Experimental investigations of stress-gas pressure evolution rules of coal and gas outburst: A case study in Dingji coal mine, China

Coal and gas outburst is a potentially fatal risk during the mining of gassy coal seams, which seriously threatens the safe mining of collieries. To understand the outburst mechanism and evolution rules, a new apparatus (LSTT) was developed to conduct simulated experiment. In the context of an outburst accident in Dingji coal mine, the authors launched an authentic outburst experiment to replay the outburst accident. Experimental apparatus, similar criterion, coal‐like materials and gas sources, and experimental design were discussed systematically in this paper. Experimentally, the study analyzed the geo‐stress has significant influence on the outburst evolution. At the driving face, the stress concentration possibly caused gas outburst, under the influence of mining‐induced stress. After the outburst occurred, the stress balance of the coal changed, resulting in the instability of the coal. Furthermore, the elastic energy, gas enthalpy, and gravitational potential energy were released rapidly. The experimental result stated that outburst coal has the sorting characteristics, in line with the field outburst law. The intensity prediction model has been built based on the energy model. Moreover, the factors that impact outburst intensity were analyzed. In the process of coal and gas outburst, the gas enthalpy of gas and the elastic potential of coal are the main energy sources. This study provides guidance for the development of the outburst mechanism and outburst mine management

Tilting flat bands in an empty microcavity

Recently microcavities with anisotropic materials are shown to be able to create novel bands with non-zero local Berry curvature. The anisotropic refractive index of the cavity layer is believed to be critical in opening an energy gap at the tilted Dirac points. In this work, we show that an anticrossing between a cavity mode and a Bragg mode can also form within an empty microcavity without any birefringent materials. Flat bands are observed within the energy gap due to the particular refractive index distribution of the sample. The intrinsic TE-TM splitting and XY splitting induce the squeezing of the cavity modes in momentum space, so that the flat bands are spin-dependently tilted. Our results pave the way to investigate the spin orbit coupling of photons in a simple microcavity without anisotropic cavity layers

Characterization of the neural circuitry of the auditory thalamic reticular nucleus and its potential role in salicylate-induced tinnitus

IntroductionSubjective tinnitus, the perception of sound without an external acoustic source, is often subsequent to noise-induced hearing loss or ototoxic medications. The condition is believed to result from neuroplastic alterations in the auditory centers, characterized by heightened spontaneous neural activities and increased synchrony due to an imbalance between excitation and inhibition. However, the role of the thalamic reticular nucleus (TRN), a structure composed exclusively of GABAergic neurons involved in thalamocortical oscillations, in the pathogenesis of tinnitus remains largely unexplored.MethodsWe induced tinnitus in mice using sodium salicylate and assessed tinnitus-like behaviors using the Gap Pre-Pulse Inhibition of the Acoustic Startle (GPIAS) paradigm. We utilized combined viral tracing techniques to identify the neural circuitry involved and employed immunofluorescence and confocal imaging to determine cell types and activated neurons.ResultsSalicylate-treated mice exhibited tinnitus-like behaviors. Our tracing clearly delineated the inputs and outputs of the auditory-specific TRN. We discovered that chemogenetic activation of the auditory TRN significantly reduced the salicylate-evoked rise in c-Fos expression in the auditory cortex.DiscussionThis finding posits the TRN as a potential modulatory target for tinnitus treatment. Furthermore, the mapped sensory inputs to the auditory TRN suggest possibilities for employing optogenetic or sensory stimulations to manipulate thalamocortical activities. The precise mapping of the auditory TRN-mediated neural pathways offers a promising avenue for designing targeted interventions to alleviate tinnitus symptoms

Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker

Finding classifiers robust to adversarial examples is critical for their safe deployment. Determining the robustness of the best possible classifier under a given threat model for a given data distribution and comparing it to that achieved by state-of-the-art training methods is thus an important diagnostic tool. In this paper, we find achievable information-theoretic lower bounds on loss in the presence of a test-time attacker for multi-class classifiers on any discrete dataset. We provide a general framework for finding the optimal 0-1 loss that revolves around the construction of a conflict hypergraph from the data and adversarial constraints. We further define other variants of the attacker-classifier game that determine the range of the optimal loss more efficiently than the full-fledged hypergraph construction. Our evaluation shows, for the first time, an analysis of the gap to optimal robustness for classifiers in the multi-class setting on benchmark datasets.Comment: NeurIPS 2023 Spotligh

A dynamic learning method based on the Gaussian process for tunnel boring machine intelligent driving

