Design optimization of TBM disc cutters for different geological conditions

A novel optimization methodology for the disc cutter designs of tunnel boring machines (TBM) was presented. To fully understand the characteristics and performance of TBM cutters, a comprehensive list of performance parameters were investigated, including maximum equivalent stress and strain, specific energy and wear life which were closely related to the cutting forces and profile geometry of the cutter rings. A systematic method was employed to evaluate an overall performance index by incorporating objectives at all possible geological conditions. The Multi-objective & Multi-geologic Conditions Optimization (MMCO) program was then developed, which combined the updating of finite element model, system evaluation, finite element solving, post-processing and optimization algorithm. Finally, the MMCO was used to optimize the TBM cutters used in a TBM tunnel project in China. The results show that the optimization significantly improves the working performances of the cutters under all geological conditions considered

Optimal surface profile design of deployable mesh reflectors via a force density strategy

Based on a force density method coupled with optimal design of node positions, a novel approach for optimal surface profile design of mesh reflectors is presented. Uniform tension is achieved by iterations on coefficients of force density. The positions of net nodes are recalculated in each iteration so that the faceting RMS error of the reflector surface is minimized. Applications of both prime focus and offset configurations are demonstrated. The simulation results show the effectiveness of the proposed approach

Low-lying s=+1s=+1 Pentaquark states in the Inherent Nodal Structure Analysis

The strangeness $s=+1$ pentaquark states as $qqqq\bar{q}$ clusters are investigated in this letter. Starting from the inherent geometric symmetry, we analyzed the inherent nodal structure of the system. As the nodeless states, the low-lying states are picked out. Then the S-wave state $(J^P, T)= ({{1/2}}^{-}, 0)$ and P-wave state $(J^P, T)= ({{1/2}}^{+}, 0)$ may be the candidates of low-lying pentaquark states. By comparing the accessibility of the two states and referring the presently obtained K-N interaction potential, we propose that the quantum numbers of the observed pentaquark state $\Theta^{+}$ may be $(J^P, T)=({{1/2}}^{+}, 0)$ and L=1.Comment: 15 pages, 2 figures, 4 tables. Revised version with detailed description, expanded discussion and reference for the geometric configuration to be proposed being adde

CluCDD:Contrastive Dialogue Disentanglement via Clustering

A huge number of multi-participant dialogues happen online every day, which leads to difficulty in understanding the nature of dialogue dynamics for both humans and machines. Dialogue disentanglement aims at separating an entangled dialogue into detached sessions, thus increasing the readability of long disordered dialogue. Previous studies mainly focus on message-pair classification and clustering in two-step methods, which cannot guarantee the whole clustering performance in a dialogue. To address this challenge, we propose a simple yet effective model named CluCDD, which aggregates utterances by contrastive learning. More specifically, our model pulls utterances in the same session together and pushes away utterances in different ones. Then a clustering method is adopted to generate predicted clustering labels. Comprehensive experiments conducted on the Movie Dialogue dataset and IRC dataset demonstrate that our model achieves a new state-of-the-art result.Comment: 5 page

The Neuronal Channel NALCN Contributes Resting Sodium Permeability and Is Required for Normal Respiratory Rhythm

SummarySodium plays a key role in determining the basal excitability of the nervous systems through the resting “leak” Na+ permeabilities, but the molecular identities of the TTX- and Cs+-resistant Na+ leak conductance are totally unknown. Here we show that this conductance is formed by the protein NALCN, a substantially uncharacterized member of the sodium/calcium channel family. Unlike any of the other 20 family members, NALCN forms a voltage-independent, nonselective cation channel. NALCN mutant mice have a severely disrupted respiratory rhythm and die within 24 hours of birth. Brain stem-spinal cord recordings reveal reduced neuronal firing. The TTX- and Cs+-resistant background Na+ leak current is absent in the mutant hippocampal neurons. The resting membrane potentials of the mutant neurons are relatively insensitive to changes in extracellular Na+ concentration. Thus, NALCN, a nonselective cation channel, forms the background Na+ leak conductance and controls neuronal excitability

