Search Results (10,350)

The Abrolhos Magmatic Province (AMP), situated along the southeastern Brazilian passive margin, comprises a Paleocene–Eocene transitional basalt series of alkaline affinity. Despite the lack of mineral chemistry and thermobarometric estimates, it has long been linked to a classical deep-mantle plume model. This study integrates mineral chemistry, calculations of intensive parameters (P, T, H₂O), geochronology, and geochemical modeling to evaluate an alternative explanation for AMP magmatism. Whole-rock and clinopyroxene compositions from different AMP localities are consistent with parental magmas derived from fertile, pyroxenite-enriched mantle sources that melted under ambient mantle potential temperatures (~1300–1400 °C). Inverse petrological modeling using alphaMELTS and MeltPT, together with trace-element systematics, suggests low degrees of partial melting within asthenospheric domains. These results indicate that shallow (upper-mantle) processes and high mantle fertility were important controls on melt generation. New ⁴⁰Ar/³⁹Ar ages of 24.3–28.4 Ma for southern AMP rocks are also difficult to reconcile with a simple age-progressive evolution of the previously proposed plume model. Taken together, the data support ambient-temperature melting of a fertile mantle as a plausible explanation for Abrolhos magmatism and reduce the need to invoke a classical high-temperature mantle plume as the sole model. Here, we favor a tectonically controlled model, involving localized shallow mantle processes such as edge-driven convection and/or lithospheric delamination as triggers for intraplate magmatism along the South Atlantic margins. Full article

For studies utilizing methods such as water color parameter inversion and algal bloom classification, abundant spectral bands and high spectral resolution are of great significance. However, for multispectral satellite sensors that are not designed for water color studies (e.g., Sentinel-2 MSI), the number of bands in the visible–near-infrared range is limited, and lacks specific spectral bands with rich spectral information. Hyperspectral reconstruction of multispectral data based on hyperspectral remote sensing reflectance ($R_{rs}$) databases and machine learning algorithms have been proven to be a feasible solution. Based on the in situ measured $R_{rs}$ data, this study constructed a large-sample hyperspectral $R_{rs}$ database covering various optical water types using two Chinese hyperspectral satellites, and compared the spectral reconstruction accuracy of six machine learning algorithms. The results show that expanding the $R_{rs}$ database for model training by integrating hyperspectral satellite data can effectively improve the reconstruction accuracy in waters of different optical types. Comparisons with in situ measured hyperspectral $R_{rs}$ indicate that the reconstructed Sentinel-2 hyperspectral data achieve high accuracy, with the Spectral Angle Mapper (SAM) less than 5° and the correlation coefficient ($r$) higher than 0.7. Furthermore, the reconstructed data can effectively restore spectral information not captured by the original multispectral data, such as the suspended sediment $R_{rs}$ peak at 580 nm and the chlorophyll $R_{rs}$ valley at 680 nm. Through spectral reconstruction, the spectral resolution of Sentinel-2 can be maximized while retaining its advantages of fast revisit capability and high spatial resolution, thereby expanding its application potential in water color remote sensing. Full article

Multivariate time series forecasting presents a challenging problem in stochastic modeling, particularly under non-stationary conditions with low signal-to-noise ratios. While recent inverted architectures enhance cross-variable dependency modeling, the conventional point-wise inversion strategy often compromises local temporal patterns. To address this limitation, we propose PiTransformer, a gated patch-wise inverted framework for multivariate time series modeling. Specifically, a Patch-wise Inverted Embedding (PIE) mechanism is introduced to segment temporal sequences into regional patches prior to inversion, enabling the preservation of localized temporal structures. In addition, a Variable–Temporal Gating (VTG) module is incorporated to regulate feature interactions based on the information bottleneck principle, thereby suppressing spurious correlations in noisy environments. Empirical evaluations on diverse benchmarks—including financial and energy datasets—demonstrate that PiTransformer achieves consistent improvements in predictive accuracy and stability over competitive baselines. These results suggest that the proposed framework provides a robust and interpretable approach for modeling high-dimensional stochastic time series under non-stationary conditions. Full article

