Search Results (307)

The resummation of Stieltjes series remains a key challenge in mathematical physics, especially when Padé approximants fail, as in the case of superfactorially divergent series. Weniger’s $\delta$-transformation, which incorporates a priori structural information on Stieltjes series, offers a superior framework with respect to Padé. In the present work, the following fundamental question is addressed: Is the $\delta$-transformation, once it is applied to a typical Stieltjes series, capable of correctly simulating the branch cut structure of the corresponding Stieltjes function? Here, it is proved that the intrinsic log-convexity of the Stieltjes moment sequence (guaranteed via the positivity of Hankel’s determinants) allows the necessary condition for $\delta$ to have all real poles to be satisfied. The same condition, however, is not sufficient to guarantee this. In attempting to bridge such a gap, we propose a mechanism rooted in the iterative action of a specific linear differential operator acting on a class of suitable auxiliary log-concave polynomials. To this end, we show that the denominator of the $\delta$-approximants can always be recast as a high-order derivative of a log-concave polynomial. Then, on invoking the Gauss–Lucas theorem, a consistent geometrical justification of the $\delta$ pole positioning is proposed. Through such an approach, the pole alignment along the negative real axis can be viewed as the result of the progressive restriction of the convex hull under differentiation. Since a fully rigorous proof of this conjecture remains an open challenge, in order to substantiate it, a comprehensive numerical investigation across an extensive catalog of Stieltjes series is proposed. Our results provide systematic evidence of the potential $\delta$-transformation ability to mimic the singularity structure of several target functions, including those involving superfactorial divergences. Full article

Pumpkin seedlings serve as rootstocks for watermelon grafting, and the partial leaf trimming operation performed approximately two days before grafting is crucial for the survival rate of grafted watermelon seedlings. Extracting the position of the main veins of the leaf is a prerequisite for achieving automated partial pruning. The existing methods have problems such as low segmentation accuracy and misclassification between primary and branch veins in the pumpkin seedling segmentation task. This study proposes a three-classification segmentation model Dynamic Region Enhancement Transformer (DRE-Former) of main vein, branch vein and background, as well as a post-processing system. The encoder of DRE-Former consists of two modules. The former is Dynamic Frequency Conv and Normalized Efficient Conv (DN Block), which can enhance the feature extraction ability for small targets. The latter is the Region Transformer Block, which enhances the ability to distinguish between the main vein and the branch vein. In addition, in the skip connection part of the model, a Skip Connection Fusion Block (SCF Block) has been added, which can reduce the dilution degree of detailed features. The post-processing section outputs the cutting position and cutting Angle through rule-based methods and geometric analysis. The experimental results show that the proposed model achieves mean Intersection-over-Union (mIoU) and Overall Accuracy (OA) of 90.80% and 95.88%, respectively, outperforming the comparative models. In stability and error testing, the average standard deviation is 0.60, and the average relative error is 11.90%. Compared with the primary mIoU data in the dataset, the average relative error differs by only 2.11%. The post-processing system enables the accurate determination of cutting positions and angles, but it has a strong dependence on the segmentation model. The research can provide reliable technical support for the subsequent automatic cutting equipment for pumpkin seedlings. Full article

Background/Objectives: Tuberculosis remains a significant global health problem, particularly in resource-limited environments. Its mortality and spread can be considerably decreased by early and precise detection via chest X-ray imaging. This study introduces a novel approach based on hybrid deep learning for Tuberculosis detection from chest X-ray images. Methods: The introduced approach combines GhostNet, a lightweight convolutional neural network tuned for computational efficiency, and MobileViT, a transformer-based model that can capture both local spatial patterns and global contextual dependencies. Through such integration, the model attains a balanced trade-off between classification accuracy and computational efficiency. The architecture employs feature fusion, where spatial features from GhostNet and contextual representations from MobileViT are globally pooled and concatenated, which allows the model to learn discriminative and robust feature representations. Results: The suggested model was assessed on two publicly available chest X-ray datasets and contrasted against several cutting-edge convolutional neural network architectures. Findings showed that the introduced hybrid model surpasses individual baselines, attaining 99.52% accuracy on dataset 1 and 99.17% on dataset 2, while keeping low computational cost (7.73M parameters, 282.11M Floating Point Operations). Conclusions: These outcomes verify the efficacy of feature-level fusion between a convolutional neural network and transformer branches, allowing robust tuberculosis detection with low inference overhead. The model is ideal for clinical deployment and resource-constrained contexts due to its high accuracy and lightweight design. Full article