Research on stress sensitivity of fractured carbonate reservoirs based on CT technology

Fracture aperture change under stress has long been considered as one of primary causes of stress sensitivity of fractured gas reservoirs. However, little is known about the evolution of the morphology of fracture apertures on flow property in loading and unloading cycles. This paper reports a stress sensitivity experiment on carbonate core plugs in which Computed Tomography (CT) technology is applied to visualize and quantitatively evaluate morphological changes to the fracture aperture with respect to confining pressure. Fracture models were obtained at selected confining pressures on which pore-scale flow simulations were performed to estimate the equivalent absolute permeability. The results showed that with the increase of confining pressure from 0 to 0.6 MPa, the fracture aperture and equivalent permeability decreased at a greater gradient than their counterparts after 0.6 MPa. This meant that the rock sample is more stress-sensitive at low effective stress than at high effective stress. On the loading path, an exponential fitting was found to fit well between the effective confining pressure and the calculated permeability. On the unloading path, the relationship is found partially reversible, which can evidently be attributed to plastic deformation of the fracture as observed in CT images

Integrated metagenomics and metabolomics analysis reveals changes in the microbiome and metabolites in the rhizosphere soil of Fritillaria unibracteata

Fritillaria unibracteata (FU) is a renowned herb in China that requires strict growth conditions in its cultivation process. During this process, the soil microorganisms and their metabolites may directly affect the growth and development of FU, for example, the pathogen infection and sipeimine production. However, few systematic studies have reported the changes in the microbiome and metabolites during FU cultivation thus far. In this work, we simultaneously used metagenomics and metabolomics technology to monitor the changes in microbial communities and metabolites in the rhizosphere of FU during its cultivation for one, two, and three years. Moreover, the interaction between microorganisms and metabolites was investigated by co-occurrence network analysis. The results showed that the microbial composition between the three cultivation-year groups was significantly different (2020-2022). The dominant genera changed from Pseudomonas and Botrytis in CC1 to Mycolicibacterium and Pseudogymnoascus in CC3. The relative abundances of beneficial microorganisms decreased, while the relative abundances of harmful microorganisms showed an increasing trend. The metabolomics results showed that significant changes of the of metabolite composition were observed in the rhizosphere soil, and the relative abundances of some beneficial metabolites showed a decreasing trend. In this study, we discussed the changes in the microbiome and metabolites during the three-year cultivation of FU and revealed the relationship between microorganisms and metabolites. This work provides a reference for the efficient and sustainable cultivation of FU

GIST: Improving Parameter Efficient Fine Tuning via Knowledge Interaction

The Parameter-Efficient Fine-Tuning (PEFT) method, which adjusts or introduces fewer trainable parameters to calibrate pre-trained models on downstream tasks, has become a recent research interest. However, existing PEFT methods within the traditional fine-tiuning framework have two main shortcomings: 1) They overlook the explicit association between trainable parameters and downstream task knowledge. 2) They neglect the interaction between the intrinsic task-agnostic knowledge of pre-trained models and the task-specific knowledge in downstream tasks. To address this gap, we propose a novel fine-tuning framework, named GIST, in a plug-and-play manner. Specifically, our framework first introduces a trainable token, called the Gist token, when applying PEFT methods on downstream tasks. This token serves as an aggregator of the task-specific knowledge learned by the PEFT methods and forms an explicit association with downstream knowledge. Furthermore, to facilitate explicit interaction between task-agnostic and task-specific knowledge, we introduce the concept of Knowledge Interaction via a Bidirectional Kullback-Leibler Divergence objective. As a result, PEFT methods within our framework can make the pre-trained model understand downstream tasks more comprehensively by leveraging the knowledge interaction. Extensive experiments demonstrate the universality and scalability of our framework. Notably, on the VTAB-1K benchmark, we employ the Adapter (a prevalent PEFT method) within our GIST framework and achieve a performance boost of 2.25%, with an increase of only 0.8K parameters. The Code will be released.Comment: 17pages, 8 figures, 22 tables, Work in progres