A New Approach to Designing the Conical Point of a Twist Drill

The point geometry of a twist drill is the most significant part which may affect to cutting performance in the drilling process. However, it is really complicated to establish an exact mathematic equation to represent the conical flank surface of a twist drill. In order to simplify the problem and meet the practical engineering demand in the industry, this research focuses on developing an approximate mathematic model of the conical drill point geometry. Additionally, an integrated CAD/CAM software is developed. This software integrates the modelling function of flute, margin, point and split features and is able to calculate grinding path of each feature. With the help of this software, drill geometric parameters can be modified reasonably according to different requirements easier than ever. Finally, this research also mentions a CAD/CAM/CAE application to evaluate the cutting performance of a twist drill. The designed 3D model can be imported in Thridwave to predict cutting force, torque and peak temperature during the drilling process. Based on these simulative factors, the cutting performance of a twist drill can be generally evaluated. Four evaluated designs were selected and ground by 5-axis CNC grinding machine. The geometry of the ground drill shows a good agreement with the dimension of each design parameter, which validates the accuracy of the proposed modelling method and the corresponding grinding path

ANALYSIS OF MUSCLE STRENGTH CHARACTERISTICS FOR FLEXION AND EXTENSION OF THE KNEE JOINT IN FEMALE CYCLING ATHLETES

The purpose of this study was to analyze the characteristics of muscle strength that is involved in extension and flexion of the knee joint in female cyclists. The flexion and extension exercise of the knee joint is the main source of the muscle power used by the bicycle athlete. It is also one of the subjects which attracts a great deal of attention from scientific researchers and instructors of physical culture both inside and outside of China. For the present study, an advanced CYBEX6000 dynamic testing equipment were used to carry out a considerable amount of research on athletes in various sports events. Based on the published studies from national and international, a specific theory, analysis and exploration were made to the working condition of muscle flexion and extension of the knee joint from the bicycle athlete. The following conclusion was gotten from the comparison between experienced athletes engaged in swimming and boat racing. It was found that athletes engaged in different sports, have different working characteristics of muscle strength from the knee joint. For the experienced bicycle athletes, with the acceleration of rotating speed of the knee joint, the descending degree of the maximum extension muscle torque is much greater than that of the flexion muscle

Impact of Camera Viewing Angle for Estimating Leaf Parameters of Wheat Plants from 3D Point Clouds

Estimation of plant canopy using low-altitude imagery can help monitor the normal growth status of crops and is highly beneficial for various digital farming applications such as precision crop protection. However, extracting 3D canopy information from raw images requires studying the effect of sensor viewing angle by taking into accounts the limitations of the mobile platform routes inside the field. The main objective of this research was to estimate wheat (Triticum aestivum L.) leaf parameters, including leaf length and width, from the 3D model representation of the plants. For this purpose, experiments with different camera viewing angles were conducted to find the optimum setup of a mono-camera system that would result in the best 3D point clouds. The angle-control analytical study was conducted on a four-row wheat plot with a row spacing of 0.17 m and with two seeding densities and growth stages as factors. Nadir and six oblique view image datasets were acquired from the plot with 88% overlapping and were then reconstructed to point clouds using Structure from Motion (SfM) and Multi-View Stereo (MVS) methods. Point clouds were first categorized into three classes as wheat canopy, soil background, and experimental plot. The wheat canopy class was then used to extract leaf parameters, which were then compared with those values from manual measurements. The comparison between results showed that (i) multiple-view dataset provided the best estimation for leaf length and leaf width, (ii) among the single-view dataset, canopy, and leaf parameters were best modeled with angles vertically at -45⸰_ and horizontally at 0⸰_ (VA -45, HA 0), while (iii) in nadir view, fewer underlying 3D points were obtained with a missing leaf rate of 70%. It was concluded that oblique imagery is a promising approach to effectively estimate wheat canopy 3D representation with SfM-MVS using a single camera platform for crop monitoring. This study contributes to the improvement of the proximal sensing platform for crop health assessment. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Two Novel Tyrosinase Inhibitory Sesquiterpenes Induced by CuCl2 from a Marine-Derived Fungus Pestalotiopsis sp. Z233

