Search Results (4,719)

Background: Deep medullary vein (DMV) drainage has been suggested as a new biomarker for predicting clinical outcomes in patients with acute ischemic stroke (AIS). We evaluated this hypothesis in patients who received endovascular treatment (EVT) within 24 h of symptom onset. Methods: We performed a retrospective study of consecutive AIS patients at a single institution treated with EVT achieving successful recanalization (final mTICI score ≥2b). DMV drainage was graded on a three-point scale (0-1-2) during the second peak venous phase of mCTA by assessing contrast filling, with grade 2 indicating a favorable DMV profile. Our primary outcomes were functional independence, defined as a modified Rankin Scale (mRS) score of 0–2 at 90 days, and ordinal mRS shift at 90 days. Secondary outcomes were excellent clinical status (mRS 0–1 at 90 days), hemorrhagic transformation, and symptomatic intracranial hemorrhage. We investigated independent associations using multivariable logistic and ordinal regression analyses as appropriate, adjusting for age, sex, baseline mRS, NIHSS at onset, occlusion site, intravenous thrombolysis, onset-to-CT time, and ASPECTS. Results: We included 506 patients; the mean age was 76 years. A favorable DMV profile was present in 394 (78%) patients. We found that DMV doubled the odds of achieving functional independence (OR = 2.22; 95% CI = 1.28–3.85) and was associated with a shift towards better functional outcomes in ordinal regression analysis (cOR = 1.93; 95% CI = 1.24–3.02), whereas we did not find any association between a favorable DMV profile and secondary outcomes. Conclusions: In AIS patients successfully recanalized with EVT, a favorable DMV profile was associated with better functional outcomes. Further investigations may clarify the clinical use and predictive ability of this novel radiological marker. Full article

Non-diffracting beams play crucial roles in the field of free-space optical communication due to their robust resistance to distortion in atmospheric turbulence. As a non-diffracting beam characterized by multiple parameters, the Tricomi beam exhibits great versatility and adjustability, attracting considerable interest recently. In this paper, we study the propagation properties of Tricomi beam in atmospheric turbulence based on the theory of random phase screen. It is found that the performance of Tricomi beam in atmospheric turbulence shows strong dependence on its asymmetric constants, topological charge, and half-cone angle. Meanwhile, the Tricomi beam manifests superior resistance to distortion and spatial mode stability in contrast to the conventional non-diffracting Bessel beam. Our work provides a valuable theoretical foundation for the design and performance optimization of next-generation free-space optical communication systems, potentially enabling enhanced data transmission fidelity over long atmospheric paths. Full article

In the classical Stochastic Subspace Identification (SSI) method, mode selection is primarily based on frequency stability, damping stability, and mode shape similarity using the Modal Assurance Criterion (MAC). However, these criteria are often insufficient for reliable modal identification in high-noise environments. This study advances beyond the classical approach by introducing a multi-criteria optimization framework for mode evaluation. In addition to the conventional frequency and damping assessments utilized in the classical SSI method, the proposed approach incorporates a range of supplementary structural metrics. These include Density, Cosine Similarity Difference (CSD), Damping Stability (DS), Spatial Roughness (SR), Mode Shape Complexity (MSC), Signal Energy Coherence (SEC), and Normalized Modal Difference (NMD). These metrics are computed within specifically optimized windows on the stabilization diagram. By integrating spatial, phase, and energy-based characteristics of mode shapes alongside traditional metrics such as the MAC, the method enables a more comprehensive and robust mode selection process that surpasses the limitations of relying solely on frequency and damping stability. Compared to the classical SSI, the optimized window approach provides a significant advantage by enabling the reliable selection of consistent modes by considering the continuity and multi-criteria coherence of modes across window transitions. As a result, the elimination of noise modes and the reliable separation of structural modes are established on a more systematic basis. To achieve this, a two-stage optimization strategy is implemented: the first stage determines the optimal frequency window width and minimum mode count threshold, while the second stage utilizes a Multi-Criteria Decision Making (MCDM) framework based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm to assign optimized weights to the structural metrics and rank the candidate windows accordingly. As a result, the ideal frequency window is identified based on its TOPSIS score and subsequently validated using the MAC, confirming that the selected window corresponds to reliable structural modes. The framework is validated using long-term in situ measurements from a Roller Compacted Concrete (RCC) dam operating under significant environmental and operational noise. The dataset comprises continuous, high-resolution (200 Hz) vibration recordings collected between 1 July 2023 and 30 October 2024. While the calendar duration is limited to several weeks, the uninterrupted 24 h measurements yield a high-density time-series dataset with substantial information content, enabling a statistically meaningful and robust evaluation of modal identification performance under real-world and noisy conditions. The results reveal that relying solely on traditional selection criteria such as pole density and the MAC can often lead to the identification of spurious modes, particularly in noisy environments. In contrast, the proposed TOPSIS-based multi-criteria decision-making framework incorporates a broader range of structural indicators, balancing frequency, damping, spatial, and energy-related metrics to enhance the consistency and reliability of mode selection. This approach proved effective even under high-noise conditions, successfully distinguishing true structural modes from artificial ones. Application of the TOPSIS method to RCC dam data revealed consistent fundamental frequencies at approximately 5–10 Hz, 10 Hz, and 15 Hz, confirming its robustness and suitability for complex structural monitoring tasks. Full article

