (CA), were applied to comprehensively assess cultivar-dependent quality differences. EH exhibited the highest total acid and glucose contents, whereas MD showed superior soluble solids, total sugars, solid–acid ratio, and several organic acids and sugars. Yunnan cultivars generally showed higher flavonoid contents and stronger antioxidant activities than Sichuan cultivars. Citric acid was the predominant organic acid. A total of 97 volatile compounds were identified. Ten volatile compounds were detected in all eight varieties, including butyl acetate, hexyl acetate, and butyl butyrate. EH and MD released the higher volatile, and PCA-based comprehensive evaluation ranked the cultivars as follows: EH, MD, EY, YZ, PX, EZ, DY and DN. Therefore, EH and MD exhibited superior overall quality in physicochemical properties and volatile composition. These findings provide a theoretical basis for evaluating green plum quality and their rational utilization in production and processing. Full article

Background/Objectives: The fundamental classification based on white, brown, pink, and beige adipose tissue morphology together with fat vacuole content released into the tumor microenvironment incompletely defines breast cancer-associated adipose tissue (BCAAT) heterogeneity and does not sufficiently explain its controversial impact on invasion, recurrence, or survival in breast cancer (BC). We aim to expand BCAAT characterization by systematically evaluating stromal cellular elements within peritumoral adipose tissue, including CD34-positive fibroblasts, smooth muscle actin (SMA)-positive myofibroblasts, inflammatory cells, and microvascular structures to define distinct BCAAT subgroups. Methods: CD34 and smooth muscle actin (SMA) double immunohistochemistry was performed on 109 BC tissue specimens from patients aged 35 to 79 years old, followed by microscopic evaluation of cellular and vascular components inside peritumor adipose tissue. Microscopic findings were then correlated to age, body mass index (BMI), lymphovascular (LVI) and perineural invasion (PnI), recurrence (R), and tertiary lymphoid structures (TLSs). Results: Four BCAAT subtypes have been identified as fibroblast-rich (F^Rich_BCAAT), myofibroblast-rich (MyoF^Rich_BCAAT), vascular-rich (V^Rich_BCAAT), and mixed-vascular and inflammatory-rich (VI^Rich_BCAAT). The F^Rich_BCAAT subtype predominates for the age subgroup 35 to 49 years old and is a significantly worse prognostic factor for survival (p = 0.022). For the age subgroup of 50 to 69 years old, the VI^Rich_BCAAT subtype significantly influences PnI (p = 0.05) but not survival (Log-rank test, z = 0.57, p = 0.57). V^Rich_BCAAT was significantly impactful for BC patient survival aged 70 to 75 years old (p = 0.043). BMI did not correlate with any of the BCAAT subtypes but was strongly correlated with prognostic markers for each BCAAT subtype. Conclusions: Based on immunohistochemically detected cellular and vascular components, four microscopic BCAAT subtypes were identified. Three of four BCCAT subtypes specifically affect BC patient prognosis and survival depending on age. Full article

