Search Results (37,136)

The rational design of supported ruthenium catalysts for sustainable energy applications requires precise control over metal nanoparticle size, dispersion, and metal–support interactions. This study investigates the influence of mesoporous silica support topology—SBA-15 (2D hexagonal, cylindrical pores), SBA-12 (3D hexagonal structure), and SBA-3 (2D hexagonal)—on the structure and catalytic performance of 1 wt% ruthenium catalysts in CO₂ methanation and gas-phase toluene hydrogenation. Comprehensive characterization by nitrogen physisorption, low- and high-angle X-ray diffraction (XRD), H₂ temperature-programmed reduction (H₂-TPR), CO chemisorption, and transmission electron microscopy (TEM) revealed that support pore architecture dictates ruthenium particle size (1.2 nm for Ru/SBA-15, 2.8 nm for Ru/SBA-3, 4.3 nm for Ru/SBA-12) and dispersion (80%, 35%, 23%, respectively) through geometric confinement effects. Catalytic testing demonstrated contrasting structure–activity relationships: CO₂ methanation exhibited strong structure sensitivity with turnover frequency (TOF) increasing with particle size (Pearson’s r = 0.96), favoring Ru/SBA-3 and Ru/SBA-12 with near-optimal 3–4 nm particles, while toluene hydrogenation showed weaker structure sensitivity, with Ru/SBA-12 achieving the highest TOF owing to its larger particle size and higher crystallinity. These findings underscore the critical importance of tailoring mesoporous support topology to match reaction-specific structure sensitivity, providing fundamental insights for the design of bifunctional catalysts for hydrogenation reactions. Full article

Glucagon-like peptide-1 (GLP-1) is an incretin hormone secreted by intestinal endocrine L cells that activates the GLP-1 receptor (GLP-1R), leading to glucose-dependent insulin secretion and suppression of glucagon release. In recent years, GLP-1R agonists (GLP-1RAs) have become one of the leading therapeutic options for the treatment of type 2 diabetes mellitus; however, for a long time clinically approved GLP-1RAs were limited to peptide drugs unsuitable for oral administration. The discovery of the “first-in-class” small molecule agonist danuglipron in 2018 demonstrated the feasibility of orally available GLP-1RAs and stimulated the development of numerous danuglipron-like compounds, some of which showed increased efficacy over the prototype. In this study, we report the design and synthesis of novel GLP-1RAs based on a regioisomeric danuglipron scaffold, 1H-benzo[d]imidazole-5-carboxylic acid. A series of 35 compounds was synthesized and evaluated in vitro for cytotoxicity and GLP-1R agonistic activity using a cAMP accumulation assay. A potent lead compound 12r (pEC₅₀ = 7.72, pCC₅₀ < 3.60) was found which is a close structural analog of danuglipron with reduced cytotoxicity and excellent selectivity over two other class B GPCRs, including GCGR and GIPR. Despite decreased potency compared to danuglipron, the obtained results hold promise for further optimization and provide valuable structure–activity relationship insights. Full article

A dual-band flexible wearable MIMO antenna with two operating modes, namely low-frequency narrowband and high-frequency broadband, is proposed and investigated in this paper. The antenna is based on a polyimide (PI) flexible printed circuit (FPC) substrate and has a compact size (90 mm × 40 mm × 0.1 mm), enabling easy integration into helmet-mounted devices. The antenna elements are fed by a coplanar waveguide (CPW) and integrated with a ground decoupling structure, achieving an isolation of at least 23.4 dB between the two ports across the entire operating frequency band. In addition, the impedance-matching characteristics of the antenna under bending conditions and the Specific Absorption Rate (SAR) of this MIMO antenna in a 1 g human-tissue model at 3.7 GHz and 4.6 GHz were evaluated. The results indicate that the antenna’s key electromagnetic performance remains relatively stable under bending conditions, and the SAR values comply with international limit requirements, verifying its feasibility for application in head-worn terminals. With an impedance bandwidth of −10 dB, this antenna achieves dual-band coverage at 3.42–3.84 GHz (relative bandwidth of 11.6%) and 4.37–7.80 GHz (relative bandwidth of 56.4%), effectively meeting the requirements of 5G/6G communication frequency bands. Full article