The increasing penetration of single-phase photovoltaic (PV) generation and electric vehicle (EV) charging has aggravated phase current asymmetry in low-voltage distribution networks. In contrast to voltage-oriented control strategies, this work focuses directly on mitigating current imbalance at the point of common coupling (PCC). A coordinated control framework based on multi-agent deep deterministic policy gradient (MADDPG) is developed to regulate distributed battery energy storage systems (BESS). The control objective is formulated in terms of the Current Unbalance Factor (IUF), derived from symmetrical component theory. A linearized DistFlow model is embedded in the learning environment to preserve physical consistency while maintaining computational tractability. Device-level constraints, including state-of-charge limits and ramp-rate bounds, are enforced through action projection, whereas network security limits are incorporated via reward penalties. Case studies on a modified residential feeder indicate that coordinated BESS control reduces the peak IUF from 2.75% to 2.50% under the studied operating condition. The maximum dominant-phase current decreases from 125 A to 110 A. The performance is close to that of centralized convex optimization while enabling decentralized real-time execution after offline training. These results suggest that multi-agent reinforcement learning can serve as a feasible alternative for phase imbalance mitigation in distribution networks with high renewable penetration. Full article

With the rapid expansion of wind energy infrastructure, there is an increasing accumulation of wind turbine blade waste (WTBW), which is mainly composed of glass fiber-reinforced thermosetting composites. Due to the irreversible nature of polymer crosslinking, conventional recycling methods remain limited. In this study, plasma vitrification was employed to convert WTBW into a reactive calcium-aluminum-silicate slag suitable for use in geopolymer materials. Plasma treatment at a temperature of approximately 2750 K resulted in the formation of predominantly amorphous vitrified slag (VS). Structural characterization using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS) revealed the spatial heterogeneity of the VS. This heterogeneity was influenced by thermal gradients and varied between samples collected from different slag discharge zones, both vertically and horizontally from the reactor. All VS samples contained between 30 and 89% amorphous phase and 10–55% anorthite, with the proportions varying by sampling location. Chemical stability tests showed the dissolution of calcium and aluminum in acidic media, resulting in a silica-enriched residual structure in which the Ca and Al content decreased to less than 0.5 at.% after 100 days. In contrast, exposure to alkaline media caused only minimal surface reorganization—the addition of 5 wt.% VS to acid-based geopolymers made with two metakaolin precursors resulted in a 35% decrease in the mechanical strength of pure metakaolin-based systems. In contrast, when metakaolin containing illite impurities was used, strength values were similar to those of the reference geopolymer. The results quantitatively demonstrate that plasma-derived slag exhibits composition-dependent reactivity, directly linked to its amorphous content and dissolution behavior. Full article

