Search Results (1,758)

Karst springs play a critical strategic role in regional economic and ecological sustainability, yet their spatiotemporal heterogeneity and hydrological complexity pose substantial challenges for flow prediction. This study proposes FMD-mGTO-BiGRU-KAN, a four-stage hybrid deep learning architecture for daily spring flow prediction that integrates multi-feature signal decomposition, meta-heuristic optimization, and interpretable neural network design: constructing an Feature Mode Decomposition (FMD) decomposition layer to mitigate modal aliasing in meteorological signals; employing the improved Gorilla Troops Optimizer (mGTO) optimization algorithm to enable autonomous hyperparameter evolution, overcoming the limitations of traditional grid search; designing a Bidirectional Gated Recurrent Unit (BiGRU) network to capture long-term historical dependencies in spring flow sequences through bidirectional recurrent mechanisms; introducing Kolmogorov–Arnold Networks (KAN) to replace the fully connected layer, and improving the model interpretability through differentiable symbolic operations; Additionally, residual modules and dropout blocks are incorporated to enhance generalization capability, reduce overfitting risks. By integrating multiple deep learning algorithms, this hybrid model leverages their respective strengths to adeptly accommodate intricate meteorological conditions, thereby enhancing its capacity to discern the underlying patterns within complex and dynamic input features. Comparative results against benchmark models (LSTM, GRU, and Transformer) show that the proposed framework achieves 82.47% and 50.15% reductions in MSE and RMSE, respectively, with the NSE increasing by 8.01% to 0.9862. The prediction errors are more tightly distributed, and the proposed model surpasses the benchmark model in overall performance, validating its superiority. The model’s exceptional prediction ability offers a novel high-precision solution for spring flow prediction in complex hydrological systems. Full article

Fluorescence microscopy performance can be significantly enhanced with image post-processing algorithms, particularly deconvolution techniques. These methods aim to revert optical aberrations by deconvolving the image with the point spread function (PSF) of the microscope. However, analytical deconvolution algorithms are computationally demanding, time-consuming, and require precise PSF estimation and careful parameter selection for optimal results. This paper introduces NeuroDecon, a neural network-based method for volumetric deconvolution of confocal images with residual blocks and U-net based architecture. NeuroDecon employs a training strategy that implicitly incorporates the experimental PSF, which acts as a “fingerprint” of system aberrations. This open-source approach allows for personalized training dataset generation, enabling its wide usage for various applications, reduces imaging artifacts and improves computational efficiency. NeuroDecon network outperforms analytical deconvolution methods in image restoration, resolution, and signal-to-noise ratio enhancement and facilitates further data analysis with methods based on automatic segmentation, including protein cluster detection, endoplasmic reticulum network, and dendritic spine 3D-morphology analysis. Full article

Graphoepitaxial directed self-assembly (DSA) of block copolymers (BCPs) has emerged as a promising strategy for sub-30 nm line spacing patterning in semiconductor nanofabrication. Among the available BCP systems, polystyrene-block-poly (methyl methacrylate) (PS-b-PMMA) has been extensively utilized due to its well-characterized phase behavior and compatibility with standard lithographic processes. However, achieving a high-fidelity pattern with PS-b-PMMA remains challenging, owing to its limited etch contrast and reliance on UV-assisted degradation for PMMA removal. In this study, we report the synthesis of an acid-cleavable lamellar BCP, PS-N=CH-PMMA, incorporating a dynamic Schiff base (-N=CH-) linkage at the junction. This functional design enables UV-free wet etching, allowing selective removal of PMMA domains using glacial acetic acid. The synthesized copolymers retain the self-assembly characteristics of PS-b-PMMA and form vertically aligned lamellar nanostructures, with domain spacings tunable from 36.1 to 40.2 nm by varying the PMMA block length. When confined within 193i-defined trench templates with a critical dimension (CD) of 55 nm (trench width), these materials produced well-ordered one-space-per-trench patterns with interline spacings tunable from 15 to 25 nm, demonstrating significant line spacing shrinkage relative to the original template CD. SEM and FIB-TEM analyses confirmed that PS-N=CH-PMMA exhibits markedly improved vertical etch profiles and reduced PMMA residue compared to PS-b-PMMA, even without UV exposure. Furthermore, Ohta–Kawasaki simulations revealed that trench sidewall angle critically influences PS distribution and residual morphology. Collectively, this work demonstrates the potential of dynamic covalent chemistry to enhance the wet development fidelity of BCP lithography and offers a thermally compatible, UV-free strategy for sub-30 nm nanopatterning. Full article

