Search Results (5,015)

Expansive agents (CaO, MgO, C₄A₃Š) were incorporated into coal gangue-slag-fly ash based geopolymer (CSFG). The influence of expansive agents on the properties and microstructure of CSFG was investigated by macroscopic tests including setting time, compressive strength, and shrinkage values, along with microstructural tests including XRD, FTIR, SEM-EDS, and BET. Results showed that CaO and MgO added separately and their combination exhibited similar trends, with CaO added separately yielding the most favorable outcome. In comparison to the control group, the sample with 7% CaO reduced initial and final setting times by 43.6% and 52.8%, increased 28 d compressive strength by 12.6%, and decreased 28 d drying shrinkage and autogenous shrinkage values by 43.5% and 29.9%, respectively. Moderate MgO and CaO enhanced dissolution of precursors (e.g., coal gangue, fly ash), promoting formation of C-A-S-H gel, CaCO₃, and periclase. Incorporating 3% C₄A₃Š shortened initial and final setting times by 41.3% and 17.8%, improved 28 d compressive strength by 32.2%, but increased 28 d drying and autogenous shrinkage values by 58.3% and 12.8%. Exceeding 3% content significantly reduced 3 d strength. Excessive C₄A₃Š promoted rapid ettringite (AFt) formation, leading to microcracking. Correction prediction models for drying shrinkage strain and autogenous shrinkage strain of CSFG were developed, demonstrating good agreement between predictive and actual values. Full article

In this paper, we propose a novel integrated model architecture, called a level-of-detail (LoD)-aware convolutional vision transformer network (LCvT). It is designed to enhance digital twin (DT) synchronization by effectively integrating LoD awareness in DTs through hierarchical image classification. LCvT employs a vision transformer (ViT)-based backbone coupled with dedicated branch networks for each LoD. This integration of ViT and branch networks ensures that key features are accurately detected and tailored to the specific objectives of each detail level while also efficiently extracting common features across all levels. Furthermore, LCvT leverages a coarse-to-fine inference strategy and incorporates an early exit mechanism for each LoD, which significantly reduces computational overhead without compromising accuracy. This design enables a single model to dynamically adapt to varying LoD requirements in real-time, offering substantial improvements in inference time and resource efficiency compared to deploying separate models for each level. Through extensive experiments on benchmark datasets, we demonstrate that LCvT outperforms existing methods in accuracy and efficiency across all LoDs, especially in DT synchronization scenarios where LoD requirements fluctuate dynamically. Full article

The placement of Daphne genkwa has long been controversial, as its intermediate morphological traits blur the boundary between Daphne and Wikstroemia. To address this challenge, we adopted a chemotaxonomic approach, integrating flavonoid contents with ecological indicators, as an independent line of evidence complementing morphology and molecular data. Using UPLC-UV, six flavonoids were quantified from 16 Korean populations representing six taxa. Multivariate analyses clearly distinguished Daphne and Wikstroemia, with D. genkwa and W. ganpi forming closely related but separate clades. Ecological factors such as precipitation and canopy openness significantly affected flavonoid levels, particularly luteolin 7-O-glucoside and yuankanin. However, the diagnostic flavonoid fingerprints remained consistent across habitats. This study demonstrates that integrating chemical and environmental perspectives can strengthen taxonomic frameworks and support both classification and chemotaxonomic evidence in Thymelaeaceae, offering a methodological basis for future comparative studies across related plant families. Full article

Marine animal segmentation aims at segmenting marine animals in complex ocean scenes, which plays an important role in underwater intelligence research. Due to the complexity of underwater scenes, relying solely on a single RGB image or learning from a specific combination of multi-model information may not be very effective. Therefore, we propose a uniform multi-modal feature extraction and adaptive local–global feature fusion structure for RGB-X marine animal segmentation. It can be applicable to various situations such as RGB-D (RGB+depth) and RGB-O (RGB+optical flow) marine animal segmentation. Specifically, we first fine-tune the SAM encoder using parallel LoRA and adapters to separately extract RGB information and auxiliary information. Then, the Adaptive Local–Global Feature Fusion (ALGFF) module is proposed to progressively fuse multi-modal and multi-scale features in a simple and dynamical way. Experimental results on both RGB-D and RGB-O datasets demonstrate that our model achieves superior performance in underwater scene segmentation tasks. Full article

