Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

Kinetic Monte Carlo simulation of hydrogen production from photocatalytic water splitting in the presence of methanol by 1 wt% Au/TiO2

In this research, using the kinetic Monte Carlo simulation (KMC), the hydrogen production from a water-methanol mixture using Au/TiO₂ photocatalyst is investigated. A mechanism is proposed, and the rate constants of the reaction steps are specified. The reaction rate constants of different steps and the concentration of the active sites on the photocatalyst surface were determined. An excellent match between simulated and experimental data confirms the results. The electron-hole pair production, methanol adsorption on the photocatalyst surface, and electron-hole recombination steps are considered the most critical steps. To study the effects of independent variables (initial concentration of methanol, photocatalyst dosage, pH, and time of reaction) on the produced hydrogen, a combination of KMC simulation and design of experiment was employed. The concentration of photocatalysis has the highest and pH has the lowest effect on the hydrogen production. The optimal conditions for photocatalytic hydrogen production are presented.     

格点QCD基础求解器及其异构计算实现的性能优化

格点量子色动力学(格点QCD)是研究夸克、胶子等微观粒子间相互作用的重要理论和方法. 通过将时空离散化为四维结构网格, 并将量子色动力学的基本场量定义在网格上, 让研究人员可以使用数值模拟方法, 从第一性原理出发研究强子间相互作用和性质, 但这个过程中的计算量极大, 需要进行大规模并行计算. 格点QCD计算的核心基础为格点QCD求解器, 是程序运行主要的计算热点模块. 本文研究在国产异构计算平台下格点QCD求解器的实现与优化, 提出一套格点QCD求解器的设计实现, 实现了BiCGSTAB求解器, 显著降低了迭代次数; 通过对奇偶预处理技术, 降低了所求问题的计算规模; 针对国产异构加速卡的特点, 优化了Dslash模块的访存操作. 实验测试表明, 相比优化前的求解器获得了约30倍的加速比, 为国产异构超算下格点QCD软件性能优化提供了有益的参考价值.     

Facile synthesis of Ti3C2 MXene-modified Bi2.15WO6 nanosheets with enhanced reactivity for photocatalytic reduction of Cr(VI)

Through a facile hydrothermal method, we have successfully prepared Ti₃C₂/Bi_2.15WO₆ (TC/BWO) composite, and systematically investigated their reactivity for the photocatalytic reduction of Cr(VI) under visible light. X-ray diffraction and Raman analysis confirm the formation of heterostructure between Bi_2.15WO₆ and Ti₃C₂. The resultant 7TC/BWO composite exhibits enhanced photoactivity toward Cr(VI) reduction. After 120 min irradiation, the conversion of Cr(VI) reaches 92.5% with the quasi-first-order kinetic constant of k = 0.0145 min^?1, which is higher than that of pure BWO (30% and k = 0.0005 min^?1). The electrochemical and photoluminescent characterization confirm that the introduction of Ti₃C₂ is conducive to the separation of carriers, thus significantly improves the photocatalytic performance of TC/BWO. Furthermore, the radical capture experiments verify that the electrons are important for enhancing reduction of Cr(VI) to Cr(III). As a result, this research provides a comprehensive understanding of the reduction of Cr(VI) by TC/BWO composite under visible light.     

复杂环境下氧化铝储槽定向爆破拆除

在较为复杂的环境下,爆破拆除钢筋混凝土氧化铝储槽。该储槽自重大、呈圆形,内有4根立柱支撑下料漏斗。为使储槽顺利定向倒塌,通过爆破方案选择、参数确定,采取梯形切口和预处理以及安全防护和减振措施,使储槽爆破拆除获圆满成功。     

基准平面垂直断面法在爆破漏斗试验中的应用

系统阐述了基准平面垂直断面法在爆破漏斗试验中测量爆破漏斗体积的基本原理,并将隧道激光断面仪应用于金厂河矿1 750 m水平15^#采场底部切割巷道爆破漏斗试验爆破漏斗体积测量中。通过与传统体重法等计算法所得漏斗体积分析比较,结果表明基于隧道激光断面仪与3D Mine软件分析的基准平面垂直断面法实用性强、操作方便、结果直观可靠,达到试验预期目的。     

Influence of CO2 inleakage on the slight-alkalization of generator internal cooling water

The slight-alkalization of generator internal cooling water (GICW) is widely used to inhibit the corrosion of hollow copper conductor and thereby ensure the safe operation of the generator. CO₂ inleakage is increasingly identified as a potential security risk for GICW system. In this paper, the influence of CO₂ inleakage on the slight-alkalization of GICW was theoretically discussed. Based on the equilibriums of the CO₂-NaOH-H₂O system, CO₂ inleakage saturation was derived to quantify the amount of the dissolved CO₂ in GICW. This parameter can be directly calculated with the measured conductivity and the [Na⁺] of GICW. The influence of CO₂ inleakage on the slight-alkalization conditioning of GICW and the measurement of its water quality parameters were then analyzed. The more severe the inleakage, the narrower the water quality operation ranges of GICW, resulting in the more difficult the slight-alkalization conditioning of GICW. The temperature calibrations of the conductivity and the pH value of GICW show non-linear correlations with the amount of CO₂ inleakage and the NaOH dosage. This study provides insights into the influence of CO₂ inleakage on the slight-alkalization of GICW, which can serve as the theoretical basis for the actual slight-alkalization when CO₂ inleakage occurs.     

Improving accuracy of automatic optical inspection with machine learning

Electronic devices require the printed circuit board(PCB)to support the whole structure,but the assembly of PCBs suffers from welding problem of the electronic components such as surface mounted devices(SMDs)resistors.The automated optical inspection(AOI)machine,widely used in industrial production,can take the image of PCBs and examine the welding issue.However,the AOI machine could commit false negative errors and dedicated technicians have to be employed to pick out those misjudged PCBs.This paper proposes a machine learning based method to improve the accuracy of AOI.In particular,we propose an adjacent pixel RGB value based method to pre-process the image from the AOI machine and build a customized deep learning model to classify the image.We present a practical scheme including two machine learning procedures to mitigate AOI errors.We conduct experiments with the real dataset from a production line for three months,the experimental results show that our method can reduce the rate of misjudgment from 0.3%–0.5%to 0.02%–0.03%,which is meaningful for thousands of PCBs each containing thousands of electronic components in practice.     

Influence of applied voltage and conductive material in DIET promotion for methane generation

The utilization of biological-, electrode- and conductive material-mediated direct interspecies electron transfer (DIET) between exoelectrogenic bacteria and methanogenic archaea for enhancing methane productivity is widely reported in the literature. However, two cardinal questions are still controversial, i.e., which applied voltage value would be more recommended to enhance methane generation? and how the DIET over IIET has the upper hand in enhancing methane productivity? Herein, the influence of different applied voltages to promote biological-, conductive- and electrode-mediated DIET was investigated in MEC-AD reactors with conductive material. Polarized bioelectrodes induced electrode-mediated DIET (eDIET) and biological DIET (bDIET), in addition to cDIET (conductive material-mediated DIET), improved the methane yield to 315.40 mL/g COD_r with an applied voltage of 0.9 V. Whereas further increase of applied voltage 1.2 V, lessened methane production efficiency due to high-voltage inhibition and adverse effect on DIET promotion. The anaerobic digestion coupled microbial electrolysis cells with optimal electric potential selectively promotes the DIET through polarized electrodes were confirmed through microbial analysis. As the contribution of DIET increased to 80%, the methane yield increased, and the substrate residue decreased, resulting in a significant improvement in methane production.