Branched horizontal wells are widely applied in oil and gas development. However, their complex structures make cuttings transport and deposition problems more pronounced. In this study, a three-dimensional branched wellbore model was established based on an intelligent drilling and completion simulation system. A computational fluid dynamics (CFD) approach, incorporating the Eulerian–Eulerian two-fluid model and the kinetic theory of granular flow, was employed to investigate the effects of wellbore diameter, eccentricity, curvature, flow rate, and rheological parameters on cuttings transport behavior. Results from the steady-state simulations indicate that increasing the wellbore diameter and eccentricity intensifies cuttings deposition at the connection section, with the lower-region concentration rising significantly as the eccentricity increases from 0% to 60%. A larger curvature enhances local flow disturbance but reduces the overall cuttings transport efficiency. Increasing the flow rate improves hole cleaning but may promote cuttings accumulation near the bottom of the main wellbore. As the flow behavior index increases from 0.4 to 0.8, the average cuttings concentration rises from 0.0996 to 0.1008, and the pressure drop increases from 1,010,894 Pa to 1,042,880 Pa, indicating improved transport capacity but higher energy consumption. Experimental results are consistent with the numerical simulation trends, confirming the model’s reliability. This study provides both theoretical and experimental support for optimizing complex wellbore structures and drilling fluid parameters. Full article

Three optimization problems based on node-colored undirected graphs are the subject of the present study. These problems model real-world applications in several domains, such as cybersecurity, bioinformatics, and social networks, although they have a similar abstract representation. In all of the problems, the goal is to partition the graph into colorful connected components, which means that in each of the connected components, a color can appear in at most one node. The problems are optimized according to different objective functions, leading to different optimal partitions. We propose a compact Mixed Integer Linear Programming formulation for each of the three problems. These models are based on spanning trees, represented through multi-commodity flows. The compact nature of the new linear models is easier to handle than the approaches that previously appeared in the literature. These were based on models with an exponential number of constraints, which, therefore, required complex solving techniques based on the dynamic generation of constraints within a branch-and-cut framework. Computational experiments carried out on the standard benchmark instances for the problems show the potential of the new compact methods, which, once fed into modern state-of-the-art solvers, are able to obtain results better than the previous algorithmic approaches. As an outcome of the experimental campaign, a dozen instances of the different problems considered are closed for the first time. Full article

This study investigated the influence of cutting techniques on the growth, development, yield, and oil quality of Cannabis sativa found in the Eastern Cape Province. The greenhouse pot experiment was conducted at Dohne Agricultural Development Institute (DADI), Stutterheim, Eastern Cape, during the winter and summer growing seasons of 2024/25. It was laid out in a Randomized Complete Design (RCD) with three treatments replicated three times. The treatments used were herbaceous shoot cutting with two different leaf area (LA) trimming amounts and sexual propagation. The parameters measured were plant height, number of branches, stem girth, number of weeks to first flowering, number of flowers, flower sex, number of weeks to 50% embar colorations, plant fresh weight, leaf and flower weights, and dry leaf and flower weights. The flower oil yield and cannabinoid composition were determined using GC-MS. The results indicate that the sexually propagated plants were taller (p < 0.05) with vigorous growth; had the highest fresh plant, leaf, and dry leaf weights; and had a higher number of male flowers overall. Herbaceous shoot cutting without LA trimming showed a significantly higher numbers of branches and flowers, as well as more rapid flowering, fresh and dry flower weights, and physiological maturity. The highest number of female flowers was recorded from cuttings, irrespective of the cutting technique. Additionally, cannabinoid concentrations in Cannabis sativa oil were influenced by the propagation techniques. In the first growing season, herbaceous shoot cutting with 50% LA trimming had the highest CBD, while in the second growing season, the sexually propagated treatment had the highest CBD concentration. Additionally, herbaceous shoot cutting without LA trimming recorded the highest Δ9-THC concentration, followed by the treatment with 50% LA trimming during the first growing season. These findings indicate that asexual propagation through cuttings is a suitable propagation choice for flower production for pharmaceutical purposes, as female-only plants can be selected. However, sexual propagation should be used for fibre production. Full article