We report the study of seven commercially available rosehip oils (Rosa canina L.) using GC-MS, colorimetry (CIELab), UV-VIS, FTIR, and 3D EEM fluorescence spectroscopy, including using a smartphone spectrometer. GC-MS revealed two groups of oil samples with different chemical constituents: ω-6-dominant with 45–51% α-linolenic acid (samples S1, S2, and S5–S7) and ω-3-dominant with 47–49% α-linolenic, 7.3–19.1% oleic, 1.9–2.8% palmitic, 1.0–1.8% stearic, and 0.1–0.72% arachidic acid (S3, S4). In S1 PUFA content was found to be ~75% with ω-6/ω-3 ≈ 2:1. Favorable lipid indices of AI 0.0197–0.0302, TI 0.0208–0.0304, and h/H 33.0–50.6 were observed. The highest h/H (50.55) was observed in S5 and the lowest TI (0.0208) in S3. FTIR showed characteristic lines at ~3021, 2929/2853, 1749, and ~1370 cm⁻¹, and PCA yielded 60–80% variation and separated S1 from the rest of the samples, while the clusters grouped S5 and S6. The smartphone spectrometer also reproduced the individual differences in sample volumes ≤ 1 µL under 355–395 nm UV excitation. The non-destructive optical markers reflect the fatty acid profile and allow fast low-cost identification and quality control. An integrated control method including routine optical screening, periodic CG-MS verification, and chemometric models to trace oxidation and counterfeiting is suggested. Full article

Background. Physical activity enhances cognitive performance in adolescents, yet the role of circadian timing within the school day remains poorly understood. Purpose. This study examined whether the timing of school-based physical activity (morning, midday, afternoon) influences cognitive performance, subjective alertness, and mood states in early adolescents. Methods. A 12-week crossover intervention was conducted with 102 students (aged 12–13 years) from southern Italy. Each class participated in three 4-week conditions of structured physical activity scheduled in the morning (8:10–9:10), midday (12:10–13:10), and afternoon (15:10–16:10), separated by one-week washouts. Cognitive outcomes (d2-R, Digit Span backward, TMT-A), subjective alertness (KSS), and mood (PANAS-C) were assessed at baseline and after each condition. Analyses employed linear mixed-effects models and repeated-measures ANOVAs, adjusting for sex, BMI, chronotype, and sleep duration. Results. Morning activity produced the strongest improvements in attention (d2-R, η²p = 0.16), working memory (Digit Span backward, η²p = 0.06), processing speed (TMT-A, η²p = 0.08), alertness (KSS, η²p = 0.19), and positive affect (PANAS-C, η²p = 0.05). Midday sessions yielded moderate benefits (d2-R, η²p = 0.09; Digit Span backward, η²p = 0.05; TMT-A, η²p = 0.07; KSS, η²p = 0.09), while afternoon activity showed the weakest or nonsignificant changes (all η²p < 0.05). Chronotype moderated the effects on attention and working memory, with morning types deriving the largest gains. Conclusions. The timing of physical activity is a critical determinant of its cognitive and affective benefits. Incorporating morning exercise into school timetables may represent a low-cost, scalable strategy to optimize both learning readiness and well-being in adolescents. Full article

This paper proposes a radar mainlobe anti-jamming method based on Space-Time Coding (STC) and Blind Source Separation (BSS). Addressing the performance degradation issue of traditional BSS methods under low Signal-to-Noise Ratio (SNR) and insufficient spatial resolution, this study first establishes the airborne SAR imaging geometric model and the jamming signal mixing model. Subsequently, STC technology is introduced to construct more equivalent phase centers and increase the system’s spatial Degrees of Freedom (DOF). Leveraging the increased DOFs, a JADE-based blind source separation algorithm is then employed to separate the mixed jamming signals. The separation of these signals significantly enhances the anti-jamming capability of the radar system. Simulation results demonstrate that the proposed method effectively improves BSS performance. As compared to traditional BSS schemes, this method provides an additional jamming suppression gain of approximately 10 dB in point target scenarios and about 3 dB in distributed target scenarios, significantly enhancing the radar system’s mainlobe anti-jamming capability in complex jamming environments. This method provides a new insight into radar mainlobe anti-jamming by combining the STC scheme and BSS technology. Full article

