Influence of temperature on acoustic emission source location accuracy in underground structure

An acoustic emission (AE) experiment was carried out to explore the AE location accuracy influenced by temperature. A hollow hemispherical specimen was used to simulate common underground structures. In the process of heating with the flame, the pulse signal of constant frequency was stimulated as an AE source. Then AE signals received by each sensor were collected and used for comparing localization accuracy at different temperatures. Results show that location errors of AE keep the same phenomenon in the early and middle heating stages. In the later stage of heating, location errors of AE increase sharply due to the appearance of cracks. This provides some beneficial suggestions on decreasing location errors of structural cracks caused by temperature and improves the ability of underground structure disaster prevention and control.     

Investigation on thermal conductivity and mechanical properties of Si3N4 ceramics via one-step sintering

High thermal conductivity Si₃N₄ ceramics were fabricated using a one-step method consisting of reaction-bonded Si₃N₄ (RBSN) and post-sintering. The influence of Si content on nitridation rate, β/(α+β) phase rate, thermal conductivity and mechanical properties was investigated in this work. It is of special interest to note that the thermal conductivity showed a tendency to increase first and then decrease with increasing Si content. This experimental result shows that the optimal thermal conductivity and fracture toughness were obtained to be 66 W (m K)^-1 and 12.0 MPa m^1/2, respectively. As a comparison, the nitridation rate and β/(α+β) phase rate in a static pressure nitriding system, i.e., 97% (MS10), 97% (MS15), 97% (MS20) and 8.3% (MS10), 8.3% (MS15), 8.9% (MS20), respectively, have obvious advantages over those in a flowing nitriding system, i.e., 91% (MS10), 91% (MS15), 93% (MS20) and 3.1% (MS10), 3.3% (MS15), 3.3% (MS20), respectively. Moreover, high lattice integrity of the β-Si₃N₄ phase was observed, which can effectively confine O atoms into the β-Si₃N₄ lattice using MgO as a sintering additive. This result indicates that one-step sintering can provide a new route to prepare Si₃N₄ ceramics with a good combination of thermal conductivity and mechanical properties.     

爆破位置对深凹露天矿山炮烟扩散的影响研究

深凹露天矿山由于其特殊的结构,爆破产生的炮烟扩散稀释较为困难,严重危害生产作业人员的生命安全与健康。基于实际矿山构建了深凹露天矿山的二维物理及数学模型,采用非稳态数值分析方法研究了不同爆破位置下,深凹露天矿山采坑内爆破炮烟的扩散规律。研究结果表明：不同爆破位置下,露天采坑内均出现复环流,爆破点位置是影响露天采坑内风流结构特征的重要因素；露天采坑内的炮烟最高浓度均随着时间变化而逐渐下降,但下降的速率逐步减小,呈现三个阶段的下降趋势；爆破位置位于背风侧时露天采坑内的炮烟最高浓度和降至安全浓度所需时间远高于迎风侧三个爆破位置；随着背风侧爆破点距采坑底部距离的减小,炮烟最高浓度及降至安全浓度所需时间先降低后增加,炮烟最高浓度及降至安全浓度所需时间随着迎风侧爆破位置距采坑底部距离的减小而增加。研究结果对于指导深凹露天矿山企业合理组织爆破后的生产作业和保障作业人员安全具有重要意义。     

微波辅助孵育法富集麦胚多肽的工艺优化

目的 麦胚孵育过程中蛋白酶将蛋白质水解成肽和氨基酸，多肽具有明确的生理活性和营养调节作用。本研究以麦胚为试验原料，采用微波辅助预处理的方法，研究孵育的温度、时间、pH及料液比4种因素对孵育后的蛋白酶活力及多肽含量的影响。 方法 通过单因素和响应曲面试验进行工艺条件优化。 结果 与未进行微波辅助预处理的样品相比，微波辅助处理（600 W, 10 s）能显著提高孵育后样品中的蛋白酶酶活力（约9.4倍）和肽含量（约3.1倍）。经过微波辅助处理后，孵育温度为51.5℃、pH为4.0、时间为6.33 h、料液比为1:7时，蛋白酶活力达最高为3826.24 U/g；孵育温度为45.0 ℃、pH为4.8、时间为8 h、料液比为1:7时，肽含量达最高为262.63 mg/g。 结论 微波辅助处理能有效的激活麦胚孵育液中的内源性蛋白酶活性，促进蛋白水解反应，显著提高孵育后的肽含量。该研究结果为麦胚多肽的制备新工艺开发提供了研究基础。     

The effect of biomass feeding location on rice husk gasification for hydrogen production

The objective of this study is to investigate the impact of biomass feeding location on rice husk gasification for hydrogen production. By comparing the results between top-feed and bottom-feed of the feedstock of the fluidized bed biomass gasification at the reaction temperature between 600～1000 °C and ER = 0.2, 0.27, and 0.33 without steam, the optimum low heating value was increase by 2.35 kJ/g-rice husk by the top-feed to gasifier. Although the yield of hydrogen was decreased by 42% for the rice husk gasification by the top-feed operation, the yield of CO, CO2, and CH4 were highly increased, which enhancing the heating value of the effluent gas. The study results suggested the potential route of the biomass gasification at the different feeding location.     

No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

Influence of metal-support interactions on reaction pathways over Ni/CeZrOx–Al2O3 catalysts for ethanol steam reforming

This work investigates selective Ni locations over Ni/CeZrO_x–Al₂O₃ catalysts at different Ni loading contents and their influences on reaction pathways in ethanol steam reforming (ESR). Depending on the Ni loading contents, the added Ni selectively interacts with CeZrO_x–Al₂O₃, resulting in the stepwise locations of Ni over CeZrO_x–Al₂O₃. This behavior induces a remarkable difference in hydrogen production and coke formation in ESR. The selective interaction between Ni and CeZrO_x for 10-wt.% Ni generates more oxygen vacancies in the CeZrO_x lattice. The Ni sites near the oxygen vacancies enhance reforming via steam activation, resulting in the highest hydrogen production rate of 1863.0 μmol/g_cat·min. In contrast, for 15 and 20-wt.% Ni, excessive Ni is additionally deposited on Al₂O₃ after the saturation of Ni–CeZrO_x interactions. These Ni sites on Al₂O₃ accelerate coking from the ethylene produced on the acidic sites, resulting in a high coke amount of 19.1 mg_c/g_cat·h (20Ni/CZ-Al).     

YOLSO: You Only Look Small Object

Small object detection is challenging and far from satisfactory. Most general object detectors suffer from two critical issues with small objects: (1) Feature extractor based on classification network cannot express the characteristics of small objects reasonably due to insufficient appearance information of targets and a large amount of background interference around them. (2) The detector requires a much higher location accuracy for small objects than for general objects. This paper proposes an effective and efficient small object detector YOLSO to address the above problems. For feature representation, we analyze the drawbacks in previous backbones and present a Half-Space Shortcut(HSSC) module to build a background-aware backbone. Furthermore, a coarse-to-fine Feature Pyramid Enhancement(FPE) module is introduced for layer-wise aggregation at a granular level to enhance the semantic discriminability. For loss function, we propose an exponential L1 loss to promote the convergence of regression, and a focal IOU loss to focus on prime samples with high classification confidence and high IOU. Both of them significantly improves the location accuracy of small objects. The proposed YOLSO sets state-of-the-art results on two typical small object datasets, MOCOD and VeDAI, at a speed of over 200 FPS. In the meantime, it also outperforms the baseline YOLOv3 by a wide margin on the common COCO dataset.