This data descriptor provides a standardized and reproducible subsystem-level representation of the NREL wind turbine gearbox condition monitoring benchmarking dataset. The released records are derived from Healthy (H1–H10) and Damaged (D1–D10) measurement files and include subsystem-level standardized indices (KHI_HS, KHI_IMS, KHI_PL) together with a calibrated 0–1 Gearbox Health Index (GHI). The indices are generated using a fully specified and deterministic feature extraction and aggregation workflow based on established vibration indicators and healthy-referenced normalization. The Zenodo deposit contains machine-readable CSV tables intended to support transparent benchmarking across supervised classification and anomaly detection studies. The proposed GHI is introduced as an interpretable and reproducible reference baseline rather than an optimized diagnostic model. Technical validation demonstrates condition-level separability within the analyzed dataset while emphasizing the descriptive nature of the index. By releasing structured derived records and a documented regeneration procedure, this work enables an implementation-independent comparison of gearbox condition monitoring approaches and supports reproducible evaluation of alternative health index formulations. Full article

Expanded polystyrene (EPS) waste poses a major environmental concern due to its high volume, low density, and resistance to biodegradation. In this study, post-consumer EPS was reprocessed into continuous microfibers by Air Jet Spinning (AJS) using chloroform and chloroform/D-limonene as solvent systems. The effects of polymer concentration, air pressure, and solvent ratio on fiber formation were systematically investigated through rheological and surface tension analyses. The incorporation of 10 vol. % D-limonene improved jet stability and reduced bead formation, attributed to its lower volatility and favorable solubility with EPS, as supported by Hansen solubility parameters. SEM analysis confirmed uniform microfiber formation within a defined processing window. FTIR spectra indicated preservation of the polystyrene chemical structure, while TGA and DSC analyses were used to evaluate thermal behavior and assess potential residual solvent retention, particularly related to D-limonene. The results elucidate the interplay between solvent volatility, solution properties, and fiber morphology, establishing a sustainable processing framework for converting EPS waste into value-added fibrous materials via AJS. This work contributes to the United National Sustainable Development Goals, particularly SDG 12 (Responsible Consumption and Production) by promoting EPS waste valorization, and SDG 13 (Climate Action) through the partial replacement of conventional solvents with sustainable alternative. Full article

The study presents novel photocrosslinked hydrogels based on methacrylated chitosan and methacrylated gelatin/allyl-modified gelatin and compares their properties as drug delivery systems in wound healing applications. The polymers were selected due to their biocompatible, mucoadhesive, cell-interactive properties and flexibility in adjusting their structure, making them suitable candidates for applications that require tissue repair. A range of hydrogel formulations was obtained by modulating the ratio of modified chitosan to two distinct modified gelatins, with photocrosslinking performed using Irgacure 2959 as the photoinitiator. FT-IR analysis, SEM data, and swelling and mechanical properties confirmed the 3D networking and the compatibility between the hydrogel components. Allylic gelatin-based hydrogels present larger pores and a stronger pH-responsive swelling behaviour compared to methacrylated gelatin-based samples, reflecting the higher flexibility of allylic gelatin networks. The hydrogels release bacitracin during the first six hours, with a release profile that follows a non-Fickian diffusion mechanism. Cytocompatibility and wound healing potential were tested in the presence of human and mouse fibroblasts, cells with a pivotal role in the wound healing process. All formulated hydrogels exhibit antioxidant capacity and protein stabilization properties, which are attributed to the presence of chitosan in their composition. The cytocompatibility, in vitro wound healing, and biological properties of the obtained hydrogels, as well as the drug release results, confirm their suitability in wound healing applications. Full article

