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

An AI-Driven Approach to Wind Turbine Bearing Fault Diagnosis from Acoustic Signals

This study aimed to develop a deep learning model for the classification of bearing faults in wind turbine generators from acoustic signals. A convolutional LSTM model was successfully constructed and trained by using audio data from five predefined fault types for both training and validation. To create the dataset, raw audio signal data was collected and processed in frames to capture time and frequency domain information. The model exhibited outstanding accuracy on training samples and demonstrated excellent generalization ability during validation, indicating its proficiency of generalization capability. On the test samples, the model achieved remarkable classification performance, with an overall accuracy exceeding 99.5%, and a false positive rate of less than 1% for normal status. The findings of this study provide essential support for the diagnosis and maintenance of bearing faults in wind turbine generators, with the potential to enhance the reliability and efficiency of wind power generation

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

ADHD and family life: A cross-sectional study of ADHD prevalence among pupils in China and factors associated with parental depression

Background: Attention Deficit Hyperactivity Disorder (ADHD) is increasingly recognized as a major problem for children and their families in China. However, its influence on parental mental health has been seldom explored. Objective: To examine the prevalence of attention deficit hyperactivity disorder in a community sample of children aged 6–13 years, and the extent to which it impacts parental mental health. Method: Cross-sectional study of primary school pupils (number = 2497) in Deyang, Sichuan Province, South-West China. We used standardized instruments to identify children with ADHD symptoms and parent depression. Results: The prevalence of ADHD was 9.8%. Factors associated with the likelihood of ADHD, included family environment(P = 0.003), time spent with children(P = 0.01), parenting style(P = 0.01), and parental relationship, pupils self-harm and lower academic ability (P = 0.001). After controlling for other factors, having a child with ADHD increased the likelihood of parents’ depression (OR = 4.35, CI = 2.68~7.07), additional factors included parent relationship. Conclusions: ADHD may be a common disorder among Chinese children, the symptoms of which may increase the likelihood of parent depression. There is a need for greater detection of ADHD in schools, acknowledgement of the challenges the disorder creates for academic success and family wellbeing, and psychoeducational tools for supporting parents of children with ADHD

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