Introduction: The application of intelligent learning methods to the mining of characteristics and rules of time-series data has gained increasing attention with the rapid development of deep learning. One critical application of such methods is the intelligent assistant driving of tunnel boring machines (TBMs), for which the optimization of driving parameters is essential to improve construction efficiency. However, existing prediction models for TBM parameters are “static” and cannot dynamically capture parameter evolution during real-time driving cycles.Methods: In this study, we propose a novel dynamic learning model for TBM parameters by introducing the Gaussian process to address this problem. The model can learn decision-making experiences from historical driving cycles, dynamically update the model based on small sample data from current driving cycles, and simultaneously achieve driving parameter prediction. We focused on real-time prediction of TBM parameters in a tunnel project in western China.Results: The results show that the average relative errors of predicted total thrust and torque values were 1.9% and 2.7%, respectively, and the prediction accuracy was higher than that of conventional models such as random forest and long short-term memory. The model fully exploited updating of small samples of parameters, reducing the average time cost of the model to 29.7 s, which satisfies the requirements of efficient application.Discussion: The dynamic learning strategy of time-series data adopted in this study provides a reference for other similar engineering applications. The proposed model can improve the prediction accuracy of TBM parameters, thus facilitating the optimization of driving parameters and enhancing the construction efficiency of tunnels.Conclusion: In summary, this study establishes a dynamic learning model of TBM parameters that can dynamically capture parameter evolution and achieve accurate real-time driving parameter prediction. The proposed model can contribute to the development of intelligent assistant driving of TBMs and similar engineering applications

A prediction method for blade deformations of large-scale FVAWTs using dynamics theory and machine learning techniques

There is renewed interest in floating vertical axis wind turbines (FVAWTs) as offshore wind turbines progressively increase in size and move into deeper waters. To explore the potential of large-scale FVAWTs for future commercialization, it is crucial to investigate blade deformations using an accurate and effective method. In this study, we developed a hybrid model, namely, the SVST-ANN, which integrates dynamic theory and machine learning techniques to predict blade deformations. Specifically, an artificial neural network (ANN) module is incorporated into the slack coupled vertical axis wind turbine simulation tool (SVST), which significantly reduces the total computational time. A comparative study was conducted between the SVST-ANN model and the traditional SVST model, employing a 10 MW helical-type FVAWT as an example. The results show that the SVST-ANN model can accurately and efficiently predict blade deformations. The maximum errors for the maximum value, average value, and standard deviation across all nodes are minimal, with a corresponding computational time reduction of approximately 60 %. This study provides a novel method for investigating the dynamic behavior of the FVAWTs, which is more effective for calculating the elastic deformations of blades than traditional numerical methods

Electrically controlling vortices in a neutral exciton polariton condensate at room temperature

Manipulating bosonic condensates with electric fields is very challenging as the electric fields do not directly interact with the neutral particles of the condensate. Here we demonstrate a simple electric method to tune the vorticity of exciton polariton condensates in a strong coupling liquid crystal (LC) microcavity with CsPbBr$_3$ microplates as active material at room temperature. In such a microcavity, the LC molecular director can be electrically modulated giving control over the polariton condensation in different modes. For isotropic non-resonant optical pumping we demonstrate the spontaneous formation of vortices with topological charges of +1, +2, -2, and -1. The topological vortex charge is controlled by a voltage in the range of 1 to 10 V applied to the microcavity sample. This control is achieved by the interplay of a built-in potential gradient, the anisotropy of the optically active perovskite microplates, and the electrically controllable LC molecular director in our system with intentionally broken rotational symmetry. Besides the fundamental interest in the achieved electric polariton vortex control at room temperature, our work paves the way to micron-sized emitters with electric control over the emitted light's phase profile and quantized orbital angular momentum for information processing and integration into photonic circuits

High-mobility graphene on liquid p-block elements by ultra-low-loss CVD growth

The high-quality and low-cost of the graphene preparation method decide whether graphene is put into the applications finally. Enormous efforts have been devoted to understand and optimize the CVD process of graphene over various d-block transition metals (e.g. Cu, Ni and Pt). Here we report the growth of uniform high-quality single-layer, single-crystalline graphene flakes and their continuous films over p-block elements (e.g. Ga) liquid films using ambient-pressure chemical vapor deposition. The graphene shows high crystalline quality with electron mobility reaching levels as high as 7400 cm2 V−1s−1 under ambient conditions. Our employed growth strategy is ultra-low-loss. Only trace amounts of Ga are consumed in the production and transfer of the graphene and expensive film deposition or vacuum systems are not needed. We believe that our research will open up new territory in the field of graphene growth and thus promote its practical application