In this article, we introduce and investigate a novel class of graphs that are called Power Congruence Graph PCGs, which are defined over the vertex set V$=\{0,1,2,\dots ,n-1\}$ where two vertices $a,b\in V$ are adjacent if $a^k\equiv b^k(modm)$ for some modulus $m\in M_p$, where $M_p=\{p,p^2,\dots ,p^t\mid p^t<n\}$. We thoroughly characterize the structural features of these graphs, establishing that each PCG decomposes into a union of $d+1$ complete components, where $d={\displaystyle \frac{p-1}{gcd(k,p-1)}}$. The component sizes are explicitly given for n, p, and k. This decomposition highlights symmetry patterns in the component arrangement, emphasizing connectedness and structural balance. We derive key graph-theoretic metrics such as degree distribution, size, chromatic number, clique number and domination number. We also compute the adjacency and Laplacian matrices, as well as their spectra and associated graph energies to better understand the structural similarities and differences among PCGs with different exponents and prime moduli. This paper offers a systematic framework for comprehending power congruence based graph constructs, integrating number theory with structural and spectral graph theory and illustrating the natural symmetry that underpins these combinatorial structures. Full article

We investigate 3D-printed composite materials composed of a photosensitive polylactic acid (PLA) resin blended with 10% starch and fabricated by Digital Light Processing. We synthesize the 3D-printed composites by incorporating a post-processing stage involving thermomoulding at various temperatures ranging from 50 °C to 150 °C. The composition, structure, and thermal and mechanical performance of the 3D-printed composites are evaluated using infrared spectroscopy (FTIR), Differential Scanning Calorimetry (DSC), synchrotron X-ray microtomography and tensile testing assisted with digital image correlation. Our results indicate that post-treatment influences the mechanical behaviour of the composites, leading to a moderate increase in stiffness while the tensile strength remains slightly reduced compared with the reference condition, particularly when moulding temperatures reach 100 °C. Our 3D printing approach combined with the photosensitive/starch blend provides a cost-effective alternative for obtaining 3D-printed biosourced components, maintaining technical performance at a reasonable cost. Full article

As high-dimensional behavioral datasets grow, interactive 3D visualization is increasingly limited by rendering and annotation bottlenecks rather than data availability. Existing web-based tools often degrade sharply under large node counts, making real-time exploration impractical. We propose a web-based 3D point-cloud visualization system optimized for rendering performance under condition-locked experimental settings (load × guidance). To enable reproducible evaluation without proprietary data, the system uses a synthetic surrogate dataset with clustered structure and three node types (user/attribute/action) and provides guided/free exploration workflows with interaction logging. We report a technical benchmark across two load scales (N = 500 vs. N = 5000) and two modes (guided vs. free). Under the high-load setting (N = 5000), the system maintains real-time rendering performance while supporting interactive selection (point/cluster), tooltips/inspector, and session logging. We discuss practical strategies for controlling on-screen annotations under overload conditions and outline limitations and future work for validating the approach on real-world embeddings. Full article

Introduction: Optical coherence tomography (OCT) is the gold standard for retinal nerve fibre layer (RNFL) assessment; its high cost and limited accessibility hinder widespread use. This study aims to develop deep learning models that generate RNFL thickness maps from fundus images, providing a cost-effective alternative to OCT. Methods: A dataset of 5000 fundus-OCT image pairs from 5000 unique glaucoma patients was used to train and compare the following four U-Net-based deep learning models: ResU-Net, R2U-Net, Nested U-Net, and Dense U-Net. All models were trained for up to 1000 epochs with early stopping (patience = 50 epochs). Performance was evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Fréchet Inception Distance (FID). Results: ResU-Net demonstrated the best performance, achieving MSE = 0.00061, MAE = 0.01877, SSIM = 0.9163, PSNR = 32.19 dB, and FID = 30.08. These results represent a 108% improvement in SSIM and a 67% improvement in PSNR compared to previously published benchmark for this task. Conclusions: This study demonstrates that deep learning models, particularly ResU-Net, can generate high-fidelity RNFL thickness maps from fundus photographs, substantially outperforming prior published benchmarks. This approach represents a potential contribution toward accessible glaucoma assessment, contingent upon prospective clinical validation and regulatory evaluation. Full article

