The development and utilization of forest parks based on the experiences strategy

AbstractAs a piece of forestland under special protection and a specific site for tourism and leisure, national forest parks have already become an important carrier for forest tourism and drawn more and more attention from all walks of the society. This paper is a case study of Baicaowa National Forest Park in Chengde, which focuses on the development and utilization of forest parks based on the experiences strategy. It claims that during the planning of tourism facilities of national forest parks, principles of “thematic experience” (including temporal, spatial and physical experiences), “profound experience” and “educational experience” should be adopted to offer a newly complex tourism product system with “experience at its core” and strengthened experience elements as well as improved experience design, thus propelling the upgrade of the on-going forest tourism products

Zinc 2-((2-(benzoimidazol-2-yl)quinolin-8-ylimino)methyl)phenolates : synthesis, characterization and photoluminescence behavior

A series of 2-(2-(1H-benzoimidazol-2-yl)quinolin-8-yliminomethyl)phenol derivatives and their zinc complexes (C1 – C5) were synthesized and fully characterized. The molecular structure of the representative complex C2 was determined by single crystal X-ray diffraction, which revealed that the zinc was five-coordinated with the tetra-dentate ligand and a methanol bound to the metal afford a distorted square-pyramidal geometry. The UV-Vis absorption and fluorescence spectra of the organic compounds and their zinc complexes were measured and investigated in various solvents such as methanol, THF, dichloromethane, and toluene; significant influences by solvents were observed on their luminescent properties; red-shifts for the zinc complexes were clearly observed in comparisons to the free organic compounds

Scalable and Effective Conductance-based Graph Clustering

Conductance-based graph clustering has been recognized as a fundamental operator in numerous graph analysis applications. Despite the significant success of conductance-based graph clustering, existing algorithms are either hard to obtain satisfactory clustering qualities, or have high time and space complexity to achieve provable clustering qualities. To overcome these limitations, we devise a powerful \textit{peeling}-based graph clustering framework \textit{PCon}. We show that many existing solutions can be reduced to our framework. Namely, they first define a score function for each vertex, then iteratively remove the vertex with the smallest score. Finally, they output the result with the smallest conductance during the peeling process. Based on our framework, we propose two novel algorithms \textit{PCon\_core} and \emph{PCon\_de} with linear time and space complexity, which can efficiently and effectively identify clusters from massive graphs with more than a few billion edges. Surprisingly, we prove that \emph{PCon\_de} can identify clusters with near-constant approximation ratio, resulting in an important theoretical improvement over the well-known quadratic Cheeger bound. Empirical results on real-life and synthetic datasets show that our algorithms can achieve 5$\sim$42 times speedup with a high clustering accuracy, while using 1.4$\sim$7.8 times less memory than the baseline algorithms

Point Cloud Self-supervised Learning via 3D to Multi-view Masked Autoencoder

In recent years, the field of 3D self-supervised learning has witnessed significant progress, resulting in the emergence of Multi-Modality Masked AutoEncoders (MAE) methods that leverage both 2D images and 3D point clouds for pre-training. However, a notable limitation of these approaches is that they do not fully utilize the multi-view attributes inherent in 3D point clouds, which is crucial for a deeper understanding of 3D structures. Building upon this insight, we introduce a novel approach employing a 3D to multi-view masked autoencoder to fully harness the multi-modal attributes of 3D point clouds. To be specific, our method uses the encoded tokens from 3D masked point clouds to generate original point clouds and multi-view depth images across various poses. This approach not only enriches the model's comprehension of geometric structures but also leverages the inherent multi-modal properties of point clouds. Our experiments illustrate the effectiveness of the proposed method for different tasks and under different settings. Remarkably, our method outperforms state-of-the-art counterparts by a large margin in a variety of downstream tasks, including 3D object classification, few-shot learning, part segmentation, and 3D object detection. Code will be available at: https://github.com/Zhimin-C/Multiview-MA

Fault identification technology for gear tooth surface wear based on MPE method by MI and improved FNN algorithm

