Free radical-scavenging activity and flavonoid contents of Polygonum orientale leaf, stem, and seed extracts

The present study was designed to explore the total flavonoid and taxifolin contents and the radical-scavenging activity of 50% ethanol extracts of Polygonum orientale leaves, stems, and seeds by 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay. The extract with higher total flavonoid content has higher radical scavenging activity. Taxifolin (IC50 = 2.83 μmol/L) has antioxidant activity stronger than that of rutin (IC50 = 3.08 μmol/L). The free radical-scavenging potentials of chloroform, ethyl acetate, water, ethanol, and methanol extracts of Polygonum orientale seeds were also investigated. The free radical-scavenging abilities of various extracts were determined as: methanol > ethanol > water > ethyl acetate > chloroform

Improved uniform error bounds for long-time dynamics of the high-dimensional nonlinear space fractional sine-Gordon equation with weak nonlinearity

In this paper, we derive the improved uniform error bounds for the long-time dynamics of the $d$-dimensional $(d=2,3)$ nonlinear space fractional sine-Gordon equation (NSFSGE). The nonlinearity strength of the NSFSGE is characterized by $\varepsilon^2$ where $0<\varepsilon \le 1$ is a dimensionless parameter. The second-order time-splitting method is applied to the temporal discretization and the Fourier pseudo-spectral method is used for the spatial discretization. To obtain the explicit relation between the numerical errors and the parameter $\varepsilon$, we introduce the regularity compensation oscillation technique to the convergence analysis of fractional models. Then we establish the improved uniform error bounds $O\left(\varepsilon^2 \tau^2\right)$ for the semi-discretization scheme and $O\left(h^m+\varepsilon^2 \tau^2\right)$ for the full-discretization scheme up to the long time at $O(1/\varepsilon^2)$. Further, we extend the time-splitting Fourier pseudo-spectral method to the complex NSFSGE as well as the oscillatory complex NSFSGE, and the improved uniform error bounds for them are also given. Finally, extensive numerical examples in two-dimension or three-dimension are provided to support the theoretical analysis. The differences in dynamic behaviors between the fractional sine-Gordon equation and classical sine-Gordon equation are also discussed

Location-aware Collaborative Filtering for QoS-Based Service Recommendation

Collaborative filtering is one of widely used Web service recommendation techniques. in QoS-Based Web service recommendation, predicting missing QoS values of services is often required. There have been several methods of Web service recommendation based on collaborative filtering, but seldom have they considered locations of both users and services in predicting QoS values of Web services. Actually, locations of users or services do have remarkable impacts on values of QoS factors, such as response time, throughput, and reliability. in this paper, we propose a method of location-aware collaborative filtering to recommend Web services to users by incorporating locations of both users and services. Different from existing user-Based collaborative filtering for finding similar users for a target user, instead of searching entire set of users, we concentrate on users physically near to the target user. Similarly, we also modify existing service similarity measurement of collaborative filtering by employing service location information. after finding similar users and services, we use the similarity measurement to predict missing QoS values based on a hybrid collaborative filtering technique. Web service candidates with the top QoS values are recommended to users. to validate our method, we conduct series of large-scale experiments based on a real-world Web service QoS dataset. Experimental results show that the location-aware method improves performance of recommendation significantly. © 2012 IEEE

A Flexible Electronic Helical Guide Controller

AbstractIn this paper, an Electronic Helical Guide Controller (EHGC) is proposed, for helical gear shaping processes. In most traditional gear shaper machines, the cutter's reciprocating movement is driven by a crank-connecting rod mechanism. Therefore, this study adopts this kind of gear shaper as the machine platform to establish an accurate mathematical model. The control algorithm is embedded in the interpolation module of the CNC system using electronic gearbox techniques to realize special multi-axis linkage control requirements. The crankshaft's angular position is measured and the rotational speed is calculated in each control cycle. The actual position and velocity of the cutter along the Z-axis can be calculated using the geometric relations of the crank-connecting mechanism, and motion in the other axes can be controlled by the electronic gearbox. A special G code with parameters (G83) is also designed and the EHGC control through NC programming is realized in an improvised gear shaping CNC machine. The proposed EHGC is low cost and easy to implement in practice since it does not need a linear grating ruler and a probe on the Z-axis. Furthermore, EHGC allows the flexibility to change a part's helix angle to compensate for distortions caused by heat treatment. Simulations and experiments are performed to verify the effectiveness of the proposed EHGC