Background: Genetic diversity and genetic differentiation between Gossypium hirsutum-Gossypium barbadense introgression lines (ILs) and early-maturing upland cotton lines are critical for resolving the core breeding contradiction in Xinjiang cotton region: narrow genetic basis of early-maturing cultivars and late maturity of ILs with superior fiber quality. Xinjiang is one of the major cotton-producing regions in China, and breeding high-quality early-maturing upland cotton adapted to local ecological conditions is essential for improving cotton yield and quality. However, the genetic relationship and differentiation between the two types of cotton germplasm remain unclear, which hinders the efficient utilization of germplasm resources in breeding. Therefore, this study aimed to clarify the genetic diversity and differentiation between the two germplasm types and identify key candidate loci related to early maturity and fiber quality, providing support for cotton breeding. Results: Here, we used a 40K Single Nucleotide Polymorphism chip to genotype core cotton germplasm in northern Xinjiang, and analyzed their population structure, genetic diversity and functional SNP loci associated with early maturity and fiber quality. The tested materials were clearly divided into two subgroups (ILs and early-maturing lines). Genetic diversity analysis revealed a significantly narrow genetic basis in the early-maturing subgroup, while the IL subgroup had higher genetic diversity. Specifically, the early-maturing subgroup showed lower nucleotide diversity and polymorphism information content compared with the IL subgroup, indicating that the genetic variation of early-maturing cotton germplasm in northern Xinjiang is relatively limited. A total of 25 non-synonymous SNPs were identified, among which the c.A613G:p.T205A mutation in GH_D09G1484 (mRNA-decapping enzyme 1, DCP1) was a characteristic variation of early-maturing cotton, and a possible non-synonymous mutation in GH_A09G2400 (Heat shock transcription factor A6b, HSFA6B) was associated with fiber development. These two candidate genes were annotated to be involved in plant growth and development, further supporting their potential roles in regulating cotton early maturity and fiber quality. Conclusions: This study clarified the genetic differentiation between the two types of germplasms and identified key candidate loci for early maturity and fiber quality, providing precise molecular markers and theoretical support for breeding high-quality early-maturing upland cotton adapted to Xinjiang’s ecological conditions. The results also highlight the value of Gossypium hirsutum–Gossypium barbadense introgression lines in enriching the genetic basis of early-maturing cotton, which can be further utilized to solve the core breeding contradiction in the Xinjiang cotton region. Full article

In fire emergency ventilation systems, EC (Electronically Commutated) internal-rotor axial fans are critical devices, but their high-speed operation generates aerodynamic noise often exceeding 90 dB (A). While struts are core structural components regulating flow field stability, their specific geometric impact on trailing-edge vortex shedding and noise generation mechanisms remains unclear. This study investigates three strut configurations: a hexagonal annular type, a hexagonal double-ring type, and a three-pronged type. A coupled numerical model was established using Large Eddy Simulation (LES) and the Ffowcs Williams and Hawkings (FW-H) acoustic analogy. The Q-criterion was employed to analyze vortical structures, with numerical predictions validated against experimental measurements in a semi-anechoic chamber. The results quantitatively demonstrate that optimizing the strut geometry significantly mitigates unsteady flow separation. The three-pronged strut (Model C) effectively dispersed high-velocity airflow, reducing the peak turbulent kinetic energy (TKE) at the inlet by 30% compared to the original design (Model a). Furthermore, Model C achieved a 6.7 dB reduction in the sound pressure level at the blade-passing frequency (BPF), alongside a 14.1% reduction in pressure pulsation amplitude near the blade tip. Structural optimization of struts enables synergistic control over turbulence distribution and pressure fluctuations. By disrupting the phase coherence of shed vortices, the optimized design fundamentally suppresses aerodynamic noise, advancing axial fan design toward precise quantitative aeroacoustic optimization. Full article