Two new sesquiterpenes, 1β,5α,6α,14-tetraacetoxy-9α-benzoyloxy-7β H-eudesman-2β,11-diol (1) and 4α,5α-diacetoxy-9α-benzoyloxy-7βH-eudesman-1β,2β,11, 14-tetraol (2), were produced as stress metabolites in the cultured mycelia of Pestalotiopsis sp. Z233 isolated from the algae Sargassum horneri in response to abiotic stress elicitation by CuCl2. Their structures were established by spectroscopic means. New compounds 1 and 2 showed tyrosinase inhibitory activities with IC50 value of 14.8 µM and 22.3 µ

Flow-Attention-based Spatio-Temporal Aggregation Network for 3D Mask Detection

Anti-spoofing detection has become a necessity for face recognition systems due to the security threat posed by spoofing attacks. Despite great success in traditional attacks, most deep-learning-based methods perform poorly in 3D masks, which can highly simulate real faces in appearance and structure, suffering generalizability insufficiency while focusing only on the spatial domain with single frame input. This has been mitigated by the recent introduction of a biomedical technology called rPPG (remote photoplethysmography). However, rPPG-based methods are sensitive to noisy interference and require at least one second (> 25 frames) of observation time, which induces high computational overhead. To address these challenges, we propose a novel 3D mask detection framework, called FASTEN (Flow-Attention-based Spatio-Temporal aggrEgation Network). We tailor the network for focusing more on fine-grained details in large movements, which can eliminate redundant spatio-temporal feature interference and quickly capture splicing traces of 3D masks in fewer frames. Our proposed network contains three key modules: 1) a facial optical flow network to obtain non-RGB inter-frame flow information; 2) flow attention to assign different significance to each frame; 3) spatio-temporal aggregation to aggregate high-level spatial features and temporal transition features. Through extensive experiments, FASTEN only requires five frames of input and outperforms eight competitors for both intra-dataset and cross-dataset evaluations in terms of multiple detection metrics. Moreover, FASTEN has been deployed in real-world mobile devices for practical 3D mask detection.Comment: 13 pages, 5 figures. Accepted to NeurIPS 202

Crop Monitoring Using Sentinel-2 and UAV Multispectral Imagery: A Comparison Case Study in Northeastern Germany

Monitoring within-field crop variability at fine spatial and temporal resolution can assist farmers in making reliable decisions during their agricultural management; however, it traditionally involves a labor-intensive and time-consuming pointwise manual process. To the best of our knowledge, few studies conducted a comparison of Sentinel-2 with UAV data for crop monitoring in the context of precision agriculture. Therefore, prospects of crop monitoring for characterizing biophysical plant parameters and leaf nitrogen of wheat and barley crops were evaluated from a more practical viewpoint closer to agricultural routines. Multispectral UAV and Sentinel-2 imagery was collected over three dates in the season and compared with reference data collected at 20 sample points for plant leaf nitrogen (N), maximum plant height, mean plant height, leaf area index (LAI), and fresh biomass. Higher correlations of UAV data to the agronomic parameters were found on average than with Sentinel-2 data with a percentage increase of 6.3% for wheat and 22.2% for barley. In this regard, VIs calculated from spectral bands in the visible part performed worse for Sentinel-2 than for the UAV data. In addition, large-scale patterns, formed by the influence of an old riverbed on plant growth, were recognizable even in the Sentinel-2 imagery despite its much lower spatial resolution. Interestingly, also smaller features, such as the tramlines from controlled traffic farming (CTF), had an influence on the Sentinel-2 data and showed a systematic pattern that affected even semivariogram calculation. In conclusion, Sentinel-2 imagery is able to capture the same large-scale pattern as can be derived from the higher detailed UAV imagery; however, it is at the same time influenced by management-driven features such as tramlines, which cannot be accurately georeferenced. In consequence, agronomic parameters were better correlated with UAV than with Sentinel-2 data. Crop growers as well as data providers from remote sensing services may take advantage of this knowledge and we recommend the use of UAV data as it gives additional information about management-driven features. For future perspective, we would advise fusing UAV with Sentinel-2 imagery taken early in the season as it can integrate the effect of agricultural management in the subsequent absence of high spatial resolution data to help improve crop monitoring for the farmer and to reduce costs