Image classification and segmentation are important tasks in computer vision. ResNet and U-Net are representative networks for image classification and image segmentation, respectively. Although many scholars used to fuse these two networks, most integration focuses on image segmentation with U-Net, overlooking the capabilities of ResNet for image classification. In this paper, we propose a novel U-ResNet structure by combining U-Net’s convolution–deconvolution structure (UBlock) with ResNet’s residual structure (ResBlock) in a parallel manner. This novel parallel structure achieves rapid convergence and high accuracy in image classification and segmentation while also efficiently alleviating the vanishing gradient problem. Specifically, in the UBlock, the pixel-level features of both high- and low-resolution images are extracted and processed. In the ResBlock, a Selected Upsampling (SU) module was introduced to enhance performance on low-resolution datasets, and an improved Efficient Upsampling Convolutional Block (EUCB*) with a Channel Shuffle mechanism was added before the output of the ResBlock to enhance network convergence. Features from both the ResBlock and UBlock were merged for better decision making. This architecture outperformed the state-of-the-art (SOTA) models in both image classification and segmentation tasks on open-source and private datasets. Functions of individual modules were further verified via ablation studies. The superiority of the proposed U-ResNet displays strong feasibility and potential for advanced cross-paradigm tasks in computer vision. Full article

Diabetic Retinopathy (DR) is a leading cause of vision impairment globally, underscoring the need for accurate and early diagnosis to prevent disease progression. Although fundus imaging serves as a cornerstone of Computer-Aided Diagnosis (CAD) systems, several challenges persist, including lesion scale variability, blurry morphological patterns, inter-class imbalance, limited labeled datasets, and computational inefficiencies. To address these issues, this study proposes an end-to-end diagnostic framework that integrates an enhanced preprocessing pipeline with a novel deep learning architecture, Hierarchical-Inception-Residual-Dense Network (HIRD-Net). The preprocessing stage combines Contrast Limited Adaptive Histogram Equalization (CLAHE) with Dilated Difference of Gaussian (D-DoG) filtering to improve image contrast and highlight fine-grained retinal structures. HIRD-Net features a hierarchical feature fusion stem alongside multiscale, multilevel inception-residual-dense blocks for robust representation learning. The Squeeze-and-Excitation Channel Attention (SECA) is introduced before each Global Average Pooling (GAP) layer to refine the Feature Maps (FMs). It further incorporates four GAP layers for multi-scale semantic aggregation, employs the Hard-Swish activation to enhance gradient flow, and utilizes the Focal Loss function to mitigate class imbalance issues. Experimental results on the IDRiD-APTOS2019, DDR, and EyePACS datasets demonstrate that the proposed framework achieves 93.46%, 82.45% and 79.94% overall classification accuracy using only 4.8 million parameters, highlighting its strong generalization capability and computational efficiency. Furthermore, to ensure transparent predictions, an Explainable AI (XAI) approach known as Gradient-weighted Class Activation Mapping (Grad-CAM) is employed to visualize HIRD-Net’s decision-making process. Full article