Multiscale Permutation Entropy (MPE) is a presented nonlinear dynamic technology for measuring the randomness and detecting the nonlinear dynamic change of time sequences and can be used effectively to extract the nonlinear dynamic wear fault feature of gear tooth surface from vibration signals of gear set. To solve the subjectivity drawback of threshold parameter selection process in MPE method, a joint calculation method based on the Mutual Information (MI) and improved False Nearest Neighbor (FNN) principle for calculating threshold parameters for MPE method was presented in this article. Then, the influence of threshold parameters on the identification accuracy of fault features with the MPE was studied by analyzing simulation data. Through the simulation analysis, the effectiveness of the proposed MPE method is validated. Finally, the wear failure test of spur gear was carried out, and the proposed method was applied to analyze the experimental data of fault signal. Meanwhile, the vibration characteristics of the fault signal are acquired. The analysis results show that the proposed method can effectively realize the fault diagnosis of gear box and has higher fault identification accuracy than the existing methods

Enhancing copper infiltration into alumina using spark plasma sintering to achieve high performance Al2O3/Cu composites

Al2O3/Cu (with 30 wt% of Cu) composites were prepared using a combined liquid infiltration and spark plasma sintering (SPS) method using pre-processed composite powders. Crystalline structures, morphology and physical/mechanical properties of the sintered composites were studied and compared with those obtained from similar composites prepared using a standard liquid infiltration process without any external pressure. Results showed that densities of the Al2O3/Cu composites prepared without applying pressure were quite low. Whereas the composites sintered using the SPS (with a high pressure during sintering in 10 minutes) showed dense structures, and Cu phases were homogenously infiltrated and dispersed with a network from inside the Al2O3 skeleton structures. Fracture toughness of Al2O3/Cu composites prepared without using external pressure (with a sintering time of 1.5 hours) was 4.2 MPa·m1/2, whereas that using the SPS process was 6.5 MPa·m1/2. These toughness readings were increased by 18% and 82%, respectively, compared with that of pure alumina. Hardness, density and electrical resistivity of the samples prepared without pressure were 693 HV, 82.5% and 0.01Ω•m, whereas those using the SPS process were 842 HV, 99.1%, 0.002Ω•m, respectively. The enhancement in these properties using the SPS process are mainly due to the efficient pressurized infiltration of Cu phases into the network of Al2O3 skeleton structures, and also due to high intensity discharge plasma which produces fully densified composites in a short time

Multi-task unscented Kalman inversion (MUKI): a derivative-free joint inversion framework and its application to joint inversion of geophysical data

In the geophysical joint inversion, the gradient and Bayesian Markov Chain Monte Carlo (MCMC) sampling-based methods are widely used owing to their fast convergences or global optimality. However, these methods either require the computation of gradients and easily fall into local optimal solutions, or cost much time to carry out the millions of forward calculations in a huge sampling space. Different from these two methods, taking advantage of the recently developed unscented Kalman method in computational mathematics, we extend an iterative gradient-free Bayesian joint inversion framework, i.e., Multi-task unscented Kalman inversion (MUKI). In this new framework, information from various observations is incorporated, the model is iteratively updated in a derivative-free way, and a Gaussian approximation to the posterior distribution of the model parameters is obtained. We apply the MUKI to the joint inversion of receiver functions and surface wave dispersion, which is well-established and widely used to construct the crustal and upper mantle structure of the earth. Based on synthesized and real data, the tests demonstrate that MUKI can recover the model more efficiently than the gradient-based method and the Markov Chain Monte Carlo method, and it would be a promising approach to resolve the geophysical joint inversion problems.Comment: 13 pages, 4 figure

End water content determines the magnitude of N2_{2}O pulse from nitrifier denitrification after rewetting a fluvo-aquic soil

