Search

Sensors are very import parts of the IoT (Internet of Things). To detect the cleaning loss of combine harvesters during operation, a new detection method based on acoustic signal was proposed. The simulation models of the impact among grain, stem and plate were established by using the ALE algorithm, and the vibration excitation characteristics of the grain and stem were investigated; the results showed that both grain and stem signal were generated by surface vibration's excitation of the air, and the highest amplitudes of grain and stem signal were 15 kHz and 7 kHz respectively, which made it possible to distinguish grain and stem impact acoustic signals. Therefore, this paper proposed a novel method to separate the grain and stem signals by identifying the characteristic frequency area and subsequently an improved EMD denoising method based on an autocorrelation function was proposed. The critical stratum of EMD was identified based on the autocorrelation value of the eigenmode function, and the improved de-noising method was able to effectively remove the noise and the stem signal. To verify the feasibility of the improved denoising method, a field wheat harvesting experiment was conducted to acquire the actual acoustic signal of the cleaning operation. Data processing results showed that the average error of the comparison between the detected loss amount and the actual loss amount was 6.1%. This paper presents a novel approach for researching combine harvester cleaning loss detection and denoising methods. [ABSTRACT FROM AUTHOR]

The proliferation of short texts resulting from the rapid advancements of social networks, online communication, and e-commerce has created a pressing need for short text classification in various applications. This paper presents a novel approach for short text classification, which combines a hierarchical heterogeneous graph with latent Dirichlet allocation (LDA) fusion. Our method first models the short text dataset as a hierarchical heterogeneous graph, which incorporates more syntactic and semantic information through a word graph, parts-of-speech (POS) tag graph, and entity graph. We then connected the representation of these three feature maps to derive a comprehensive feature vector for the text. Finally, we used the LDA topic model to adjust the feature weight, enhancing the effectiveness of short text extension. Our experiments demonstrated that our proposed approach has a promising performance in English short text classification, while in Chinese short text classification, although slightly inferior to the LDA + TF-IDF method, it still achieved promising results. [ABSTRACT FROM AUTHOR]

The diverse functional roles of the insula may emerge from its heavy connectivity to an extensive network of cortical and subcortical areas. Despite several previous attempts to investigate the hierarchical organization of the insula by applying the recently developed gradient approach to insula‐to‐whole brain connectivity data, little is known about whether and how there is variability across connectivity gradients of the insula to different cerebral systems. Resting‐state functional MRI data from 793 healthy subjects were used to discover and validate functional connectivity gradients of the insula, which were computed based on its voxel‐wise functional connectivity profiles to distinct cerebral systems. We identified three primary patterns of functional connectivity gradients of the insula to distinct cerebral systems. The connectivity gradients to the higher‐order transmodal associative systems, including the prefrontal, posterior parietal, temporal cortices, and limbic lobule, showed a ventroanterior‐dorsal axis across the insula; those to the lower‐order unimodal primary systems, including the motor, somatosensory, and occipital cortices, displayed radiating transitions from dorsoanterior toward both ventroanterior and dorsoposterior parts of the insula; the connectivity gradient to the subcortical nuclei exhibited an organization along the anterior–posterior axis of the insula. Apart from complementing and extending previous literature on the heterogeneous connectivity patterns of insula subregions, the presented framework may offer ample opportunities to refine our understanding of the role of the insula in many brain disorders. [ABSTRACT FROM AUTHOR]

In this work, the magnetic properties of the WSe2 monolayer with different nonmetals (B, C, F, N, and O) are studied by the ab initio calculations. Different behavior shows up by applying the normal strain. Under the normal strain, the magnetism becomes tunable except for the B and O adatoms. Then, the magnetic coupling between two nonmetals has been checked. A transformation from ferromagnetic (FM) to nonmagnetic (NM) has been observed in two F-adsorbed systems, which could be explained by the p–p hybridization mechanism. Our results provide a theoretical reference for the adsorption on the WSe2 surfaces. [ABSTRACT FROM AUTHOR]