We present a short review dedicated to low-lying meson states. We present all meson nonets, which consist from up, down and strange light quarks. We consider the scalar nonet as a basic nonet. We work in the framework of the massless Nambu–Jona-Lasinio $U_R\left(3\right)\times U_L\left(3\right)$ quark model. The collective meson states are described through initially bare quark–antiquark pairs, whose condensates lead simultaneously to spontaneous breaking of the chiral and the flavour symmetry. After quantisation and the spontaneous breaking of the chiral symmetry, when quarks obtain constituent nonzero masses, they become dressed. We present an explanation of the inverse mass hierarchy of the low-lying nonet of the scalar mesons. The proposed explanation is based on symmetry principles. It is shown that, due to the flavour symmetry breaking, two isodoublets of $K_0^{*}\left(700\right)$ mesons play the role of Goldstone bosons. It is also proven that there exists a solution with almost degenerate masses of the $a_0\left(980\right)$ and $f_0\left(980\right)$ mesons and a zero mass of the $f_0\left(500\right)$ meson. Short description of the physical properties of other meson nonets is provided. In particular unique mass relations among the different nonets, which are experimentally confirmed, are presented. Full article

Non-destructive characterization of electromagnetic (EM) wave propagation properties in drill cores is gaining prominence as a foundation for reliable geophysical inversion, improved rock-physics modeling, and increasingly data-driven mineral exploration workflows. Lab-based rock characterization requires benchmarks that link the density, elastic, electrical, magnetic, and EM properties of studied cores to lithology and mineralization, enabling more accurate interpretation of geophysical data. This study develops a robust high-frequency EM (HFEM) wave velocity measurement technique and incorporates it within a standardized non-destructive framework validated across multiple mineral systems in Newfoundland and Labrador, Canada. The developed method derives EM velocities from two-way travel time through drill cores positioned above a metallic reflector, supported by finite-difference time-domain simulations to optimize antenna frequency and test geometry. A repeatable signal-processing workflow was implemented to enhance reflection picking. Results reveal systematic EM velocity contrasts among host rocks and oxide or sulfide-bearing systems, with oxide-rich and massive sulfide intervals exhibiting higher density, elevated conductivity and susceptibility with strong EM attenuation. The integrated dataset shows that conductivity and magnetic susceptibility significantly influence EM velocity response and detectability limits. The proposed multi-parameter benchmark enables enhanced discrimination of lithological and mineralization controls in mineral exploration workflows and supports more accurate time–depth conversion in HFEM geophysical and ground-penetrating radar (GPR) methods. Full article

Under turbulent wind conditions, rapid wind speed fluctuations can markedly increase the fatigue loads borne by wind turbine blades and towers. In practice, conventional PID pitch control based on speed feedback often struggles to deliver satisfactory load mitigation, mainly because the wind turbine system is highly nonlinear, strongly coupled, and subject to time-delay effects. To overcome these limitations, this paper proposes a load-predictive pitch control strategy built on a Long Short-Term Memory (LSTM) network. Specifically, the LSTM model is first employed to predict the hub-fixed tilt and yaw moments ahead of time. These predicted values are then introduced as feedforward signals and combined with the conventional speed-based pitch control signal as well as a proportional feedback term. After that, the inverse Coleman transformation is used to generate the individual pitch commands for each blade. To verify the effectiveness of the proposed method, co-simulations were carried out in FAST and MATLAB/Simulink on a 5000 KW distributed pitch-controlled wind turbine under IEC Kaimal spectrum wind conditions, with a mean wind speed of 18 m/s and Class B turbulence intensity. The results show that the LSTM prediction model achieves an R² of 0.998 on the test dataset, with an RMSE as low as 0.0051. Compared with the conventional pitch-based power control strategy, the proposed approach maintains the same average power output while significantly reducing fatigue loads, thereby contributing to a longer service life for the wind turbine. Full article