An Effective Web Service Recommendation Method based on Personalized Collaborative Filtering

Collaborative filtering is one of widely used Web service recommendation techniques. There have been several methods of Web service selection and recommendation based on collaborative filtering, but seldom have they considered personalized influence of users and services. in this paper, we present an effective personalized collaborative filtering method for Web service recommendation. a key component of Web service recommendation techniques is computation of similarity measurement of Web services. Different from the Pearson Correlation Coefficient (PCC) similarity measurement, we take into account the personalized influence of services when computing similarity measurement between users and personalized influence of services. based on the similarity measurement model of Web services, we develop an effective Personalized Hybrid Collaborative Filtering (PHCF) technique by integrating personalized user-Based algorithm and personalized item-Based algorithm. We conduct series of experiments based on real Web service QoS dataset WSRec [11] which contains more than 1.5 million test results of 150 service users in different countries on 100 publicly available Web services located all over the world. Experimental results show that the method improves accuracy of recommendation of Web services significantly. © 2011 IEEE

GlanceSeg: Real-time microaneurysm lesion segmentation with gaze-map-guided foundation model for early detection of diabetic retinopathy

Early-stage diabetic retinopathy (DR) presents challenges in clinical diagnosis due to inconspicuous and minute microangioma lesions, resulting in limited research in this area. Additionally, the potential of emerging foundation models, such as the segment anything model (SAM), in medical scenarios remains rarely explored. In this work, we propose a human-in-the-loop, label-free early DR diagnosis framework called GlanceSeg, based on SAM. GlanceSeg enables real-time segmentation of microangioma lesions as ophthalmologists review fundus images. Our human-in-the-loop framework integrates the ophthalmologist's gaze map, allowing for rough localization of minute lesions in fundus images. Subsequently, a saliency map is generated based on the located region of interest, which provides prompt points to assist the foundation model in efficiently segmenting microangioma lesions. Finally, a domain knowledge filter refines the segmentation of minute lesions. We conducted experiments on two newly-built public datasets, i.e., IDRiD and Retinal-Lesions, and validated the feasibility and superiority of GlanceSeg through visualized illustrations and quantitative measures. Additionally, we demonstrated that GlanceSeg improves annotation efficiency for clinicians and enhances segmentation performance through fine-tuning using annotations. This study highlights the potential of GlanceSeg-based annotations for self-model optimization, leading to enduring performance advancements through continual learning.Comment: 12 pages, 10 figure

Detaching and Boosting: Dual Engine for Scale-Invariant Self-Supervised Monocular Depth Estimation

Monocular depth estimation (MDE) in the self-supervised scenario has emerged as a promising method as it refrains from the requirement of ground truth depth. Despite continuous efforts, MDE is still sensitive to scale changes especially when all the training samples are from one single camera. Meanwhile, it deteriorates further since camera movement results in heavy coupling between the predicted depth and the scale change. In this paper, we present a scale-invariant approach for self-supervised MDE, in which scale-sensitive features (SSFs) are detached away while scale-invariant features (SIFs) are boosted further. To be specific, a simple but effective data augmentation by imitating the camera zooming process is proposed to detach SSFs, making the model robust to scale changes. Besides, a dynamic cross-attention module is designed to boost SIFs by fusing multi-scale cross-attention features adaptively. Extensive experiments on the KITTI dataset demonstrate that the detaching and boosting strategies are mutually complementary in MDE and our approach achieves new State-of-The-Art performance against existing works from 0.097 to 0.090 w.r.t absolute relative error. The code will be made public soon.Comment: Accepted by IEEE Robotics and Automation Letters (RAL