To address the shortage of natural sand and the unclear mechanism of lithology’s influence on the application of manufactured sand, this study explores the applicability of neutral rock manufactured sand in asphalt mixtures. Taking neutral diabase manufactured sand as the research object, a series of tests including the Marshall test, water stability test, high- and low-temperature stability test, and surface free energy (SFE) test were conducted to systematically analyze the effects of aggregate lithology on the volumetric indicators, road performance, and interface adhesion of asphalt mixtures. Additionally, the improvement effect of cement as an anti-stripping agent was verified. The results show that lithology of manufactured sand significantly regulates the performance of asphalt mixtures. In terms of volumetric indicators, the limestone manufactured sand mixture has the smallest void ratio (3.81%), while the diabase manufactured sand mixture has the largest (5.81%), requiring an appropriate increase in the mixing ratio of diabase manufactured sand to optimize the compaction effect. For water stability, the short-term performance ranks as diabase ≈ limestone > granite, and the long-term durability ranks as limestone > diabase > granite. A least-squares linear regression model demonstrated that the polar component of aggregate surface free energy exhibits a strong positive correlation with asphalt–aggregate adhesion work (R² = 0.92), which quantitatively explains variations in the 48 h immersed Marshall residual stability ratio among different lithologies. Regarding high-temperature stability, the order is diabase > limestone > granite. Thanks to its low crushing value and strong angularity, the diabase manufactured sand mixture achieves a dynamic stability of 12,629 times/mm at 60 °C, showing the best rutting resistance. In terms of low-temperature performance, the diabase manufactured sand mixture exhibits the optimal initial crack resistance (maximum flexural strain of 2757 με) and long-term durability (strain attenuation rate of 11.7% after 30 cycles), while the granite manufactured sand mixture fails to meet the design requirements. Adding 1.5%~2.0% cement can significantly improve the adhesion between manufactured sand and asphalt, with more obvious enhancement effects on granite and diabase, thereby optimizing water stability and high-temperature stability. The research results provide theoretical support and technical reference for the scientific selection and engineering application of fine aggregates in asphalt pavements. Full article

The core component to ensure the refined and safe operation of distribution network scheduling is 10 kV bus load probabilistic prediction. However, existing probabilistic prediction methods suffer from insufficient dynamic feature extraction and compromised prediction reliability caused by quantile crossing. To address these issues, this paper proposes a 10 kV bus load probabilistic prediction method integrating multi-value quantile regression (MQR) and a temporal fusion ensemble learning model (ELM). Firstly, a temporal fusion ensemble learning model is constructed, which integrates multiple temporal fusion network (TFN) sub-models through a stacking framework to parallel extract multi-dimensional temporal features of loads, effectively enhancing its feature capture capability for complex load data. Secondly, MQR is introduced as the core objective function to synchronously generate multi-quantile load forecasting results, comprehensively depicting the load probability distribution. Finally, a Listwise Maximum Likelihood Estimation (ListMLE) ranking constraint mechanism is embedded, which optimizes quantile ordering through monotonicity constraints, significantly reducing the degree of quantile crossing and improving the interpretability of forecasting results. The results show that the MQR-ELM algorithm achieves a Prediction Interval Coverage Probability of 94.624% (close to the nominal coverage rate of 95%), a Prediction Interval Averaged Width of 588.526, a Crossing Degree Index of only 0.0476, and a Continuous Ranked Probability Score as low as 84.931. All core indicators are significantly superior to those of the comparative algorithms. Full article

Urban flood resilience has emerged as a holistic citywide approach for mitigating flood hazards and navigating the impacts of extreme weather patterns induced by climate change. This is particularly pertinent for high-risk, low-elevation coastal cities like Georgetown, Guyana. However, while the literature on Georgetown includes assessments, analyses, modeling, vulnerability, and the socio-political history of flooding, we found no evidence of flood resilience assessment for the city. Therefore, this study presents a data-driven evaluation of flood resilience at the sub-district level in Georgetown. To accomplish this, we constructed flood resilience indices (FRIs) using the aggregated weighted mean index approach and census-based indicators across physical, social, and economic dimensions. Principal component analysis (PCA) was employed to generate these weights and, subsequently, to perform dimensionality reduction and determine a linear regression model for the FRI values. To evaluate the stability of the constructed indices, robustness tests were conducted using alternative normalization and weighting schemes to demonstrate the consistency of resilience rankings across specifications. The results show that (a) economic resilience is lowest, (b) there is notable clustering and sharp disparities in the physical and social dimensions, and (c) the social dimension has the strongest correlation with the total FRI, which is generally heterogeneous. PCA-derived principal components explained 77.347% of the variation in the FRI values, enabling dimensionality reduction and three-dimensional graphical presentations. Our findings provide urban planners with insights into the distribution of flood resilience needs across the city. This study enables informed decision-making, serving as a pathway to achieve equitable resource allocation and build the city’s resilience. Full article