We introduce a local phase-field framework for spin-entanglement correlations. In this framework, the relevant hidden variable is an internal scalar phase associated with each fermion and derived from two underlying real fields. The fields are assumed to evolve locally in ordinary spacetime. When a particle pair is produced at a common spacetime event, the pair acquires a shared phase-locking condition at creation; after separation, the two internal phases evolve independently and no nonlocal interaction is introduced. Spin measurements by Stern–Gerlach analyzers are modeled as local filtering operations. Each local response depends only on the internal phase carried by the particle and on the orientation of the local analyzer. The local response function A(α,λ) = cos(λ − 2α) is derived from the spinorial transformation law of the underlying real field pair and the projection geometry of the detector interaction; it is not a phenomenological ansatz. From these deterministic local responses we derive an analog correlator. The raw product moment of the continuous detector outputs evaluates to ⟨AB⟩ = −½ cos 2(α − β), which satisfies classical Clauser-Horne-Shimony-Holt (CHSH) bounds. After Pearson normalization—the operationally appropriate correlation measure for continuous analog detector outputs, justified by channel-contrast physics and scale invariance—the normalized correlator yields E(α,β) = −cos 2(α − β), matching the quantum singlet correlator in functional form. When this normalized correlator is inserted into the CHSH expression, it yields the numerical value 2√2. This result is a structural consequence of the reduced marginal variance of continuous response functions relative to the unit-variance dichotomic observables assumed in Bell’s derivation; it does not constitute a violation of Bell’s inequality. The model does not reproduce quantum singlet statistics at the level of binary detector outcomes, where the correlator takes a triangular rather than cosine form. The contribution is therefore ontological and conceptual rather than predictive. The framework preserves parameter independence and no-signaling throughout. It provides a concrete real-field ontology for spin correlations based on internal phase structure, and it demonstrates that the functional form of the quantum singlet correlation can be obtained from a strictly local deterministic description, provided that the detector responses are treated as continuous analog quantities and normalized accordingly. We compare the model with earlier phase-based approaches and discuss experimental configurations—including time-resolved and multi-stage Stern–Gerlach measurements—that could in principle probe the proposed internal-phase dynamics at the pre-registration level. Full article

This study evaluates the environmental performance of 34 single-engine light utility helicopters across five operational phases: ground idle departure, ground idle arrival, takeoff, approach, and landing-takeoff (LTO). A hybrid multi-criteria decision-making (MCDM) framework integrating interval type-2 fuzzy sets with the Analytic Hierarchy Process (AHP), VIKOR, and TOPSIS was applied to ensure robust and reliable assessment. Six criteria: shaft horsepower (SHP), fuel flow, hydrocarbon (HC), carbon monoxide (CO), particulate matter (PM), and nitrogen oxides (NOx) were considered to capture both engine performance and environmental impact, with relative importance determined through AHP. VIKOR generated a compromise ranking, while TOPSIS validated the results. The analysis revealed that the HUGHES 500 (DDA250-C18, A34), HUGHES 501 (DDA250-C20B, A29), and BELL 206B-1 (DDA250-C20, A32) engines achieved the best environmental performance due to low fuel consumption and reduced emissions across NOx, PM, HC, and CO. In contrast, engines such as K-1200 (T53 17A-1, A1) and BELL UH-1H (T53 L13, A2) performed the poorest, with high fuel flow and elevated emissions. Sensitivity analysis showed minimal changes in rankings when the NOx weight was varied, confirming the robustness of the framework. These results highlight that emissions and fuel efficiency are more critical than engine power in determining environmental sustainability. Full article