Proportional, fair scheduling in OFDM-based vehicle-to-everything (V2X) uplink causes the resource-block allocation of each vehicle to vary from slot to slot, yet conventional semantic encoders produce a fixed number of output tokens regardless of the instantaneous channel capacity. When the encoder output exceeds the slot budget, transmitted features are truncated and the resulting federated learning gradient is corrupted—a problem that affected 23% of training rounds for non-line-of-sight vehicles in our experiments. The difficulty is worsened by a spatial pattern common in urban deployments: vehicles at congested intersections suffer the poorest propagation conditions while carrying the training data most relevant to safety, and throughput-driven client selection excludes them in favor of vehicles with strong channels but uninformative scenes. We address both issues within a single framework for OFDM-based V2X federated learning. On the transmission side, a Sensing-Guided Adaptive Modulation (SGAM) module derives a per-slot token budget from the current resource-block allocation and selects tokens through differentiable Gumbel-TopK pruning with a hard capacity clip, so the transmitted token count stays within the slot budget. On the scheduling side, a Channel-Decoupled Federated Learning (CDFL) module partitions clients independently by channel quality and data complexity, selects diverse representatives per partition via facility location optimization, and corrects for partition-size imbalance through inverse propensity weighting during model aggregation. Experiments on NuScenes with 20 non-IID vehicular clients under realistic OFDM channel simulation demonstrate a Macro-F1 of 0.710 (+8.7 points over the Oort-adapted baseline), zero budget violations throughout training, and a 75% reduction in training variance; the worst-class F1 more than doubles relative to FedAvg. Full article

Background: This study examines how medical consumption is discussed in online communities among individuals who are blind or visually impaired using the Social Ecological Model (SEM) to capture multilevel healthcare experiences within digital discourse. Methods: A total of 428 posts and comments were collected from Reddit’s r/Blind community. Term frequency–inverse document frequency keyword extraction and a theory-driven LLM-based classification approach were applied to categorize texts into five SEM levels: intrapersonal, interpersonal, institutional, community, and public policy. Results: The findings show that intrapersonal (44.4%) and public policy (29.8%) levels were the most prominent, indicating a strong emphasis on personal coping experiences alongside structural constraints in healthcare access. Institutional-level discourse accounted for 15.8%, whereas interpersonal (6.2%) and community (3.8%) discourse were relatively limited. Keywords and qualitative analyses revealed themes related to emotional adaptation, social support, service accessibility, mobility constraints, and welfare policy barriers. The Jaccard similarity analysis indicated stronger associations between institutional and policy levels, whereas community-level discourse remained relatively distinct. Conclusions: These findings highlight the importance of understanding healthcare experiences, both individually and structurally, in online environments. This study also demonstrated the potential of integrating LLM-based classification with theory-driven frameworks to enable an interpretable and scalable analysis of complex health-related discourse. Full article

Future fusion power plants like DEMO must be remotely maintained for safety, including breeding blankets (BBs) weighing up to 180$\mathrm{t}$. The BB vertical transporter (BBVT), a crane-like redundant robot with 7 joints, has been previously designed for handling the five unique BB segments per sector. This includes grasping, preloading and collision-free spatial manipulation of BB segments in a space-constrained environment, necessitating advanced motion planning and real-time control. To achieve this, the challenge of obtaining accurate and performant inverse kinematic (IK) solutions for the redundant BBVT must be addressed. Therefore, a kinematic model is presented, and the redundant IK probelm is solved analytically for task-relevant cases, including derivation and analysis of the Jacobian. The model is verified by comparison with an MSC Adams model. Meanwhile, the analytical IK is found to be 53× to 84× faster than a gradient projection-based numerical solver in Matlab while providing multiple solutions. The IK and Jacobian are applied to create collision-free waypoints, verified in Matlab, for handling each BB segment while minimizing static joint loads in key configurations. A first-order estimate of the total BB handling time for a maintenance of nine days is calculated. These developments support the feasibility of the BBVT robot for the BB maintenance task in DEMO, and underpin future efforts in modelling dynamics and achieving real-time resilient control. Full article

