Hierarchical Structure with Highly Ordered Macroporous-Mesoporous Metal-Organic Frameworks as Dual Function for CO2 Fixation.

As a major greenhouse gas, the continuous increase of carbon dioxide (CO2) in the atmosphere has caused serious environmental problems, although CO2 is also an abundant, inexpensive, and nontoxic carbon source. Here, we use metal-organic framework (MOF) with highly ordered hierarchical structure as adsorbent and catalyst for chemical fixation of CO2 at atmospheric pressure, and the CO2 can be converted to the formate in excellent yields. Meanwhile, we have successfully integrated highly ordered macroporous and mesoporous structures into MOFs, and the macro-, meso-, and microporous structures have all been presented in one framework. Based on the unique hierarchical pores, high surface area (592 m2/g), and high CO2 adsorption capacity (49.51 cm3/g), the ordered macroporous-mesoporous MOFs possess high activity for chemical fixation of CO2 (yield of 77%). These results provide a promising route of chemical CO2 fixation through MOF materials

PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module

LIDAR point clouds and RGB-images are both extremely essential for 3D object detection. So many state-of-the-art 3D detection algorithms dedicate in fusing these two types of data effectively. However, their fusion methods based on Birds Eye View (BEV) or voxel format are not accurate. In this paper, we propose a novel fusion approach named Point-based Attentive Cont-conv Fusion(PACF) module, which fuses multi-sensor features directly on 3D points. Except for continuous convolution, we additionally add a Point-Pooling and an Attentive Aggregation to make the fused features more expressive. Moreover, based on the PACF module, we propose a 3D multi-sensor multi-task network called Pointcloud-Image RCNN(PI-RCNN as brief), which handles the image segmentation and 3D object detection tasks. PI-RCNN employs a segmentation sub-network to extract full-resolution semantic feature maps from images and then fuses the multi-sensor features via powerful PACF module. Beneficial from the effectiveness of the PACF module and the expressive semantic features from the segmentation module, PI-RCNN can improve much in 3D object detection. We demonstrate the effectiveness of the PACF module and PI-RCNN on the KITTI 3D Detection benchmark, and our method can achieve state-of-the-art on the metric of 3D AP.Comment: 8 pages, 5 figure

Lidar Point Cloud Guided Monocular 3D Object Detection

Monocular 3D object detection is a challenging task in the self-driving and computer vision community. As a common practice, most previous works use manually annotated 3D box labels, where the annotating process is expensive. In this paper, we find that the precisely and carefully annotated labels may be unnecessary in monocular 3D detection, which is an interesting and counterintuitive finding. Using rough labels that are randomly disturbed, the detector can achieve very close accuracy compared to the one using the ground-truth labels. We delve into this underlying mechanism and then empirically find that: concerning the label accuracy, the 3D location part in the label is preferred compared to other parts of labels. Motivated by the conclusions above and considering the precise LiDAR 3D measurement, we propose a simple and effective framework, dubbed LiDAR point cloud guided monocular 3D object detection (LPCG). This framework is capable of either reducing the annotation costs or considerably boosting the detection accuracy without introducing extra annotation costs. Specifically, It generates pseudo labels from unlabeled LiDAR point clouds. Thanks to accurate LiDAR 3D measurements in 3D space, such pseudo labels can replace manually annotated labels in the training of monocular 3D detectors, since their 3D location information is precise. LPCG can be applied into any monocular 3D detector to fully use massive unlabeled data in a self-driving system. As a result, in KITTI benchmark, we take the first place on both monocular 3D and BEV (bird's-eye-view) detection with a significant margin. In Waymo benchmark, our method using 10% labeled data achieves comparable accuracy to the baseline detector using 100% labeled data. The codes are released at https://github.com/SPengLiang/LPCG.Comment: ECCV 202

Influence of key parameters of ice accretion model under coexisting rain and fog weather

Based on 30 complete wire icing processes lasted longer than 24 h observed from the Enshi, Jinsha, Dacaoping and Shennongding of Shennongjia in mountainous areas of Hubei province during the winter of 2008–2016, the macroscopic effects of rain–fog weather on the ice accretion process were analyzed. Furthermore, the distribution characteristics of key simulation parameters in supercooled fog (SF) and freezing rain (FR) were discussed according to the physical model of icing process. Finally, the evolution characteristics of the simulated ice thickness in rain–fog weather were proposed. Results showed that the duration of ice accretion in mountainous areas is the key factor affecting the maximum ice thickness; the freezing rain is most frequent during the glaze icing process, which leads to the substantial growth of ice thickness. The average growth rates of ice thickness with and without freezing rain are 1.26 mm h−1 and -0.11 mm h−1, respectively. Collision rate is the main parameter for inhibiting ice accretion of SF, with an average value of ∼ 0.1, while freezing rate is the main parameter for inhibiting ice accretion of FR, with an average value of ∼ 0.6. The ice accretion of SF shows the characteristics of periodic growth, while the ice accretion of FR shows the explosive growth of ice thickness, which makes the simulated values of icing closer to the observations. The ice formation efficiency of FR was more than twice that of SF, with a negative feedback mechanism to the ice accumulation of SF