This study evaluates structure-preserving neural network architectures for learning holonomically constrained mechanical dynamics in Cartesian coordinates. In contrast to methods using reduced coordinates, the full ambient phase space ${\mathbb{R}}^{2n}$ is retained with explicit algebraic constraints $C_i\left(\mathbf{q}\right)=0$ to provide a test bed for constraint-aware learning. The Constraint-Aware Hamiltonian Neural Network (CA-HNN) is proposed, which augments the standard HNN with a dedicated multiplier network ${\lambda }_{\varphi }(\mathbf{q},\mathbf{p})$ for Lagrange multipliers and a composite loss function evaluated on predicted rollouts. The theoretical framework is grounded in the geometry of constrained Hamiltonian systems: the extended phase space ${\mathbb{R}}^{2n+m}$ carries a degenerate antisymmetric structure where an m-dimensional kernel encodes constraint directions, while the symplectic structure emerges on the $2(n-m)$-dimensional reduced manifold $\Sigma$. It is proven that the physical Hamiltonian is conserved on the constraint surface under augmented flow. Benchmarks on a pendulum ($C=x^2+y^2-l^2$), double pendulum, and bead on a parabola ($C=y-x^2$) demonstrate that CA-HNN reduces constraint violations $\parallel C\left(\mathbf{q}\right)\parallel$ by $5\times$ to $2400\times$ compared to standard HNNs. While the best energy conservation is achieved by PINNs, these findings clarify the roles of architectural inductive bias, constraint augmentation, and soft physics regularization. Full article

This study investigated how initial ingot thickness (400 mm vs. 520 mm) influences the microstructure and mechanical properties of Al–Zn–Mg–Cu alloys rolled to 80 mm. The combination of smaller initial thickness and lower total reduction (the 400-L route) results in lower dislocation density and a higher fraction of metastable η′ precipitates after T77 treatment. In contrast, the 520-L route, which involves a larger initial ingot thickness coupled with greater rolling reduction, yields higher dislocation density and a greater proportion of stable η phase. Texture also differs: the 400 mm ingot develops a strong S texture and high anisotropy, whereas the 520 mm ingot exhibits Brass texture and reduced anisotropy. Specifically, cross-rolling plus longitudinal rolling of the 520 mm ingot enhances recrystallization texture, giving a short-transverse yield strength of 528 MPa—within 6% of the longitudinal direction. This work offers valuable insights for controlling anisotropy in large 7xxx aluminum plates. Full article

Background: Risk factors for severe COVID-19 and in-hospital mortality are well described, but it remains unclear whether the same factors predict mortality after hospital discharge. Distinguishing risk profiles across clinical phases may improve patient management and follow-up strategies. Methods: We conducted a retrospective observational cohort study of 595 adults hospitalized with PCR-confirmed SARS-CoV-2 infection in Portugal (September–November 2020). The primary outcome was all-cause mortality during hospitalization and up to 120 days post-discharge. Secondary outcomes included intensive care unit (ICU) admission, maximum disease severity (WHO Clinical Progression Scale), oxygen supplementation, and length of stay. Univariable and multivariable regression analyses were performed using logistic regression for binary outcomes and linear regression for continuous outcomes. Results: Overall mortality was 22.5%, rising from 14.1% in-hospital to 22.5% at 120-day follow-up (p < 0.001), with 37.3% of deaths occurring post-discharge. ICU admission was required in 17.6% of patients and was significantly associated with obesity (OR = 2.12, 95% CI: 1.39–3.23, p < 0.001) and male sex (OR = 1.78, 95% CI: 1.14–2.78, p = 0.010) in univariable analysis. In contrast, post-discharge mortality was associated with longer hospital stay (18.4 vs. 9.9 days, p < 0.001) and a higher prevalence of malignancy (28.0% vs. 13.1%, p = 0.032), but not with ICU admission. In multivariable logistic regression, oxygen supplementation was the strongest predictor of 120-day mortality (OR = 2.50, 95% CI: 1.38–4.51, p = 0.002). Only pulmonary diseases and obesity were independently associated with maximum disease severity. Conclusions: Risk factors for acute COVID-19 severity differ from those for post-discharge mortality. These findings support a phase-specific approach to risk stratification, suggesting that patients with obesity are at increased risk of early respiratory deterioration, while patients with malignancy may benefit from closer post-discharge follow-up regardless of ICU admission status. Full article