The primary objective of this research is to quantitatively isolate the complex driving factors of slope deformation and explicitly reveal the long-term creep mechanism induced by early excavation unloading, thereby providing a theoretical basis for long-term stability evaluation. To achieve this, this study adopts a combined approach of multivariate statistical regression and numerical simulation inversion based on long-sequence monitoring data. First, a multivariate statistical regression model incorporating time-dependent, rainfall, temperature, valley width, and excavation components was constructed to quantitatively separate the contribution weights of each factor. Second, by introducing a rock–soil creep constitutive model, a refined finite element model was established to perform back-analysis of creep parameters and numerical simulation. The results indicate that two large-scale slope-cutting excavations were the direct triggers for the deformation, resulting in shear dislocation of the deep ancient sliding zone and superficial slippage. The dominant factors exhibit distinct phasic and spatial differences: before impoundment, the time-dependent component was absolutely dominant (>80%); after impoundment, low-elevation areas were significantly affected by valley width shrinkage (>60%), while high-elevation areas remained dominated by time-dependent deformation (>74%). Numerical simulation confirmed that the nature of the deformation is “excavation unloading-induced creep along the ancient sliding zone,” and the simulation results considering creep effects accurately reproduced the actual deformation characteristics observed in situ. It is concluded that the rheological effects induced by early excavation unloading are central to the control of long-term stability. Full article

Fugitive greenhouse gas (GHG) emissions in industrial parks are characterized by high opacity and spatial dispersion. Existing localization and quantification methods often rely on idealized meteorological assumptions and low-precision mobile monitoring data, making it difficult to achieve accurate source characterization. This study focuses on the Nansha Economic and Technological Development Zone in Guangzhou—one of the first pilot zones for synergistic pollution and carbon reduction in China—to develop an atmospheric inversion model based on multi-site fixed monitoring. By integrating GHG concentrations with multi-dimensional meteorological parameters, the model couples an atmospheric dispersion framework with a Bayesian inversion algorithm. Specifically, site-specific conditions and high-frequency meteorological data are utilized to constrain dispersion parameters, effectively reducing model uncertainty driven by meteorological variability. Within the Bayesian framework, the model enables the simultaneous inversion of both the locations and emission strengths of multiple sources. Results identified three distinct fugitive emission sources: one primary source in the International Auto Industrial Park with a CO₂ emission intensity of 103.15 g/s and two sources in the Western Industrial Park with intensities of 0.051 g/s and 0.26 g/s, respectively. Overall, this research framework significantly enhances the accuracy and spatial resolution of emission inversion, providing robust technical support for precision carbon management and the development of targeted mitigation strategies for key industrial processes. Full article

Leaks in pipe systems result in significant economic losses, environmental hazards, and public health risks. Transient-based leak detection methods, which exploit the dynamics of pressure waves in response to system anomalies, have emerged as efficient techniques for identifying and characterizing leaks in pressurized pipelines. These methods offer distinct advantages, including minimal data requirements, high sensitivity to low-pressure anomalies, and resilience to the ill-posed conditions often affecting steady-state models. This paper reviews transient-based leak detection, synthesizing findings from over 139 peer-reviewed publications spanning the past three decades. The review categorizes transient-based methods into transient damping, transient reflection, system response, and inverse transient methods, analyzing the prevalence, evolution, and research rate of each category over time. By structuring the review around key aspects such as simulation domain type, analysis approach, system response, solver strategies, adaptability to noise, viscoelasticity, and network complexity, this paper identifies significant trends and shifts in research focus. A comprehensive tabular dataset of 139 studies captures how research activity in various areas has accelerated, slowed, or reached stability, offering insights into the evolving priorities within the field. This review highlights areas for further development, particularly in addressing AI-enhanced applications, transient excitation and measurement sites design, noise resilience, comprehensive leak characterization, validation approaches, and scalability for complex network applications, providing a resource to guide future research in transient-based leak detection. Full article