Lung cancer is one of the most common causes of death from cancer around the world, and catching it early through computed tomography (CT) scans can drastically improve survival. However, automated classification of pulmonary nodule candidates is hard because images do not all have the same intensity across scanners and protocols, resulting in inconsistent performance, more false positives (FP), and a ceiling on how much deep learning models work in an average clinic. In this work, we tackle this by introducing a preprocessing step that corrects intensity differences before feeding images into classification models. We use Contrast-Limited Adaptive Histogram Equalization (CLAHE), but with its key parameters tuned automatically via a modified version of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). This helps to boost local contrast adaptively, keeps important anatomical details intact, and cuts down on noise. We tested the approach on the public LUNA16 dataset, first checking image quality (Peak Signal-to-Noise Ratio (PSNR) around 53 dB and Structural Similarity Index (SSIM) of 0.9, better than standard methods), then training three popular deep models—namely, ResNet-50, EfficientNet-B0, and InceptionV3—with CutMix augmentation for better generalization. On the enhanced images, ResNet-50 achieved up to 99.0% classification accuracy with substantially less FP than when using the raw scans. Taken together, these results demonstrate that intelligent and optimized preprocessing can effectively mitigate intensity variations via deep learning for lung nodule detection, thus coming closer to realizing the practical toolbox of computer-aided diagnosis in routine clinical practice. Full article

Aiming to address the complex coupling of transient impulses and steady-state components in vibration signals of scroll compressors in air conditioning systems, this study proposes a physically driven heterogeneous dual-path adaptive weighting network (PDW-Net). The approach constructs a physics-inspired weighting module based on kurtosis and energy criteria, enabling adaptive reconstruction of transient impulses and steady-state vibration components. Feature extraction and decision-level fusion are achieved through a heterogeneous dual-branch network comprising a Fast Fourier Transform (FFT)-based one-dimensional convolutional neural network (1D-CNN) and a Short-Time Fourier Transform (STFT)-based two-dimensional convolutional neural network (2D-CNN). In experimental validation covering four typical fault conditions—condenser failure, refrigerant deficiency, refrigerant overcharge, and main shaft wear—the PDW-Net achieved an average diagnostic accuracy of 97.87% (standard deviation: 2.60%), with 100% accuracy in identifying refrigerant deficiency and normal operating states, demonstrating significant superiority over existing mainstream methods. Ablation studies reveal that the adaptive weighting mechanism contributes most substantially to performance, as its removal results in a 34.24 percentage point drop in accuracy. Replacing the heterogeneous dual-branch structure with a homogeneous counterpart reduces accuracy by 16.18 percentage points, robustly validating the efficacy of the physics-guided and heterogeneous fusion design. Full article

The use of shotcrete is a critical support technique in ocean engineering structures. However, it often exhibits low chloride and salt erosion resistance under ocean environmental conditions and poor long-term durability. This study employed polypropylene fiber (PF) and basalt fiber (BF) to optimize the shotcrete mix design. Laboratory immersion and salt spray tests simulated chloride ion corrosion environments in the ocean’s underwater and atmospheric zones. The effects of different corrosion mechanisms and varying fiber volume fractions on shotcrete strength and durability were then analyzed. The results indicate that shotcrete demonstrates strong resistance to chloride-induced corrosion in both ocean underwater and atmospheric zones when the volume fractions of PF and BF are 0.2% and 0.1%, respectively. Based on test results from 3D digital microscopy (3D-DM), X-ray diffraction (XRD), and scanning electron microscopy (SEM), the chloride-induced degradation mechanism of hybrid fiber-reinforced shotcrete was analyzed from both mesoscopic and microscopic perspectives. This study offers theoretical support for applying hybrid fiber-reinforced shotcrete in ocean engineering environments. Full article