高深溜井井筒堵塞的爆破处理实践

溜井是采用平硐-溜井方式开拓矿山的运输咽喉,溜井正常与否对矿山生产影响极大。本文通过黑沟铁矿高深溜井井筒堵塞处理实例,对其堵塞爆破处理方法及经验做了系统分析。重点介绍的爆破冲击波破拱疏通高深溜井井筒高位堵塞的爆破方法,富有新意,可供国内外同类型矿山参考借鉴。     

一种直升机避障电力线检测方法

电力线是一类形状细长、特征稀疏、随着视角的变化容易混淆在大量背景信息中的特殊障碍物，常规电力线检测识别算法得到的目标框对电力线所在位置的估计不够准确。为此，提出了一种相对角度估计方法，基于常规电力线目标检测与识别算法，并结合电力线相对角度估计，从而提高电力线的检测识别过程中所在位置的精度。相比电力线绝对角度回归的方法，提出的相对角度估计方法容易训练易收敛，计算量小，适用于实时性要求较高的应用场合。     

Experimental and Numerical Investigation of a Novel Spiral Micromixer with Sinusoidal Channel Walls

A novel spiral micromixer with sinusoidal channel walls was designed to enhance the mixing index in the low to intermediate Reynolds number range (1 < Re < 100). To analyze the fluid flow, a set of numerical simulations were performed using the finite-difference method. The microchip was fabricated from polydimethylsiloxane, employing the soft-lithography technique. The degree of mixing was increased by 99.11 % when using the proposed micromixer, compared to 59.44 % for a simple spiral micromixer. The introduced microchannel drastically reduced the mixing length, increasing the mixing index of a 0.5-loop spiral-sinusoidal microchannel compared to that of the simple spiral microchannel with 1.5 loops. The mixing index of the 3-loop mixer was higher than that of the microchannel with 1.5 loops, and its pressure drop was increased.     

基于人工神经网络的智能化架空线路应变快速解调方法北大核心

为了提高智能化光纤复合架空线路态势感知的实时性,将人工神经网络方法应用于光纤沿线应变解调,确定了神经网络的结构。编程实现了基于洛伦兹模型的最小二乘谱拟合方法和神经网络方法,采用不同信噪比和布里渊频移的布里渊谱训练神经网络,将它们应用于某光纤复合架空线路沿线光纤应变的测量,从不同角度比较了两种方法的计算结果。计算结果表明,神经网络方法能有效获得光纤沿线的布里渊频移进而获得应变,具有与谱拟合方法相似的准确性,但应变解调时间仅约为谱拟合方法的1/20000。研究结果为提高智能光纤复合架空线路态势感知的实时性提供了参考。     

High voltage and self-healing zwitterionic double-network hydrogels as electrolyte for zinc-ion hybrid supercapacitor/battery

The hydrogel electrolyte is an important part of safety and development potential in zinc-based energy storage equipment due to its inherent low mechanical strength and voltage decomposition. However, hydrogel electrolytes possess a reduced working life for zinc dendrites growth and a narrow voltage window. In this study, a hydrogel electrolyte prepares by the zwitterionic monomer [2-(methacryloyloxy)ethyl]dimethyl-(3-sulfopropyl) (MS) and sodium alginate (SA) alleviate these problems. The zwitterionic double-network hydrogel has good mechanical strength, inhibits the growth of zinc dendrites, enhances practicability, greatly increases the voltage window (0–2.4 V), and has self-healing properties to its rich functional groups. The assembled zinc-ion hybrid supercapacitors (ZHSs) have a high-power density of 172.33 W kg^?1 and an energy density of 88.56 Wh·kg^?1 at 0.5 A g^?1. The assembled zinc-ion battery also has good electrochemical performance. Flexible ZHSs and batteries provide power to the timer stably under different bending angles. The zwitterionic double-network hydrogel can be applied to both zinc-based supercapacitors and batteries.     

Microstructure,mechanical and tribological properties of Mo–V–Cu–N coatings prepared by HIPIMS technique

A combination of high wear-resistance and low-friction is crucial for improving the wear performance of self-lubricating coatings, which is generally determined by an excellent lubricating effect and mechanical strength. In this study, the Mo–V–Cu–N coatings were prepared by HIPIMS technique with a spliced target of Mo–V–Cu at various charge voltages. The results revealed that Mo–V–Cu–N coatings presented a solid solution phase of B1–MoVN with (200) preferred orientation, and the preferred orientation was enhanced at high charge voltages. Whereas the Cu atoms formed an amorphous phase in Mo–V–Cu–N coatings due to a low Cu content of 2.3–3.6 at.%. As the charge voltage increased to 750 V, more charged metallic ions were accelerated and bombarded substrate surface efficiently, forming smooth and dense Mo–V–Cu–N coatings with a high hardness of 31.0 GPa. All the coatings presented a low friction coefficient of 0.34–0.39 due to the formation of MoO₂, VO₂ and CuO mixed oxides, and the wear mechanism was dominated by abrasive and tribo-oxidation wear at room temperature.     