To address the edge blurring issue of drone inspection images of mountainous transmission lines caused by non-uniform haze interference, as well as the low operational efficiency of traditional dehazing algorithms due to increased network complexity, this paper proposes a high-frequency guided dual-branch attention multi-scale hierarchical dehazing network for transmission line scenarios. The network adopts a core architecture of multi-block hierarchical processing combined with a multi-scale integration scheme, with each layer based on an asymmetric encoder–decoder with residual channels as the basic framework. A Mix structure module is embedded in the encoder to construct a dual-branch attention mechanism: the low-frequency global perception branch cascades channel attention and pixel attention to model global features; the high-frequency local enhancement branch adopts a multi-directional edge feature extraction method to capture edge information, which is well-adapted to the structural characteristics of transmission line conductors and towers. Additionally, a fog density estimation branch based on the dark channel mean is added to dynamically adjust the weights of the dual branches according to haze concentration, solving the problem of attention failure caused by attenuation of high-frequency signals in dense haze regions. At the decoder end, depthwise separable convolution is used to construct lightweight residual modules, which reduce running time while maintaining feature expression capability. At the output stage, an inter-block feature fusion module is introduced to eliminate cross-block artifacts caused by multi-block processing through multi-strategy collaborative optimization. Experimental results on the public datasets NH-HAZE20, NH-HAZE21, O-HAZE, and the self-built foggy transmission line dataset show that, compared with classic and cutting-edge algorithms, the proposed algorithm significantly outperforms others in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM); its running time is 19% shorter than that of DMPHN. Subjectively, the restored images have continuous and complete edges and high color fidelity, which can meet the practical needs of subsequent fault detection in transmission line inspection. Full article

To address longstanding challenges in sea buckthorn harvesting—such as the absence of effective harvesting principles, inefficient traditional manual and semi-mechanised methods, and rising labour costs—this study developed a self-propelled harvester equipped with a reciprocating cutter. The harvester featured an optimised double-support reciprocating cutter driven by a swing ring mechanism, with its kinematic parameters and cutting speed determined through analytical analysis. A coordinated transport system, consisting of an arc-shaped branch dial wheel, a conveying device, and a hydraulic system, was also designed. Field experiments were conducted employing a three-factor, three-level Box–Behnken design of Response Surface Methodology (RSM), which enabled the establishment of a predictive mathematical model for harvesting performance. Numerical optimisation via the model yielded the optimal operational parameters: harvesting forward speed of 0.6 m·s⁻¹, a cutting speed of 1.2 m·s⁻¹, and a conveyor belt linear speed of 0.8 m·s⁻¹. With this parameter combination, the missed cutting rate was 6.72%, fruit breakage rate 4.06%, and conveyor failure rate 7.79%, all meeting mechanised harvesting standards. This research provides the essential theoretical foundation and technical solutions for harvesting equipment in the sea buckthorn industry, accelerating its mechanisation process. Full article

Human Pose Estimation (HPE) models have varied applications and represent a cutting-edge branch of study, whose systems such as MediaPipe (MP), OpenPose (OP), and AlphaPose (ALP) show marked success. One of these areas, however, that is inadequately researched is the impact of image degradation on the accuracy of HPE models. Image degradation refers to images whose visual quality has been purposefully degraded by means of techniques, such as brightness adjustments (which can lead to an increase or a decrease in the intensity levels), geometric rotations, or resolution downscaling. The study of how these types of degradation impact the performance functionality of HPE models is an under-researched domaina that is a virtually unexplored area. In addition, current methods of the efficacy of existing image restoration techniques have not been rigorously evaluated and improving degraded images to a high quality has not been well examined in relation to improving HPE models. In this study, we explicitly clearly demonstrate a decline in the precision of the HPE model when image quality is degraded. Our qualitative and quantitative measurements identify a wide difference in performance in identifying landmarks as images undergo changes in brightness, rotation, or reductions in resolution. Additionally, we have tested a variety of existing image enhancement methods in an attempt to enhance their capability in restoring low-quality images, hence supporting improved functionality of HPE. Interestingly, for rotated images, using Pillow of OpenCV improves landmark recognition precision drastically, nearly restoring it to levels we see in high-quality images. In instances of brightness variation and in low-quality images, however, existing methods of enhancement fail to yield the improvements anticipated, highlighting a large direction of study that warrants further investigation and calls for additional research. In this regard, we proposed a wide-ranging system for classifying different types of image degradation systematically and for selecting appropriate algorithms for image restoration, in an effort to restore image quality. A key finding is that in a related study of current methods, the Tuned RotNet model achieves 92.04% accuracy, significantly outperforming the baseline model and surpassing the official RotNet model in predicting rotation degree of images, where the accuracy of official RotNet and Tuned RotNet classifiers were 61.59% and 92.04%, respectively. Furthermore, in an effort to facilitate future research and make it easier for other studies, we provide a new dataset of reference images and corresponding degenerated images, addressing a notable gap in controlled comparative studies, since currently there is a lack of controlled comparatives. Full article