The growing demand for natural and sustainable food preservatives has drawn interest in carbon dots (CDs) derived from plant sources. This study aimed to synthesize CDs from dried German chamomile flowers (DF) and residual biomass (RB) obtained after essential oil extraction using a hydrothermal process. Their characteristics, bioactivities and cytotoxicity were examined. Both DF-CDs and RB-CDs were spherical (7–10 nm), exhibited strong UV blocking properties and tunable fluorescence and were rich in polyphenolic functional groups, especially the –OH group. DF-CDs generally showed higher antioxidant capacity than RB-CDs as assayed by DPPH, ABTS radical scavenging activities, FRAP and metal chelation activity. Both CDs showed antibacterial effects toward pathogenic bacterial strains (Escherichia coli and Listeria monocytogenes) and spoilage bacteria (Shewanella putrefaciens and Pseudomonas aeruginosa) in a dose-dependent manner. Cytotoxicity was assessed in BJ human fibroblasts, and both CDs exhibited high biocompatibility (>88% viability at 1000 µg/mL). When both CDs at 300 and 600 ppm were applied in a precooked baby clam edible portion (PBC-EP) stored at 4 °C, microbial growth, TVB and TMA contents were lower than those of the control. The total viable count was still under the limit (5.8 log CFU/mL) for the sample treated with CDs at 600 ppm up to 9 days, while the control was kept for only 3 days. Furthermore, the lipid oxidation level (PV and TBARS value) of PBC-EP decreased with CD treatment, especially at higher concentrations (600 ppm). Therefore, chamomile-derived CDs could serve as a promising alternative for perishable seafood preservation. Full article

Polymer-modified bitumen is difficult to produce and often separates during storage and transport. In contrast, granular bitumen modifiers offer wide applicability, construction flexibility, and ease of transport and storage. This study involved preparing a multipolymer granulated bitumen modifier with a styrene–butadiene–styrene block copolymer, polyethylene, and aromatic oil. To elucidate the modification mechanism of a multipolymer granulated bitumen modifier on bitumen, the elemental composition of bitumen A and B, the micro-morphology of the modifiers, the changes in functional groups, and the distribution state of the polymers in the bitumen were investigated using an elemental analyzer, a scanning electron microscope, Fourier-transform infrared spectroscopy, and fluorescence microscopy. The effects of the multipolymer granulated bitumen modifier on the physical, rheological, and viscoelastic properties of two types of base bituminous binders were investigated at various dosages. The test results show that the Z_H/C ratio of base bitumen A is smaller than that of base bitumen B and that the cross-linking effect with the polymer is optimal. Therefore, the direct-feed modified asphalt of A performs better than the direct-feed modified asphalt of B under the same multipolymer granulated bitumen modifier content. The loose, porous surface structure of styrene–butadiene–styrene block copolymer promotes the adsorption of light components in bitumen, and the microstructure of the multipolymer granulated bitumen modifier is highly coherent. When the multipolymer granulated bitumen modifier content is 20%, the physical, rheological, and viscoelastic properties of the direct-feed modified asphalt of A/direct-feed modified asphalt of B and the commodity styrene–butadiene–styrene block copolymer are essentially identical. While the multipolymer granulated bitumen modifier did not significantly improve the performance of bitumen A/B at contents greater than 20%, the mass loss rate of the direct-feed modified asphalt of A to aggregate stabilized, and the adhesion effect reached stability. Image processing determined the optimum mixing temperature and time for multipolymer granulated bitumen modifier and aggregate to be 185–195 °C and 80–100 s, respectively, at which point the dispersion homogeneity of the multipolymer granulated bitumen modifier in the mixture was at its best. The dynamic stability, fracture energy, freeze–thaw splitting strength ratio, and immersion residual stability of bitumen mixtures were similar to those of commodity styrene–butadiene–styrene block copolymers with a 20% multipolymer granulated bitumen modifier mixing amount, which was equivalent to the wet method. The styrene–butadiene–styrene block copolymer bitumen mixture reached the same technical level. Full article

