Developing a Volume Model Using South NTS-372R Total Station without Tree Felling in a Populus canadensis Moench Plantation in Beijing, China

Volume table preparation using the traditional method and a collection model requires the harvest of approximately 200–300 trees of individual species. Although high precision could be achieved using that method, it causes huge damage to the forest. To minimize these losses, in this study, a South NTS-372R total station with a precise angle and distance measurement mode was used to measure 507 trees of Populus canadensis Moench without single tree felling. Moreover, the C# programming language was used in this study and the collected volume data were inserted in the total station. Using this method, a real-time precise measurement of volume could be achieved. After data collection, the optimal binary volume model of Populus canadensis Moench could be obtained through a comparative analysis. It turns out that the Yamamoto model is the optimal binary volume model (also known as two predictor variable model), with 0.9641 as the coefficient of determination (R2) and 0.19 m3 as the standard deviation of estimated value (SEE), which presents a good imitative effect. Moreover, it showed relative stability with the general relative error (TRE) of –0.12% and the mean system error (MSE) of –1.24%. The mean predicted error (MPE) of 1.18% and the mean predicted standard error (MPSE) of 9.25% showed high estimated precision of the average and individual tree volumes. The model has only three parameters, so it is suitable for volume table preparation. Finally, this study will present some new technical methods and means for volume modeling for further application in forestry

GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators

Imitation learning from human demonstrations is a powerful framework to teach robots new skills. However, the performance of the learned policies is bottlenecked by the quality, scale, and variety of the demonstration data. In this paper, we aim to lower the barrier to collecting large and high-quality human demonstration data by proposing GELLO, a general framework for building low-cost and intuitive teleoperation systems for robotic manipulation. Given a target robot arm, we build a GELLO controller that has the same kinematic structure as the target arm, leveraging 3D-printed parts and off-the-shelf motors. GELLO is easy to build and intuitive to use. Through an extensive user study, we show that GELLO enables more reliable and efficient demonstration collection compared to commonly used teleoperation devices in the imitation learning literature such as VR controllers and 3D spacemouses. We further demonstrate the capabilities of GELLO for performing complex bi-manual and contact-rich manipulation tasks. To make GELLO accessible to everyone, we have designed and built GELLO systems for 3 commonly used robotic arms: Franka, UR5, and xArm. All software and hardware are open-sourced and can be found on our website: https://wuphilipp.github.io/gello/

Effects of Canopy and Multi-Epoch Observations on Single-Point Positioning Errors of a GNSS in Coniferous and Broadleaved Forests

Global navigation satellite systems (GNSS) can quickly, efficiently, and accurately provide precise coordinates of points, lines, and surface elements, plus complete surveys and determine various boundary lines in forest investigations and management. The system has become a powerful tool for dynamic forest resource investigations and monitoring. GNSS technology plays a unique and important role in estimating timber volume, calculating timber cutting area, and determining the location of virgin forest roads and individual trees in forests. In this study, we quantitatively analyzed the influence of crown size and observation time on the single-point positioning accuracy of GNSS receivers for different forest types. The GNSS located single points for different forest types and crown sizes, enabling the collection of data. The locating time for each tree was more than 10 min. Statistical methods were used to analyze the positioning accuracy of multi-epoch data, and a model was developed to estimate the maximum positioning errors under different forest conditions in a certain positioning time. The results showed that for a continuous positioning time of approximately 10 min, the maximum positioning accuracies in coniferous and broadleaf forests were obtained, which were 12.13 and 15.11 m, respectively. The size of a single canopy had no obvious influence on the single-point positioning error of the GNSS, and canopy density was proven to be closely related to the positioning accuracy of a GNSS. The determination coefficients (R2) in the regression analysis of the general model, coniferous forest model, and broadleaved forest model that were developed in this study were 0.579, 0.701, and 0.544, respectively. These results indicated that the model could effectively predict the maximum positioning error in a certain period of time under different forest types and crown conditions at middle altitudes, which has important guiding significance for forest resource inventories and precise forest management

Howling Detection and Suppression Based on Segmented Notch Filtering

The existing adaptive echo cancellation based howling (typically in hearing aids) removal methods have several drawbacks such as insufficient attenuation of the howling component, slow response and nonlinear distortion. To solve these problems, we propose a segmented notch filtering based scheme. Specifically, firstly, it is proved that the attenuation value can reach −330 dB at any detected howling frequency; secondly, the filter coefficients can be readily calculated by a closed-form formula, yielding a fast response to the sudden howling accident; thirdly, the closed-form formula of this filter is theoretically an even function, indicating that this filter possesses a linear transfer characteristic. In combination with proper segmentation and precisely removing these transient samples arising from FIR (Finite Impulsive Response) filtering, nonlinear distortion can be entirely avoided. Experimental results show that our proposed scheme can not only accurately estimate the howling frequency, but can also completely remove it, which yields a high-quality output waveform with a recovery SNR of about 22 dB. Therefore, the proposed segmented notching based scheme possesses vast potential for hearing aid development and other relevant applications