The growing interest in the commercial exploitation of the bioactive components of Dictyota species, including Dictyota kunthii due to its antifungal activity and use in the development of innovative bioproducts, depends on the availability of biomass. In this context, the cultivation of this species emerges as a promising alternative. This study examined thallus fragmentation and ligulae development as methods to produce D. kunthii. Accordingly, thalli were divided into apical, middle, and basal sections to generate the respective tissue fragments, which were cultured under controlled conditions. On the other hand, ligulae development was studied under different conditions of photon flux density (10, 35 and 65 µmol m⁻²s⁻¹); temperature (10, 17 °C); photoperiod (8:16, 12:12, 16:08 h [Light:Dark]), and seawater enrichment:Basfoliar^®, Compo Expert, Krefeld, Germany and von Stosch solutions. The results show that fragmented thalli were non-viable, exhibiting neither wound healing nor regeneration at the cut sites. Furthermore, no buds or new branches were formed. In contrast, ligulae developed under all tested conditions, with nutrients, light, temperature, and photon flux enhancing apical cell formation and branching. We conclude that ligulae can effectively be used as propagules to cultivate fast-growing, branched D. kunthii plantlets. Accordingly, we recommend using a suspended culture system at 17 °C with a 12:12 (Light:Dark) photoperiod and 65 µmol m⁻² s⁻¹ light intensity, as well as adding nutrients (Basfoliar^® at 0.1 mL L⁻¹). Under these conditions, growth rates equal to or exceeding 10% d⁻¹ can be achieved, supporting the feasibility of scaling up to larger volumes for biomass production. Full article

Background: The hemoglobin–albumin–lymphocyte–platelet (HALP) score integrates the immunonutritional and inflammatory status. We evaluated whether baseline HALP predicts the 3-month response after lumbar medial branch radiofrequency ablation (RFA), defined as a Visual Analogue Scale (VAS) reduction of ≥50% and an Oswestry Disability Index (ODI) reduction of ≥40%, and identified a Youden-optimal cut-off. The discrimination and calibration of multivariable models were also assessed. Methods: This single-center retrospective cohort (N = 120) included rigorously selected patients (≥50% pain relief after two comparative medial branch blocks) undergoing standardized RFA. Multivariable logistic regression was adjusted for age, sex, Body Mass Index (BMI), smoking status, paraspinal tenderness, and baseline scores. We quantified the Area Under the Receiver Operating Characteristic Curve (AUC), Hosmer–Lemeshow (HL) goodness-of-fit, Brier score, and calibration slope; optimism was corrected using a 500-bootstrap method. Results: Responses occurred in 64.2% (VAS) and 65.8% (ODI) of participants. HALP independently predicted ODI (OR = 1.06, 95% CI 1.02–1.09; p < 0.001) and VAS (OR = 1.05, 95% CI 1.02–1.08; p = 0.001). As a single predictor, HALP showed fair discrimination (AUC 0.717 [VAS], 0.731 [ODI]). The Youden cut-off of 39.8 yielded high sensitivity (~0.87) with modest specificity (~0.58–0.61). Multivariable AUCs were 0.744 (VAS) and 0.774 (ODI), optimism-corrected to 0.680 and 0.720; calibration was acceptable (HL p > 0.05; slopes ≈ 0.74–0.78; Brier 0.188/0.179). Conclusions: HALP is a simple, low-cost adjunct that independently predicts short-term pain and functional outcomes after lumbar medial branch RFA. Incorporation into post-block triage may refine selection, especially for functional improvement, pending prospective external validation and recalibration of the cut-off. Full article

Deep neural network-based approaches have obtained remarkable progress in monaural speech enhancement. Nevertheless, current cutting-edge approaches remain vulnerable to complex acoustic scenarios. We propose a Symmetric Combined Convolution Network with ConvLSTM (SCCN) for monaural speech enhancement. Specifically, the Combined Convolution Block utilizes parallel convolution branches, including standard convolution and two different depthwise separable convolutions, to reinforce feature extraction in depthwise and channelwise. Similarly, Combined Deconvolution Blocks are stacked to construct the convolutional decoder. Moreover, we introduce the exponentially increasing dilation between convolutional kernel elements in the encoder and decoder, which expands receptive fields. Meanwhile, the grouped ConvLSTM layers are exploited to extract the interdependency of spatial and temporal information. The experimental results demonstrate that the proposed SCCN method obtains on average 86.00% in STOI and 2.43 in PESQ, which outperforms the state-of-the-art baseline methods, confirming the effectiveness in enhancing speech quality. Full article