The last decade has witnessed the growing prevalence of deep models on soft sensing in industrial processes. However, most of the existing soft sensing models are developed to learn from regular data in the Euclidean space, ignoring the complex coupling relations among process variables. On the other hand, graph networks are gaining attraction in handling non-Euclidean relations in industrial data. However, the existing graph networks on soft sensing models still suffer from two major issues: 1) how to capture the intervariable structural relations and intravariable temporal dependencies from dynamic and strongly coupled industrial data and 2) how to learn from nodes with distinctive importance for the soft sensing task. To address these problems, we propose a self-learning evolutionary and node-aware graph network (SENGraph) for industrial soft sensing. We first develop a self-learning graph generation (SLG) module to combine the coarse-and fine-grained graphs to capture the global trend and local dynamics from process data. Then, we build a self-evolutionary graph module (EGM) to obtain diversified node features from the entire graph using mutation and crossover strategies. Finally, we design a node-aware module (NAM) to highlight the informative nodes and suppress the less significant ones to further improve the discriminative ability of the downstream soft sensing. Extensive experimental results and analysis on four real-world industrial datasets demonstrate that our proposed SENGraph model outperforms the existing state-of-the-art (SOTA) soft sensing methods.

1T-1H metal-semiconductor interfaces in two-dimensional (2D) transition-metal dichalcogenides (TMDs) play a crucial role in utilizing the band gaps of TMDs for applications in electronic devices. Although the 1T-1H two-phase structure has been observed in exfoliated 2D nanosheets and chemically or physically treated TMDs, it cannot in principle be achieved in large-scale TMD monolayers grown by chemical vapor deposition (CVD), which is a fabrication method for electronic device applications, because of the metastable nature of the 1T phase. In this study we report CVD growth of 1T-1H two phase TMD monolayers by controlling thermal strains and alloy compositions. It was found that in-plane thermal strains arising from the difference in thermal expansion coefficients between TMD monolayers and substrates can drive the 1H to 1T transition during cooling after CVD growth. Moreover, grain boundaries in the 2D crystals act as the nucleation sites of the 1T phase and the lattice strain perturbations from alloying noticeably promote the formation of the metastable 1T phase. This work has an important implication in tailoring structure and properties of CVD grown 2D TMDs by phase engineering.

As a typical transition-metal dichalcogenides, MoS 2 has been a hotspot of research in many fields. In this work, the MoS 2 nanosheets were compounded on 1T-VS 2 nanoflowers (VS 2 @MoS 2 ) successfully by a two-step hydrothermal method for the first time, and their hydrogen evolution properties were studied mainly. The higher charge-transfer efficiency benefiting from the metallicity of VS 2 and the greater activity due to more exposed active edge sites of MoS 2 improve the hydrogen evolution reaction performance of the nanocomposite electrocatalyst. Adsorption and transport of an intermediate hydrogen atom by VS 2 also enhances the hydrogen evolution efficiency. The catalyst shows a low onset potential of 97 mV, a Tafel slope as low as 54.9 mV dec -1 , and good stability. Combining the electric conductivity of VS 2 with the physicochemical stability of MoS 2 , VS 2 @MoS 2 also exhibits excellent humidity properties.

Grain boundaries (GBs) are unavoidable crystal defects in polycrystalline materials and significantly influence their properties. However, the structure and chemistry of GBs in 2D transition metal dichalcogenide alloys have not been well established. Here we report significant chemical selectivity of transition metal atoms at GB dislocation cores in Mo 1-x W x S 2 monolayers. Different from classical elastic field-driven dislocation segregation in bulk crystals, the chemical selectivity in the 2D crystals originates prominently from variation of atomic coordination numbers at dislocation cores. This observation provides atomic insights into the topological effect on the chemistry of crystal defects in 2D materials.