Despite extensive research on soil organic carbon in karst regions, the synergistic changes in multiple carbon fractions and their stabilization mechanisms across a complete rocky desertification gradient remain poorly understood. To clarify how soil carbon pools and their drivers change during karst rocky desertification, we selected Kaiyuan City, Yunnan Province, China, as the study area. Total carbon (TC), soil organic carbon (SOC), and their related fractions, including particulate organic carbon (POC), mineral-associated organic carbon (MAOC), iron-bound organic carbon (Fe-OC), calcium-bound organic carbon (Ca-OC), and soil carbon isotopic composition (δ¹³C), were analyzed under different degrees of rocky desertification. SOC and TC followed a nonlinear pattern: increasing from no to potential desertification, decreasing at light and moderate stages, and rising again at the severe stage, indicating a phased response rather than a monotonic decline. POC was lowest under no rocky desertification and increased significantly after desertification occurred, reaching its maximum at the severe stage. MAOC peaked at the potential stage. With increasing rocky desertification severity, POC/SOC increased from no to moderate stages and then slightly decreased, whereas MAOC/SOC generally decreased. Fe-OC and Ca-OC were lowest under no desertification and increased after desertification occurred, pointing to enhanced mineral protection. Soil δ¹³C values under moderate and severe desertification were higher than under no, potential, and light desertification, implying intensified decomposition and a relative increase in C₄ plants. Mean weight diameter (MWD) and geometric mean diameter (GMD) did not differ significantly among rocky desertification stages (p > 0.05). In contrast, fractal dimension (FD) differed significantly only between the light and moderate stages (p < 0.05). Correlation and redundancy analyses showed that soil water content, bulk density, and porosity were the key factors driving variation in SOC and its fractions. These findings provide both a theoretical basis and practical guidance for soil restoration and ecological management in karst faulted basins affected by rocky desertification. Full article

Elemental sulfur frequently coexists with organic polysulfides in environmental samples and laboratory sulfurization experiments, complicating the accurate analysis of sulfur speciation. Reliable methods for selective sulfur removal are therefore required to avoid analytical artifacts. In this study, we systematically evaluated commonly used chemical sulfur removal approaches, including treatment with metallic copper and silver and reaction with tetrabutylammonium sulfite, and compared them with a chromatographic separation method based on C18 reversed-phase silica gel column chromatography. Model organic polysulfides, dimethyl polysulfides, diallyl polysulfides, dibenzyl disulfide, and cyclic polysulfide lenthionine were used to assess method performance under controlled conditions. The results demonstrate that chemical treatments are non-selective and lead to substantial decomposition of organic polysulfides, particularly for longer-chain compounds. In contrast, C18 reversed-phase silica gel column chromatography enables efficient and selective removal of elemental sulfur while preserving the original composition of organic polysulfides, with recoveries in the range of ~90–107%. These findings indicate that commonly applied sulfur removal procedures may introduce significant biases in sulfur speciation analyses. The chromatographic approach presented here provides a reproducible and non-destructive alternative for sample preparation, improving the reliability of studying sulfur speciation and transformation in natural and laboratory systems. Full article

Thoracic diseases represent a significant threat to human health. Chest X-ray imaging, owing to its cost-effectiveness and rapid imaging capabilities, has been widely adopted as a primary diagnostic tool in clinical practice. However, existing models are often susceptible to imbalances in disease label distributions. This study proposes a dual-phase convolutional neural network for the classification of thoracic diseases. In the first phase, matrix operations are employed to extract discriminative features corresponding to each disease label, effectively shifting the classification task from the image domain to a label-specific feature domain. The second phase incorporates feature contrastive loss and feature updating mechanisms to further enhance the model’s generalization capability. The proposed framework was evaluated on three public datasets (CheXpert, REFLACX, and EGD) to verify its consistent performance across diverse data sources. Experimental results demonstrate that our model achieved an AUC of 0.8296, AUPRC of 0.2969, Precision of 0.3943, and F1-score of 0.3301 on our dataset, outperforming existing chest X-ray classification models. These findings indicate that our proposed framework effectively learns label-specific characteristics and captures intrinsic image features associated with each disease label, offering an advanced technical tool for the diagnosis of thoracic diseases. Full article