Understanding genetic variation in commercially valuable tree species is essential for improving breeding and conservation efforts. This study investigates genetic variation, heritability, and trait relationships in Pterocarpus macrocarpus, a vital hardwood species for Thailand’s reforestation initiatives. We evaluated growth (height and diameter), morphology (biomass dry weight and specific leaf weight), and physiological traits (net photosynthesis [A], transpiration rate [E], and water-use efficiency [WUE]) across 112 open-pollinated families from six natural populations under controlled nursery conditions over 30 weeks. Using a randomised complete block design, variance and covariance analyses were conducted to estimate genetic parameters. Seedling survival reached 95%, confirming favourable conditions for genetic expression. There were significant differences among populations and families within populations in growth and biomass. In contrast, physiological traits showed notable family-level variation (A, E, WUE) and only population effects for WUE. Residual variance was predominant across traits, indicating considerable within-family variation. Growth and biomass exhibited moderate to high heritability (individual: 0.39–1.00; family: 0.61–0.90), while specific leaf weight and shoot-to-root ratio had lower heritability at the individual level. Physiological traits showed low to moderate heritabilities (individual: 0.26–0.43; family: 0.47–0.62), with maternal effects via seed weight significantly influencing early growth. The heritability of height decreased over time, whereas the heritability of diameter remained stable. Strong genetic correlations among growth and biomass suggest the potential for combined selection gains. However, physiological traits show weak or no correlations with growth, highlighting their independent genetic control. Variation at the population level in growth and WUE may reflect adaptive responses to seed-source environments. Our findings support the use of nursery-based screening as a cost-effective method for the early identification of high-quality families. WUE is a promising focus for breeding programs targeting drought-prone regions. This study provides key insights for advancing the genetic improvement and conservation of P. macrocarpus, emphasizing the importance of incorporating physiological traits into breeding and conservation strategies. Full article

This study investigates the potential of corn stover, an abundant agricultural byproduct, as a sustainable additive in concrete masonry units (CMUs). Preliminary trials were conducted to determine the optimal fiber length (~3 mm and ~10 mm), fiber content (0%, 1%, 3%, and 5% by volume), and alkalinization method (soaking in 0.5 M NaOH, KOH, or synthetic concrete pore solution) for corn stover fibers (CSFs). The results indicated that short fibers treated with synthetic concrete pore solution yielded the best compressive strength and workability, and were thus selected for the main study. A novel mixture was developed by replacing 10% of cement with corn stover ash (CSA) and incorporating 1% alkaline-treated CSF by volume. The resulting blocks (termed “Corncrete”) were evaluated for mechanical and durability properties, including strength, water absorption, bulk and surface electrical resistivity, rapid chloride permeability (RCPT), and fire resistance. Compared to conventional CMUs, Corncrete exhibited an 11–13% reduction in 28- and 91-day compressive strength, though the difference was statistically insignificant. Physically, Corncrete had a 4.4% lower bulk density and a 7.9% higher total water absorption compared to the control. However, its water absorption rates at early stages were 32% and 48% lower, indicating better resistance to moisture uptake shortly after exposure. Durability tests revealed a 13.7% reduction in chloride ion permeability and a 33% increase in bulk and surface electrical resistivity after 90 days. Fire performance was comparable between the two mixtures, with both displaying ~10.5% mass loss and ~5% residual strength after high-temperature exposure. These findings demonstrate that Corncrete offers balanced mechanical performance and enhanced durability, making it a viable eco-friendly option for non-structural masonry applications. Full article