Research on Adaptive Management of the Social–Ecological System of a Typical Mine–Agriculture–Urban Compound Area in North Shanxi, China

The mine–agriculture–urban compound area formed under the combined effects of natural conditions, mineral resource endowments, and historical development is affected by severe man-made disturbances, and faces a prominent contradiction between economic development and ecological protection. Guiding the future development is an urgent problem in this region. This research used image data, logical reasoning, and empirical analysis, based on social and economic statistics and land-use data, to analyze the typical characteristics and problems of the social–ecological system in the mine–agriculture–urban compound area. Moreover, we identified future directions for the region guided by policy documents and built a philosophy framework for sustainable development and management of the region based on the concept of adaptability. The results showed the following: (1) At present, the output value of the coal industry accounts for 84.10% of the total regional output value, severely disturbing its social–ecological system, which needs to be protected and restored under human guidance and management. (2) The future development of this region depends on the one hand on green mining, and on the other hand, it is necessary to fully tap the potential of arable land and livestock farms to develop efficient and intensive agriculture. (3) The key contents of the social–ecological system management of the mine–agriculture–urban compound area include resolving the contradiction between development and protection, ensuring development, optimizing industrial structure, and safeguarding public interests. In conclusion, this research can expand the connotation and application scope of adaptive management and provide a reference for such areas facing the prominent contradiction between development and protection

Label Propagation for Annotation-Efficient Nuclei Segmentation from Pathology Images

Nuclei segmentation is a crucial task for whole slide image analysis in digital pathology. Generally, the segmentation performance of fully-supervised learning heavily depends on the amount and quality of the annotated data. However, it is time-consuming and expensive for professional pathologists to provide accurate pixel-level ground truth, while it is much easier to get coarse labels such as point annotations. In this paper, we propose a weakly-supervised learning method for nuclei segmentation that only requires point annotations for training. The proposed method achieves label propagation in a coarse-to-fine manner as follows. First, coarse pixel-level labels are derived from the point annotations based on the Voronoi diagram and the k-means clustering method to avoid overfitting. Second, a co-training strategy with an exponential moving average method is designed to refine the incomplete supervision of the coarse labels. Third, a self-supervised visual representation learning method is tailored for nuclei segmentation of pathology images that transforms the hematoxylin component images into the H\&E stained images to gain better understanding of the relationship between the nuclei and cytoplasm. We comprehensively evaluate the proposed method using two public datasets. Both visual and quantitative results demonstrate the superiority of our method to the state-of-the-art methods, and its competitive performance compared to the fully-supervised methods. The source codes for implementing the experiments will be released after acceptance

Advances in Antioxidant Applications for Combating ¹³¹I Side Effects in Thyroid Cancer Treatment

Thyroid cancer is the most common endocrine cancer, and its prevalence has been increasing for decades. Approx. 95% of differentiated thyroid carcinomas are treated using 131iodine (131I), a radionuclide with a half-life of 8 days, to achieve optimal thyroid residual ablation following thyroidectomy. However, while 131I is highly enriched in eliminating thyroid tissue, it can also retain and damage other body parts (salivary glands, liver, etc.) without selectivity, and even trigger salivary gland dysfunction, secondary cancer, and other side effects. A significant amount of data suggests that the primary mechanism for these side effects is the excessive production of reactive oxygen species, causing a severe imbalance of oxidant/antioxidant in the cellular components, resulting in secondary DNA damage and abnormal vascular permeability. Antioxidants are substances that are capable of binding free radicals and reducing or preventing the oxidation of the substrate in a significant way. These compounds can help prevent damage caused by free radicals, which can attack lipids, protein amino acids, polyunsaturated fatty acids, and double bonds of DNA bases. Based on this, the rational utilization of the free radical scavenging function of antioxidants to maximize a reduction in 131I side effects is a promising medical strategy. This review provides an overview of the side effects of 131I, the mechanisms by which 131I causes oxidative stress-mediated damage, and the potential of natural and synthetic antioxidants in ameliorating the side effects of 131I. Finally, the disadvantages of the clinical application of antioxidants and their improving strategies are prospected. Clinicians and nursing staff can use this information to alleviate 131I side effects in the future, both effectively and reasonably

Additional file 6 of Skeleton-based cerebrovascular quantitative analysis

Normal047-Characters.docx. This file contains four tables (Table S11–S14). Table S11 records the curvature values of twenty sampling points of each vessel on the CoW. Table S12 records the torsion values of twenty sampling points of each vessel on the CoW. Table S13 records the radius values of twenty sampling points of each vessel on the CoW. Table S14 records the angle values of eighteen sampling points of each vessel on the CoW. The data of these four tables correspond to the Fig. 7 in the paper. And the data of the Fig. 8 are contained in all these four tables. (DOCX 26.9 kb

Additional file 2 of Skeleton-based cerebrovascular quantitative analysis

Normal047-segmentation.obj. This file is the segmentation result of the NO.47 data based on the Additional file 1: Normal047-MRA.mha. (OBJ 2846.72 kb