To investigate diquark correlation in baryons, the baryon spectra with different light–heavy quark combinations are calculated using Gaussian expansion method within both the naive quark model and the chiral quark model. By computing the diquark energies and separations between any two quarks in baryons, we analyze the diquark effect in the $ud$-q/Q, $us$-Q, $ss$-q/Q, and $QQ$-q/Q systems (where $q=u,d$, or s; $Q=c,b$). The results show that diquark correlations exist in baryons. In particular, for $qq$-Q and $QQ$-q systems, the same type of diquark exhibits nearly identical energy and size across different baryons. In the orbital ground states of baryons, scalar–isoscalar diquarks have lower energy and a smaller size compared to vector–isovector diquark, which qualifies them as “good diquarks”. In $QQ$-q systems, a larger mass of Q leads to a smaller diquark separation and a more pronounced diquark effect. In $qq$-Q systems, the separation between the two light quarks remains larger than that between a light and a heavy quark, indicating that the internal structure of such diquarks must be taken into account. A comparison between the naive quark model and the chiral quark model reveals that the introduction of meson exchange slightly increases the diquark size in most systems. Full article

Multi-source information fusion is a key challenge in uncertainty reasoning. Dempster–Shafer evidence theory (D-S evidence theory) offers a flexible framework for representing and fusing uncertain information. However, the classical Dempster’s combination rules may yield counter-intuitive results when faced with highly conflicting evidence. To overcome this limitation, we introduce a cluster-level information fusion framework, which shifts the focus from pairwise evidence comparisons to a more holistic cluster-based perspective. A key contribution is a novel cluster–cluster divergence measure that jointly captures the strength of belief assignments and structural differences between clusters. Guided by this measure, a reward-driven evidence assignment rule dynamically allocates new evidence to enhance inter-cluster separability while preserving intra-cluster coherence. Building upon the resulting structure, we propose a two-stage information fusion algorithm that assigns credibility weights at the cluster level. The effectiveness of the framework is validated through a range of benchmark pattern classification tasks, in which the proposed method not only improves classification accuracy compared with D-S evidence theory methods but also provides a more interpretable, cluster-oriented perspective for handling evidential conflict. Full article

This paper investigates the application of image segmentation techniques in endodontics, focusing on improving diagnostic accuracy and achieving faster segmentation by delineating specific dental regions such as teeth and root canals. Deep learning architectures, notably 3D U-Net and GANs, have advanced the image segmentation process for dental structures, supporting more precise dental procedures. However, challenges like the demand for extensive labeled datasets and ensuring model generalizability remain. Two semi-automatic segmentation workflows, Grow From Seeds (GFS) and Watershed (WS), were developed to provide quicker acquisition of ground truth training data for deep learning models using 3D Slicer software version 5.8.1. These workflows were evaluated against a manual segmentation benchmark and a recent dental segmentation automated tool on three separate datasets. The evaluations were performed by the overall shapes of a maxillary central incisor and a maxillary second molar and by the region of the root canal of both teeth. Results from Kruskal–Wallis and Nemenyi tests indicated that the semi-automated workflows, more often than not, were not statistically different from the manual benchmark based on dice coefficient similarity, while the automated method consistently provided significantly different 3D models from their manual counterparts. The study also explores the benefits of labor reduction and time savings achieved by the semi-automated methods. Full article