Cassava genome from a wild ancestor to cultivated varieties

Cassava is a major tropical food crop in the Euphorbiaceae family that has high carbohydrate production potential and adaptability to diverse environments. Here we present the draft genome sequences of a wild ancestor and a domesticated variety of cassava and comparative analyses with a partial inbred line. We identify 1,584 and 1,678 gene models specific to the wild and domesticated varieties, respectively, and discover high heterozygosity and millions of single-nucleotide variations. Our analyses reveal that genes involved in photosynthesis, starch accumulation and abiotic stresses have been positively selected, whereas those involved in cell wall biosynthesis and secondary metabolism, including cyanogenic glucoside formation, have been negatively selected in the cultivated varieties, reflecting the result of natural selection and domestication. Differences in microRNA genes and retrotransposon regulation could partly explain an increased carbon flux towards starch accumulation and reduced cyanogenic glucoside accumulation in domesticated cassava. These results may contribute to genetic improvement of cassava through better understanding of its biology

Contralateral knee osteoarthritis is a risk factor for ipsilateral knee osteoarthritis progressing: a case control study

Abstract Background Knee osteoarthritis (KOA) is a highly disabling disease, and studying its progression is crucial. However, it is still unclear whether the progression of ipsilateral knee osteoarthritis is influenced by contralateral knee osteoarthritis. Methods Data were collected from the OAI database and divided into two study cohorts (right/left KOA cohort). Each cohort had a target knee (right/left knee) and was further divided into two groups (exposure/control group). The demographic data of both cohorts were balanced at baseline by propensity score matching (PSM), and the data included rating scale and radiographic and clinical data. After checking for balance in the matched variables, we then compared the differences between the two groups in each cohort. Our primary focus was on the minimum joint space width (mJSW) of the target knee, which was measured four years after baseline. The secondary outcome was the arthroplasty rate of the target knee within nine years. Results In this study, a total of 678 participants were enrolled and matched. After 1:1 PSM of the baseline demographic data, 98 participants in the right KOA cohort (RKOAC) were successfully matched, and 117 participants in the left KOA cohort (LKOAC) were successfully matched. Furthermore, the standardized mean difference (SMD) of the matched variables in both cohorts was less than 0.25. After analyzing the outcome metrics, we found that the target knee had a significantly lower mJSW in the fourth year after baseline and a significantly greater arthroplasty rate within nine years in the exposed group than in the control group. RKOAC: mJSW (exposure: 2.6(1.1 ~ 3.6) vs. control: 3.3(2.0 ~ 4.2), P < 0.05), arthroplasty rate (exposure: 14(14.3%) vs. control: 4(4.1%), P < 0.05); LKOAC: mJSW (exposure: 3.1(2 ~ 3.9) vs. control: 3.4(2.6 ~ 4.2), P < 0.05), arthroplasty rate (exposure: 16(13.7%) vs. control: 7(6%), P < 0.05). Conclusions Patients with knee osteoarthritis experienced greater progression of osteoarthritis when the contralateral knee was also affected

A Quick Development Toolkit for Augmented Reality Visualization (QDARV) of a Factory

Augmented reality (AR) technology has been applied to the industrial field; however, at present, most industrial AR applications are developed for specific application scenarios, which are difficult to develop and have a long development cycle, and lack universality. To this end, this paper proposed a quick development toolkit for augmented reality visualization (QDARV) of a factory by using the script configuration and parsing approach. With QDARV, designers can quickly develop industrial AR applications, achieve AR registration based on quick response (QR) code and simultaneous localization and mapping (SLAM), and superpose information such as disassembly animations, industrial instruments, pictures, and texts on real scenes. In QDARV, an AR registration method based on SLAM map reloading is proposed. Through saving and reloading the map and the configuration and analysis of the SLAM-AR display content script, the AR scene configured by the designer is displayed. Meanwhile, the objects detected using YOLOv5 are used as a landmark to assist the SLAM system in positioning. The QDARV can be applied to realize AR visualization of factories with a large area

Taxonomic notes on the Alocasia-Colocasia Clade (Araceae) in China I: Alocasia yungiana, a new species from Tongbiguan Nhi ure Reserve, Yunnan Province

A new Alocasia species, Alocasia yunqiana, is described and compared with its morphologically related taxa. The novelty is characterized by an oblong-naviculifonn-shaped spathe-limb that is strongly hooded and slightly glaucous to glossy during its entire anthesis, a spadix with a sterile interstice and an appendix that are much shorter and thinner than the staminate zone, as well as the dimorphic synandroidia. Detailed information containing description, colored plates and illustration are provided herein