Large nitrous oxide (N$_{2}$O) emissions pulses have been observed after rewetting dry soil. However, few studies have uncoupled the effects of drought severity from the degree to which the soil is saturated. In this study, we conducted three aerobic incubation experiments to investigate the effects of soil rewetting on N$_{2}$O emissions from a dryland soil. The results showed that, at constant soil moisture, total N$_{2}$O emissions in soil with 90% water-holding capacity (WHC) were significantly higher than those in 30%, 45%, 60% and 75% WHC treatments. In the dry–wet group, the soil moisture content was adjusted from 30%, 45% and 60% WHC to the end content of 75% and 90% WHC, respectively; the cumulative N$_{2}$O emissions in the 30–90%, 45–90% and 60–90% WHC nitrogen (N) treatments were significantly higher than those in the 30–75%, 45–75% and 60–75% WHC N treatments. Regarding fertilizer N types, there was no significant difference in N$_{2}$O emissions from soil at 90% WHC when (NH$_{4}$)$_{2}$SO$_{4}$ or urea was applied. Nitrification inhibitor significantly reduced N$_{2}$O emissions in soil applied with NH$_{4}$$^{+}$-N fertilizer, indicating that nitrification played a major role in N$_{2}$O emissions from soils. The contribution of denitrification was negligible, according to the low emission rate of soils with only NO$_{3}$$^{-}$ additions. High N$_{2}$O emissions occurred in soil treated with NO$_{2}$$^{-}$, accounting for about 83.6% of those of the NH$_{4}$$^{+}$ treatment. Therefore, in this study we concluded that the end water content of soil was more important than the role of drought severity in the dry-wet process and that nitrifier denitrification was probably the main pathway of N$_{2}$O production under the condition of 90% WHC moisture after rewetting soil

The dependence of Ni-Fe bioxide composites nanoparticles on the FeCl2 solution used

BACKGROUND: Ni(2)O(3)- γ-Fe(2)O(3) composite nanoparticles coated with a layer of 2FeCl(3)·5H(2)O can be prepared by co-precipitation and processing in FeCl(2) solution. Using vibrating sample magnetometer (VSM), X-ray diffraction (XRD), transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS) diffraction techniques, the dependence of the preparation on the concentration of the FeCl(2) treatment solution is revealed. RESULTS: The magnetization of the as-prepared products varied non-monotonically as the FeCl(2) concentration increased from 0.020 M to 1.000 M. The Experimental results show that for the composite nanoparticles, the size of the γ-Fe(2)O(3) phase is constant at about 8 nm, the Ni(2)O(3) phase decreased and the 2FeCl(3)·5H(2)O phase increased with increasing concentration of FeCl(2) solution. The magnetization of the as-prepared products mainly results from the γ-Fe(2)O(3) core, and the competition between the reduction of the Ni(2)O(3) phase with the increase of the 2FeCl(3)·5H(2)O phase resulted in the apparent magnetization varying non-monotonically. CONCLUSIONS: When the concentration of FeCl(2) treatment solution did not exceed 0.100 M, the products are spherical nanoparticles of size about 11 nm; their magnetization increased monotonically with increasing the concentration of FeCl(2) solution due to the decreasing proportion of Ni(2)O(3) phase

Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth

Conventional self-supervised monocular depth prediction methods are based on a static environment assumption, which leads to accuracy degradation in dynamic scenes due to the mismatch and occlusion problems introduced by object motions. Existing dynamic-object-focused methods only partially solved the mismatch problem at the training loss level. In this paper, we accordingly propose a novel multi-frame monocular depth prediction method to solve these problems at both the prediction and supervision loss levels. Our method, called DynamicDepth, is a new framework trained via a self-supervised cycle consistent learning scheme. A Dynamic Object Motion Disentanglement (DOMD) module is proposed to disentangle object motions to solve the mismatch problem. Moreover, novel occlusion-aware Cost Volume and Re-projection Loss are designed to alleviate the occlusion effects of object motions. Extensive analyses and experiments on the Cityscapes and KITTI datasets show that our method significantly outperforms the state-of-the-art monocular depth prediction methods, especially in the areas of dynamic objects. Our code will be made publicly available