Clay minerals in coal play a key role in controlling mineralogical composition, geochemical signatures, and the industrial behavior of coal and its combustion residues. This study investigates the occurrence, provenance, and potential applications of clay minerals in Carboniferous ash-rich coals from the Bogatyr, Lenin, and Saradyr coal mines in northeastern Kazakhstan. A total of 60 coal samples were analyzed using XRD, SEM–EDS/BSE, XRF, and ICP-OES following acid leaching. Based on ash yield, 52 samples were classified as coal (<50% ash), while 8 samples were classified as carbonaceous shale or mudstone (>50% ash). Mineralogical assemblages show clear variability among the studied mines. Saradyr samples are strongly quartz-dominated with lower clay proportions, Bogatyr samples exhibit highly heterogeneous quartz–clay–mica assemblages, whereas Lenin samples are relatively more clay-rich and dominated by kaolinite and illite-group minerals. Across all samples, kaolinite is the dominant clay mineral (16.6–46 wt.%), occurring mainly as authigenic pore- and cell-filling aggregates. Minor phases include illite–muscovite (7.1–29.9 wt.%), illite–smectite (up to 7.6 wt.% in Bogatyr), and smectite–montmorillonite (0.4–0.7 wt.%). Clay minerals occur as discrete particles, coatings, and pore fillings, contributing to ash formation; however, their correlation with ash yield is weak (R = 0.03–0.05), reflecting heterogeneous mineral inputs and diagenetic overprinting. All geochemical data are reported on a high-temperature coal ash (HTA) basis (815 °C). Geochemical indices (CIA, CIW, CIX) and Al₂O₃/TiO₂ ratios (1.8–17.4) indicate variable provenance and moderate to high weathering intensity, reflecting mixed mafic to intermediate source rocks. A total of 23 trace elements were identified. Au occurs at trace levels (up to 0.02 ppm), while selected rare earth elements (REE: Ce, Dy, Eu, La, Nd, Sm, Y, Yb) average 0.2–0.3 ppm, indicating negligible economic recovery potential. REEs show a strong positive correlation with clay minerals (r = 0.93), indicating adsorption and minor structural incorporation. In contrast, Au correlates with As, V, Zn, Cu, Ni, and Nb, suggesting sulfide association. HTA is enriched in SiO₂–Al₂O₃ phases dominated by kaolinite and quartz, indicating strong potential for cement, geopolymer, ceramic, and zeolite applications. Full article

Previous studies have established that insight enhances memory across a variety of tasks. However, it remains unclear whether memory performance differs between perceptual and semantic insights. To address this issue, this study employed a learning–testing paradigm with Chinese riddles. In the learning phase, individuals judged whether they had grasped the relationship between each riddle and its solution (a Chinese character) under four conditions created by crossing match status (matched vs. unmatched) with riddle type (visual-rule vs. semantic-rule). During the testing phase, they performed immediate and delayed recognition tasks, judging whether each presented character was old or new. The results showed that, relative to trials on which riddles were not solved (primarily unmatched riddles), participants exhibited higher hit rates, recognition rates, and retrospective confidence judgment (RCJ) in both immediate and delayed recognition tasks when they experienced sudden insight into matched riddles. This reflects a memory advantage for insight induced by solution-appropriate processing. Moreover, hit rates, recognition rates, and memory confidence judgment accuracy were consistently higher for semantic insight than for perceptual insight, regardless of match condition (significant main effects with no interaction). In contrast, memory confidence judgment was higher for semantic (vs. perceptual) insight under matched conditions but not under unmatched conditions (significant interaction). Collectively, these findings suggest that insight yields better memory performance when it operates at the semantic level than at the perceptual level. Full article