Wireless power transfer (WPT) has emerged as a compelling solution for delivering electrical energy without physical connectors, particularly in applications requiring reliability, mobility, or encapsulation. This work presents the modeling, simulation, construction, and experimental validation of an optimized dual-frequency WPT system using magnetically coupled resonant coils. Unlike conventional single-frequency systems, the proposed architecture introduces two independently controlled excitation frequencies applied to distinct transistors, enabling improved resonance behavior and enhanced power delivery across a range of coupling conditions. The design process integrates numerical circuit simulations in PSpice and three-dimensional electromagnetic analysis in ANSYS Maxwell 3D, allowing accurate evaluation of coupling coefficient variation, mutual inductance, and magnetic flux distribution as functions of coil geometry and alignment. A sixth-degree polynomial model was derived to characterize the coupling coefficient as a function of coil separation, supporting predictive tuning. Experimental measurements were carried out using a physical prototype driven by both sinusoidal and rectangular control signals under varying load conditions. Results confirm the simulation findings, showing that specific signal periods (e.g., 8 µs, 18 µs, 20 µs, 22 µs) yield optimal induced voltage values, with strong sensitivity to the coupling coefficient. Moreover, the presence of a real load influenced system performance, underscoring the need for adaptive control strategies. The proposed approach demonstrates that dual-frequency excitation can significantly enhance system robustness and efficiency, paving the way for future implementations of self-adaptive WPT systems in embedded, mobile, or biomedical environments. Full article

In recent years, controlling the integrity of shallow-buried natural gas pipelines within surface subsidence zones caused by high-intensity underground longwall mining in the Daniudi Gas Field of China’s Ordos Basin has emerged as a critical challenge impacting both mine planning and the safe, efficient co-exploitation of coal and deep natural gas resources. This study included field measurements and an analysis of surface subsidence data from high-intensity longwall mining operations at the Xiaobaodang No. 2 Coal Mine, revealing characteristic ground movement patterns under intensive extraction conditions. The subsidence basin was systematically divided into pipeline hazard zones using three key deformation indicators: horizontal strain, tilt, and curvature. Through ABAQUS-based 3D numerical modeling of coupled pipeline–coal seam mining systems, this research elucidated the spatiotemporal evolution of pipeline Von Mises stress under varying mining parameters, including working face advance rates, mining thicknesses, and pipeline orientation angles relative to the advance direction. The simulations further uncovered non-synchronous deformation behavior between the pipeline and its surrounding sand and soil, identifying two distinct evolutionary phases and three characteristic response patterns. Based on these findings, targeted pipeline integrity preservation measures were developed, with numerical validation demonstrating that maintaining advance rates below 10 m/d, restricting mining heights to under 2.5 m within the 260 m pre-mining influence zone, and where geotechnically feasible, the maximum stress of the pipeline laid perpendicular to the propulsion direction (90°) can be controlled below 480 MPa, and the separation amount between the pipe and the sand and soil can be controlled below 8.69 mm, which can effectively reduce the interference caused by mining. These results provide significant engineering guidance for optimizing longwall mining parameters while ensuring the structural integrity of shallow-buried pipelines in high-intensity extraction environments. Full article

This study presents the development of an advanced three-dimensional (3D) bioprinted skin scaffold integrating sodium alginate (SA), gelatin (Gel), human skin-derived decellularized extracellular matrix (dECM), and microbiota-derived postbiotics. To ensure a biocompatible and functional ECM source, human skin samples collected during elective aesthetic surgical procedures were utilized. Following enzymatic treatment, the dermal layer was carefully separated from the epidermis and subjected to four different decellularization protocols. Among them, Protocol IV emerged as the most suitable, achieving significant DNA removal while maintaining the structural and biochemical integrity of the ECM, as confirmed by Fourier-transform infrared spectroscopy. Building on this optimized dECM-4, microbiota-derived postbiotics from Limosilactobacillus reuteri EIR/Spx-2 were incorporated to further enhance the scaffold’s bioactivity. Hybrid scaffolds were then fabricated using 7% Gel, 2% SA, 1% dECM-4, and 40 mg/mL postbiotics in five-layered grid structures via 3D bioprinting technology. Although this composition resulted in reduced mechanical strength, it exhibited improved hydrophilicity and biodegradability. Moreover, antimicrobial assays demonstrated inhibition zones of 16 mm and 13 mm against methicillin-resistant Staphylococcus aureus (MRSA, ATCC 43300) and Pseudomonas aeruginosa (ATCC 27853), respectively. Importantly, biocompatibility was confirmed through in vitro studies using human keratinocyte (HaCaT) cells, which adhered, proliferated, and maintained normal morphology over a 7-day culture period. Taken together, these findings suggest that the engineered hybrid scaffold provides both regenerative support and antimicrobial protection, making it a strong candidate for clinical applications, particularly in the management of chronic wounds. Full article