Background: Plasma branched-chain amino acid (BCAA) concentrations are positively associated with low-density lipoprotein cholesterol (LDL-C) levels. However, the relationship between dietary branched-chain amino acids and hyper-LDL-cholesterolemia is unclear in children and adolescents. Methods: This study explored the correlation between BCAAs and hyper-LDL-cholesterolemia risk through propensity score matching and conditional logistic regression. Machine learning based on LightGBM indicated the important role of BCAAs in the prediction of hyper-LDL-cholesterolemia. To examine the dose–response relationship, Restricted Cubic Splines (RCS) and receiver operating characteristic curves (ROC) were employed. The causal link between BCAA and cardiovascular disease (CVD) was explored via mediation Mendelian randomization. Results: For every 1 g/day increment in the intake of isoleucine, leucine, and valine, there was a corresponding 30%, 11%, and 16% rise in the risk of hyper-LDL-cholesterolemia, respectively. The optimal cut-off values stood at 5.53, 6.40, and 4.18 g/day, respectively. Utilizing the inverse variance weighted method for estimation revealed that the total effect of BCAA on CVD was OR = 1.06 (95% CI: 1.02~1.11), with p = 0.005. The indirect effect, mediated by LDL-C, was OR = 1.02 (95% CI: 1.00~1.02), with p = 0.026. The direct effect was noted at OR = 1.05 (95% CI: 1.01~1.09), with p = 0.017. Conclusions: Dietary BCAAs are positively correlated with hyper-LDL-cholesterolemia in children and adolescents. LDL-C serve as a mediator of CVD caused by BCAAs. Full article

The complex environment along railway lines, characterized by low imaging quality, strong background interference, and densely distributed small objects, causes existing detection models to suffer from low accuracy in practical applications. To tackle these challenges, this study aims to develop a robust and lightweight insulator detection model specifically optimized for these challenging railway scenarios. To this end, we release a dedicated comprehensive dataset named complexRailway that covers typical railway scenarios to address the limitations of existing insulator datasets, such as the lack of small-scale objects in high-interference backgrounds. On this basis, we present CutP5-LargeKernelAttention-SIoU (C5LS), an improved YOLOv8 variant with three key improvements: (1) optimized YOLOv8’s detection head by removing the P5 branch to improve feature extraction for small- and medium-sized targets while reducing computational redundancy, (2) integrating a lightweight Large Separable Kernel Attention (LSKA) module to expand the receptive field and improve contextual modeling, (3) and replacing CIoU with SIoU loss to refine localization accuracy and accelerate convergence. Experimental results demonstrate that it reaches 94.7% in mAP@0.5 and 65.5% in mAP@0.5–0.95, outperforming the baseline model by 1.9% and 3.5%, respectively. With an inference speed of 104 FPS and a model size of 13.9 MB, the model balances high precision and lightweight deployment. By providing stable and accurate insulator detection, C5LS not only offers reliable spatial positioning basis for subsequent defect identification but also builds an efficient and feasible intelligent monitoring solution for these failure-prone insulators, thereby effectively enhancing the operational safety and maintenance efficiency of the railway power system. Full article

Accurate and efficient pest monitoring in complex rice field environments is vital for food security. Existing detection methods often struggle with small targets and high computational redundancy, limiting deployment on resource-constrained edge devices. To address these issues, we propose YOLO-RP, a lightweight and efficient rice pest detection method based on YOLO11n. YOLO-RP reduces model complexity while maintaining detection accuracy. The model removes the redundant P5 detection head and introduces a high-resolution P2 head to enhance small-object detection. A lightweight partial convolution detection head (LPCHead) decouples task branches and shares feature extraction, reducing redundancy and boosting performance. The re-parameterizable DBELCSP module strengthens feature representation and robustness while cutting parameters and computation. Wavelet pooling preserves essential edge and texture information during downsampling, improving accuracy under complex backgrounds. Experiments show that YOLO-RP achieves a precision of 90.62%, recall of 87.38%, mAP@0.5 of 90.99%, and mAP@0.5:0.95 of 63.84%, while reducing parameters, GFLOPs, and model size by 61.3%, 50.8%, and 49.1% to 1.00 M, 3.1, and 2.8 MB. Cross-dataset tests on Common Rice Pests (Philippines), IP102, and Pest24 confirm strong robustness and generalization. On NVIDIA Jetson Nano, YOLO-RP attains 20.8 FPS—66.4% faster than the baseline—validating its potential for edge deployment. These results indicate that YOLO-RP provides an effective solution for real-time rice pest detection in complex, resource-limited environments. Full article