Drowning constitutes the leading cause of injury-related fatalities among adolescents. In swimming pool environments, traditional manual surveillance exhibits limitations, while existing technologies suffer from poor adaptability of wearable devices. Vision models based on YOLO still face challenges in edge deployment efficiency, robustness in complex water conditions, and multi-scale object detection. To address these issues, we propose YOLO11-LiB, a drowning object detection model based on YOLO11n, featuring three key enhancements. First, we design the Lightweight Feature Extraction Module (LGCBlock), which integrates the Lightweight Attention Encoding Block (LAE) and effectively combines Ghost Convolution (GhostConv) with dynamic convolution (DynamicConv). This optimizes the downsampling structure and the C3k2 module in the YOLO11n backbone network, significantly reducing model parameters and computational complexity. Second, we introduce the Cross-Channel Position-aware Spatial Attention Inverted Residual with Spatial–Channel Separate Attention module (C2PSAiSCSA) into the backbone. This module embeds the Spatial–Channel Separate Attention (SCSA) mechanism within the Inverted Residual Mobile Block (iRMB) framework, enabling more comprehensive and efficient feature extraction. Finally, we redesign the neck structure as the Bidirectional Feature Fusion Network (BiFF-Net), which integrates the Bidirectional Feature Pyramid Network (BiFPN) and Frequency-Aware Feature Fusion (FreqFusion). The enhanced YOLO11-LiB model was validated against mainstream algorithms through comparative experiments, and ablation studies were conducted. Experimental results demonstrate that YOLO11-LiB achieves a drowning class mean average precision (DmAP50) of 94.1%, with merely 2.02 M parameters and a model size of 4.25 MB. This represents an effective balance between accuracy and efficiency, providing a high-performance solution for real-time drowning detection in swimming pool scenarios. Full article

Textile defect detection technology has become a core component of industrial quality control. With the advancement of artificial intelligence technologies, an increasing number of intelligent recognition methods are being actively researched and deployed in the textile defect detection. To further improve detection accuracy and quality, we propose a new lightweight process named WSF-RTDETR with reduced computational resources. Firstly, we integrated WTConv convolution with residual blocks to form a lightweight WTConv-Block module, which could enhance the capability of capturing detailed features of tiny defective targets while reducing computational overhead. Subsequently, a lightweight slimneck-SSFF feature fusion architecture was constructed to enhance the feature fusion performance. In addition, the Focaler–MPDIoU loss function was presented by incorporating dynamic weight adjustment and multi-scale perception mechanism, which could improve the detection accuracy and convergence speed for tiny defective targets. Finally, we conducted experiments on a textile defect dataset to further validate the effectiveness of the WSF-RTDETR model. The results demonstrate that the model improves mean average precision (mAP50) by 4.71% while reducing GFLOPs and the number of parameters by 24.39% and 31.11%, respectively. The improvements in both detection performance and computational efficiency would provide an effective and reliable solution for industrial textile defect detection. Full article

With the rapid development of low-earth-orbit (LEO) internet satellite constellations, LEO satellites are becoming promising illuminators of opportunity for passive radar. However, the moving satellite platform results in a shifted Doppler frequency and increased Doppler spread of the clutter, leading to decreased clutter suppression performance. In this paper, the clutter model for a LEO satellite-based passive radar is analyzed. Based on the properties of the clutter, a Doppler-frequency-shifted normalized least mean square (LMS) filter is proposed to suppress the clutter. Furthermore, an efficient block adaptive method is introduced for fast implementation. Moreover, a Butterworth filter is designed to filter out the residual clutter. Simulations demonstrate the effectiveness of the proposed method. Full article

Lipases are widely used as biocatalysts in synthetic applications because of their high chemo-, regio-, and enantioselectivities, which play key roles in the synthesis of esters and the resolution of racemates. These biocatalytic steps are essential for the production of various products, including cosmetic ingredients, building blocks in the pharmaceutical and agrochemical industries. In this study, we produced lipases through solid-state fermentation of agricultural by-products and domestic wastes using the fungus Pycnoporus sanguineus. After fermentation, the dried solids containing lipases from P. sanguineus exhibited high catalytic activity. Lipase production was achieved via solid-state fermentation using a substrate composed of wheat bran and sugarcane bagasse supplemented with either residual frying oil or urea, resulting in an enzymatic activity of 24 U mL⁻¹ after 96 h. The resulting P. sanguineus fermentation solids (PSFS) efficiently catalyzed the esterification of capric acid with ethanol, achieving 95% ester conversion within 28 h. Additionally, PSFS proved to be effective in the kinetic resolution of (RS)-1-phenyl-1-ethanol via transesterification with various acyl donors, selectively forming the (R)-enantiomer. This process yielded a 16% conversion to (R)-1-phenylethyl propionate and an enantiomeric ratio (E) exceeding 200 after 72 h. These results demonstrate the potential of PSFS for applications in ester synthesis and resolution of enantiomerically pure sec-alcohols. Full article

