Evolution Characteristics of Pressure-Arch and Elastic Energy during Shallow Horizontal Coal Mining

To ensure safety mining during shallow horizontal coal mining based on the monitoring data of the roof weighting of a typical mining engineering in China, the load distribution characteristics of the roof in the mining field were analysed, and the mechanical model of the pressure-arch in the surrounding rock was established. Then the evolution characteristics of the pressure-arch and elastic energy were revealed during shallow coal mining by theoretical and numerical analyses. Results show that the continuous pressure-arch can form when the horizontal stress exceeds the vertical stress, and the elastic energy of the roof is released by the mining unloading effect. The caved zone of the overlying strata is formed below the inner boundary of the pressure-arch. The elastic energy is accumulated in the pressure-arch and the energy becomes the highest at the front arch foot. The sliding of the caved zone along the shear zone in the mining field can induce the strong roof weighting. The concentrated stress and the released energy during each mining stage increase with the working face advancing, and the height of the shear zone also increases. This study can provide a theoretical reference for similar mining engineering

The Basic Helix-Loop-Helix Transcription Factor Family in the Honey Bee, Apis mellifera

The basic helix-loop-helix (bHLH) transcription factors play important roles in a wide range of developmental processes in higher organisms. bHLH family members have been identified in a dozen of organisms including fruit fly, mouse and human. In this study, we identified 51 bHLH sequences in silico in the honey bee, Apis mellifera L. (Hymenoptera: Apidae), genome. Phylogenetic analyses revealed that they belong to 38 bHLH families with 21, 11, 9, 1, 8 and 1 members in high-order groups A, B, C, D, E and F, respectively. Using phylogenetic analyses, all of the 51 bHLH sequences were assigned to their corresponding families. Genes that encode ASCb, NeuroD, Oligo, Delilah, MyoRb, Figa and Mad were not found in the honey bee genome. The present study provides useful background information for future studies using the honey bee as a model system for insect development

Multimodal Token Fusion for Vision Transformers

Many adaptations of transformers have emerged to address the single-modal vision tasks, where self-attention modules are stacked to handle input sources like images. Intuitively, feeding multiple modalities of data to vision transformers could improve the performance, yet the inner-modal attentive weights may also be diluted, which could thus undermine the final performance. In this paper, we propose a multimodal token fusion method (TokenFusion), tailored for transformer-based vision tasks. To effectively fuse multiple modalities, TokenFusion dynamically detects uninformative tokens and substitutes these tokens with projected and aggregated inter-modal features. Residual positional alignment is also adopted to enable explicit utilization of the inter-modal alignments after fusion. The design of TokenFusion allows the transformer to learn correlations among multimodal features, while the single-modal transformer architecture remains largely intact. Extensive experiments are conducted on a variety of homogeneous and heterogeneous modalities and demonstrate that TokenFusion surpasses state-of-the-art methods in three typical vision tasks: multimodal image-to-image translation, RGB-depth semantic segmentation, and 3D object detection with point cloud and images.Comment: CVPR 202

Synthesis, Properties, and Characterization of Field’s Alloy Nanoparticles and Its Slurry

This chapter describes a facile one-step method developed for the synthesis of Field’s alloy nanoparticles using a nanoemulsification technique and their dispersed them in a base fluid to make slurry. The composition, size, morphology, and thermal properties of as-prepared nanoparticles were characterized by XRF, TEM and, DSC, respectively. The slurry with Field’s alloy nanoparticles exhibited good thermal properties and stability. Meanwhile, an experimental study was performed to investigate the jet impingement of HFE7100 fluid with nanosized metallic (Field’s alloy) phase change materials (nano-PCM). Surface modification was used to stabilize the slurry of the nano-PCM in HFE7100 fluid and make the slurry stable for over 1 month. The Field’s alloy nano-PCM absorbed heat during a phase change process from solid to liquid phase coupled with HFE7100 evaporation process. The effects of mass fraction of Field’s alloy nano-PCM on the pressure drop and heat transfer performances of the slurry were investigated through a heat transfer loop test. Away from the critical heat flux, Field’s alloy nano-PCM slurry provided a significant heat transfer enhancement due to the increase in the thermal capacity of the carrier fluid. Moreover, the nano-PCM slurries were able to maintain 97% of their heat removal capability after 5000 thermal cycles

Channel Exchanging Networks for Multimodal and Multitask Dense Image Prediction

Multimodal fusion and multitask learning are two vital topics in machine learning. Despite the fruitful progress, existing methods for both problems are still brittle to the same challenge -- it remains dilemmatic to integrate the common information across modalities (resp. tasks) meanwhile preserving the specific patterns of each modality (resp. task). Besides, while they are actually closely related to each other, multimodal fusion and multitask learning are rarely explored within the same methodological framework before. In this paper, we propose Channel-Exchanging-Network (CEN) which is self-adaptive, parameter-free, and more importantly, applicable for both multimodal fusion and multitask learning. At its core, CEN dynamically exchanges channels between subnetworks of different modalities. Specifically, the channel exchanging process is self-guided by individual channel importance that is measured by the magnitude of Batch-Normalization (BN) scaling factor during training. For the application of dense image prediction, the validity of CEN is tested by four different scenarios: multimodal fusion, cycle multimodal fusion, multitask learning, and multimodal multitask learning. Extensive experiments on semantic segmentation via RGB-D data and image translation through multi-domain input verify the effectiveness of our CEN compared to current state-of-the-art methods. Detailed ablation studies have also been carried out, which provably affirm the advantage of each component we propose.Comment: 18 pages. arXiv admin note: substantial text overlap with arXiv:2011.0500

Finite-time Anti-synchronization of Memristive Stochastic BAM Neural Networks with Probabilistic Time-varying Delays

This paper investigates the drive-response finite-time anti-synchronization for memristive bidirectional associative memory neural networks (MBAMNNs). Firstly, a class of MBAMNNs with mixed probabilistic time-varying delays and stochastic perturbations is first formulated and analyzed in this paper. Secondly, an nonlinear control law is constructed and utilized to guarantee drive-response finite-time anti-synchronization of the neural networks. Thirdly, by employing some inequality technique and constructing an appropriate Lyapunov function, some anti-synchronization criteria are derived. Finally, a number simulation is provided to demonstrate the effectiveness of the proposed mechanism