Depicting the role of gas-solid interactions on the hydrodynamics of converging pneumatic riser

The understanding of the flow characteristics and effect of gas-solid interactions in pneumatic risers is fundamental to investigate to ensure effective design cost-effective operation. Thus, to understand the effect of gas-solid interactions on the hydrodynamics of newly proposed conversing risers, this study mainly focused on predicting pressure drop in the dilute phase pneumatic conveying system. The experiments were conducted in a converging riser having a convergence angle of 0.2693°. Various solid particles such as sago, black mustard, and alumina have been considered to study the effect of particle sizes and density on the pressure drop. The experimental outcomes indicate that the total pressure drop increases with an increase in the solid density and gas mass flow rate. Moreover, smaller particle sizes are also increased the pressure drop. An empirical correlation is developed for the prediction of total pressure drop ΔP_T in converging pneumatic riser via dimensional analysis. All dependent variables such as particle and air density, drag force, acceleration due to gravity, the mass flow rate of air and particle, the diameter of particle and converging riser, the height of converging riser were considered to develop the empirical correlation. The established relationship is tested, and experimental data have been fitted for its validation. The estimated relative error of less than 0.05 proved the significance of the developed correlation. Hence, it can be stated that the established relationship is useful in studying the effects of various parameters on the pressure drop across the length of the conversing riser.     

[2.2]Paracyclophane-Based TCN-201 Analogs as GluN2A-Selective NMDA Receptor Antagonists

Recent studies have shown the involvement of GluN2A subunit-containing NMDA receptors in various neurological and pathological disorders. In the X-ray crystal structure, TCN-201 ( 1 ) and analogous pyrazine derivatives 2 and 3 adopt a U-shape (hairpin) conformation within the binding site formed by the ligand binding domains of the GluN1 and GluN2A subunits. In order to mimic the resulting π/π-interactions of two aromatic rings in the binding site, a [2.2]paracyclophane system was designed to lock these aromatic rings in a parallel orientation. Acylation of [2.2]paracyclophane ( 5 ) with oxalyl chloride and chloroacetyl chloride and subsequent transformations led to the oxalamide 7 , triazole 10 and benzamides 12 . The GluN2A inhibitory activities of the paracyclophane derivatives were tested with two-electrode voltage clamp electrophysiology using Xenopus laevis oocytes expressing selectively functional NMDA receptors with GluN2A subunit. The o-iodobenzamide 12 b with the highest similarity to TCN-201 showed the highest GuN2A inhibitory activity of this series of compounds. At a concentration of 10 μM, 12 b reached 36 % of the inhibitory activity of TCN-201 ( 1 ). This result indicates that the [2.2]paracyclophane system is well accepted by the TCN-201 binding site.     

Optical design of the Lyman alpha based beam emission spectroscopy(LAB)diagnostic on the HL-2A tokamak

In this article, we present the optical design of a novel diagnostic on the HL-2 A tokamak, i.e. the20-channel edge Lyman-alpha beam emission spectroscopy, which is a promising solution for edge density turbulence research on tokamaks, as it offers the possibility of density fluctuation measurement with a 3.3 mm spatial resolution while maintains a high temporal resolution of1 μs. The optical path, including the reflective collection optics, the high-dispersion spectrometer, and the linear detector array, is carefully optimized to obtain a good image quality and a high throughput. The maximum root mean square radius of the collection optics is 64 μm.The detected photon flux is estimated to be about 10~(11) photons/s/channel.     

Transmission line detection in aerial images: An instance segmentation approach based on multitask neural networks

Camera-based transmission line detection (TLD) is a fundamental and crucial task for automatically patrolling powerlines by aircraft. Motivated by instance segmentation, a TLD algorithm is proposed in this paper with a novel deep neural network, i.e., CableNet. The network structure is designed based on fully convolutional networks (FCNs) with two major improvements, considering the specific appearance characteristics of transmission lines. First, overlaying dilated convolutional layers and spatial convolutional layers are configured to better represent continuous long and thin cable shapes. Second, two branches of outputs are arranged to generate multidimensional feature maps for instance segmentation. Thus, cable pixels can be detected and assigned cable IDs simultaneously. Multiple experiments are conducted on aerial images, and the results show that the proposed algorithm obtains reliable detection performance and is superior to traditional TLD methods. Meanwhile, segmented pixels can be accurately identified as cable instances, contributing to line fitting for further applications.