Objective: Vitamin D deficiency (VDD) is common in pregnancy and may affect lipid metabolism. The underlying mechanisms are multifactorial, but most evidence so far comes from non-pregnant populations. This study aims to identify metabolites and metabolic patterns associated with VDD in early pregnancy and to evaluate their relationships with maternal lipid profiles. Methods: A nested case–control research was carried out in the Zhoushan Pregnant Women Cohort (ZPWC). Cases were defined as women with VDD (25(OH)D < 20 ng/mL), and controls (≥20 ng/mL) were matched 1:1 using propensity scores based on age, pre-pregnancy BMI, gestational week, and calendar year at blood sampling. The untargeted metabolomics of first-trimester maternal plasma were measured. Metabolic profiles were analyzed using partial least squares-discriminant analysis (PLS-DA). Principal component analysis (PCA) was applied to visualize group separation, and metabolite set enrichment analysis (MSEA) was performed to reveal biologically relevant metabolic patterns. Associations between VDD-related metabolite components in early pregnancy and lipid levels in mid-pregnancy were assessed using linear regression models. Results: 44 cases and 44 controls were selected for the study. There were 60 metabolites identified as being connected to VDD. Among these, 26 metabolites, primarily glycerophospholipids and fatty acyls, exhibited decreased levels in the VDD group. In contrast, 34 metabolites showed increased levels, mainly comprising benzene derivatives, carboxylic acids, and organooxygen compounds. PCA based on these metabolites explained 52.8% of the total variance (R²X = 0.528) across the first six principal components (PC1: 16.4%, PC2: 10.6%, PC3: 9.2%, PC4: 6.3%, PC5: 5.7%, PC6: 4.6%). PC2, dominated by lineolic acids and derivatives, was negatively associated with total cholesterol (TC), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C) (all p < 0.01). PC3, dominated by glycerophosphocholines, was negatively associated with TC, TG, and high-density lipoprotein cholesterol (HDL-C) (all p < 0.05). MSEA revealed significant enrichment of the pantothenate and CoA biosynthesis pathway after multiple testing correction (FDR < 0.05). Conclusions: This study reveals distinct metabolic alterations linked to VDD and suggests potential mechanisms underlying its association with maternal lipid metabolism in early pregnancy. Full article

Nitrogen (N) fertiliser recommendations for ratoon sugarcane crops in the Australian subtropics do not specifically account for residual soil N. The present study was undertaken to determine whether residual soil N is sufficiently high to warrant incorporation into current N fertiliser guidelines in subtropical Australia. Nine soil cores were taken to 1 m depth (separated into 0–20, 20–40, 40–60, 60–80 and 80–100 cm layers) in 25 fields in the Australian subtropics after sugarcane harvest and assessed for soil pH, total carbon and nitrogen and mineral N (NO₃⁻ + NH₄⁺) concentrations, and potentially mineralisable N (PMN) in the top 40 cm. Root weight in each soil layer was also measured in one core per field to determine rooting depth. When coupled with 14 d PMN in the top 40 cm, total available N ranged from 44–346 kg N ha⁻¹, which could potentially contribute 30–100% of the typical 150 kg N ha⁻¹ accumulated in shoots of ratoon cane crops in the region. Further field studies are required to determine the actual contributions that these N reserves can make to the N nutrition of ratoon cane crops, and the ramifications of those contributions to fertiliser recommendations. Full article