Background: Metabolic dysregulation is increasingly recognized as a contributor to carcinogenesis; however, the role of circulating lipid traits in ovarian cancer remains unclear. Methods: A systematic review and meta-analysis were conducted following PRISMA 2020 guidelines. PubMed, Web of Science, Scopus, and Embase were searched from inception to March 2026. Observational studies evaluating triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and total cholesterol (TC) in relation to ovarian cancer risk were included. Random-effects models were used to pool relative risks (RRs). Robustness was assessed via sensitivity analyses, influence diagnostics, and multiverse analysis. Mendelian randomization (MR) evidence was integrated for causal inference. Results: Six observational studies were included in the meta-analysis. Elevated triglyceride levels were associated with increased ovarian cancer risk, while HDL-C showed a modest inverse association. LDL-C and total cholesterol were not significantly associated with risk. Sensitivity analyses excluding early follow-up strengthened the triglyceride association. MR analyses supported a potential causal role for triglycerides but not for HDL-C. Conclusions: Circulating triglycerides may represent a metabolically relevant risk factor for ovarian cancer. Further large-scale prospective and mechanistic studies are warranted. Full article

In this research paper, we introduce an improved multiple-component decomposition technique based on the refined volume scattering models (MCSMRV) for polarimetric interferometric synthetic aperture radar (PolInSAR) system. The primary objective of this methodology is to address the issue of overestimation in volume scattering (OVS) and to clarify the mixed ambiguities associated with scattering mechanisms. Our approach incorporates an innovative inversion technique for rotation angles in urban areas, alongside the newly proposed volume scattering models. Furthermore, a refined Wishart mixture model (RWMM) is proposed for distinguishing building regions from non-building regions, which can effectively support the rational selection of volume scattering models. Additionally, the polarimetric interferometric similarity parameter (PISP) is employed to modify the volume scattering models for buildings with diverse orientation angles. To validate the effectiveness of MCSMRV, we utilize ESAR PolInSAR data and the PolInSAR data collected by the Aerospace Information Research Institute. Various mathematical methods are applied to assess the performance of MCSMRV. The experimental results clearly demonstrate that MCSMRV represents a robust method for characterizing the scattering mechanisms across different terrain types. Full article

Background/Objectives: Hypoalbuminaemia is a consistent predictor of mortality in sepsis; however, the temporal dynamics of albumin decline and its relationship with fluid exposure and early norepinephrine therapy remain incompletely characterised. Determining whether early norepinephrine use is associated with attenuation of albumin loss could inform fluid management strategies and identify therapeutic windows for combined vasopressor–albumin interventions. The study aimed to assess whether serum albumin trajectories in sepsis are associated with fluid exposure, modulated by early norepinephrine therapy, and related to 30-day mortality. Methods: We conducted a prospective longitudinal study of patients admitted to an intermediate care unit (IMCU) with community-acquired sepsis. Serum albumin concentrations, cumulative fluid balance (CFB), and vasopressor use were recorded during the first 5 days of hospitalisation. Longitudinal mixed-effects and segmented linear models assessed the association of CFB and vasopressor therapy with albumin trajectories. Lagged mediation modelling explored the potential mediating role of albumin in the association between fluid exposure and 30-day mortality. Results: A total of 389 patients with community-acquired sepsis were included. Thirty-day mortality was 18%. Mean serum albumin at baseline was 2.58 g/dL and declined early to 2.24 g/dL at 72 h. Serum albumin was inversely correlated with cumulative fluid balance over time (r ranging from −0.235 to −0.348; p < 0.001). In longitudinal models, each 1% increase in ΔCFB was associated with a −0.029 g/dL decrease in serum albumin (p < 0.001), supporting an independent effect of fluid exposure. Before norepinephrine initiation, the albumin slope was −0.043 g/dL per interval and was −0.008 g/dL after vasopressor initiation (interaction p = 0.012). Lower albumin concentrations at 72 h predicted 30-day mortality (OR 1.49 per 0.5 g/dL decrease), and serum albumin mediated 18.6% of the association between fluid exposure and mortality. Conclusions: Cumulative fluid exposure was associated with a progressive decline in serum albumin in patients with community-acquired sepsis. Early norepinephrine initiation was associated with attenuation of this trajectory, consistent with the hypothesis that vasopressor-guided haemodynamic stabilisation may limit fluid-associated albumin loss. Full article