Well-killing operations in water-sensitive hydrophilic formations are often complicated by extended well clean-up periods and, in some cases, failure to restore the well’s production potential post-kill. Typical development targets exhibiting these properties include the Neocomian and Jurassic deposits of fields in Western Siberia and Western Kazakhstan. This paper proposes a well-killing method incorporating simultaneous near-wellbore treatment. In cases where heavy oil components (asphaltenes, resins, or paraffins) are deposited in the near-wellbore zone, their removal with a solvent results in post-operation flow rates that exceed pre-restoration levels. For wells not affected by asphaltene, resin, and paraffin deposits, killing is performed using a blocking pill of invert emulsion stabilized with an emulsifier and hydrophobic nanosilica. During filtration into the formation, this emulsion does not break but rather reforms according to the pore throat sizes. Flow rates in such wells typically match pre-restoration levels. The described engineering solution proves less effective when the well fluid water cut exceeds 60%. For wells exhibiting premature water breakthrough that have not yet produced their estimated oil volume, the water source is identified, and water shutoff operations are conducted. This involves polymer-gel systems crosslinked with resorcinol and paraform, reinforced with inorganic components such as chrysotile microdispersions, micro- and nanodispersions of shungite mineral, and gas black. Oscillation testing identified the optimal additive concentration range of 0.6–0.7 wt%, resulting in a complex modulus increase of up to 25.7%. The most effective polymer-inorganic composite developed by us, incorporating gas black, demonstrates high water shutoff capability (residual resistance factor ranges from 12.5 to 65.0 units within the permeability interval of 151.7 to 10.5 mD). Furthermore, the developed composites exhibit the ability to selectively reduce water permeability disproportionately more than oil permeability. Filtration tests confirmed that the residual permeability to oil after placing the blocking composition with graphene is 6.75 times higher than that to water. Consequently, such treatments reduce the well water cut. Field trials confirmed the effectiveness of the developed polymer-inorganic composite systems. Full article

The corticotropin-releasing factor (CRF) and its type 1 receptor (CRF₁R) play a key role in the regulation of the hypothalamic–pituitary–adrenal (HPA) axis. Dysregulation of the HPA axis is associated with congenital adrenal hyperplasia (CAH) and depression. Non-peptide CRF₁R-selective antagonists displayed antidepressant effects on animal models and are used for the management of CAH. To develop novel non-peptide CRF₁R antagonists, we have previously designed and synthesized a series of substituted pyrimidines. Among these analogs, molecule 43 (M43) binds to CRF₁R with the highest affinity. Based on this finding, we selected M43 for further pharmacological characterization in the present study. The results suggest that M43 is a potent CRF₁R antagonist, blocking the ability of the CRF-related agonist, Tyr⁰-sauvagine, to stimulate (1) cAMP accumulation in HEK 293 cells expressing CRF₁R and (2) the proliferation rate of RAW 264.7 macrophages. Computational studies suggest that the antagonist properties of M43 are mostly attributed to its ability to interact with residues in the allosteric pocket of CRF₁R, comprised of the third, fifth, and sixth transmembrane domain residues, which block activation-associated structural rearrangements of the receptor. Our data will be used to design novel non-peptide CRF₁R antagonists for clinical use. Full article