Vision-related quality of life with myopia management: A review

The disease of myopia has come into focus as a worldwide public health concern. Myopia has shown increasing prevalence, incidence at earlier age and progression to a higher degree. Progressive increase in degree of myopia is strongly associated with increase in axial length of the eye. Various interventions have been shown to slow axial elongation in children. These interventions have been studied to assess efficacy in slowing axial elongation and correction of vision. In addition, research into quality of vision, risk of adverse events, overall safety and impact on vision-related quality (VR-QoL) of life has been pursued. In contrast, studies have been published to demonstrate the risks of myopia, high myopia and increased axial length. This review will discuss VR-QoL assessment on the most effective and most commonly prescribed interventions to slow axial elongation and myopia progression. The patient attributes considered are VR-QoL scores from validated instruments. The development and use of validated survey instruments to assess the patient-reported outcomes is discussed. The review demonstrates that there are numerous factors that may impact VR-QoL to evaluate in the decision-making process when eye care providers consider when, how and if to prescribe myopia management (MM) for children with myopia.     

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.     

空间曲线的特征识别与高质量非均匀采样

为避免传统均匀采样方法因忽视曲线重要特征而生成不理想的采样结果,获得给定数量且由特征点和辅助点组成的采样点序列,提出基于特征识别的高质量空间曲线非均匀采样方法.首先使用抛物线插值法得到曲线上所有曲率极大值点和挠率极大值点的近似位置,经筛选后产生特征点,以更好地抓住空间曲线的轮廓特征.然后定义基于弧长、曲率和挠率加权组合的特征函数,并以此自适应地选取曲线上的辅助点.与3种主流采样方法比较的实验结果表明,该方法能够获得更高质量的采样结果且具有更好的实用性,从而进一步改善空间曲线的B样条拟合效果.     

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。     

Predictive process mapping for laser powder bed fusion: A review of existing analytical solutions

One of the main challenges in the laser powder bed fusion (LPBF) process is making dense and defect-free components. These porosity defects are dependent upon the melt pool geometry and the processing conditions. Power-velocity (PV) processing maps can aid in visualizing the effects of LPBF processing variables and mapping different defect regimes such as lack-of-fusion, under-melting, balling, and keyholing. This work presents an assessment of existing analytical equations and models that provide an estimate of the melt pool geometry as a function of material properties. The melt pool equations are then combined with defect criteria to provide a quick approximation of the PV processing maps for a variety of materials. Finally, the predictions of these processing maps are compared with experimental data from the literature. The predictive processing maps can be computed quickly and can be coupled with dimensionless numbers and high-throughput (HT) experiments for validation. The present work provides a boundary framework for designing the optimal processing parameters for new metals and alloys based on existing analytical solutions.     

Removing data and using metafounders alleviates biases for all traits in Lacaune dairy sheep predictions

Bias in dairy genetic evaluations, when it exists, has to be understood and properly addressed. The origin of biases is not always clear. We analyzed 40 yr of records from the Lacaune dairy sheep breeding program to evaluate the extent of bias, assess possible corrections, and emit hypotheses on its origin. The data set included 7 traits (milk yield, fat and protein contents, somatic cell score, teat angle, udder cleft, and udder depth) with records from 600,000 to 5 million depending on the trait, ～1,900,000 animals, and ～5,900 genotyped elite artificial insemination rams. For the ～8% animals with missing sire, we fit 25 unknown parent groups. We used the linear regression method to compare “partial” and “whole” predictions of young rams before and after progeny testing, with 7 cut-off points, and we obtained estimates of their bias, (over)dispersion, and accuracy in early proofs. We tried (1) several scenarios as follows: multiple or single trait, the “official” (routine) evaluation, which is a mixture of both single and multiple trait, and “deletion” of data before 1990; and (2) several models as follows: BLUP and single-step genomic (SSG)BLUP with fixed unknown parent groups or metafounders, where, for metafounders, their relationship matrix gamma was estimated using either a model for inbreeding trend, or base allele frequencies estimated by peeling. The estimate of gamma obtained by modeling the inbreeding trend resulted in an estimated increase of inbreeding, based on markers, faster than the pedigree-based one. The estimated genetic trends were similar for most models and scenarios across all traits, but were shrunken when gamma was estimated by peeling. This was due to shrinking of the estimates of metafounders in the latter case. Across scenarios, all traits showed bias, generally as an overestimate of genetic trend for milk yield and an underestimate for the other traits. As for the slope, it showed overdispersion of estimated breeding values for all traits. Using multiple-trait models slightly reduced the overestimate of genetic trend and the overdispersion, as did including genomic information (i.e., SSGBLUP) when the gamma matrix was estimated by the model for inbreeding trend. However, only deletion of historical data before 1990 resulted in elimination of both kind of biases. The SSGBLUP resulted in more accurate early proofs than BLUP for all traits. We considered that a snowball effect of small errors in each genetic evaluation, combined with selection, may have resulted in biased evaluations. Improving statistical methods reduced some bias but not all, and a simple solution for this data set was to remove historical records.     

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.     

南水北调中线总干渠藻类的生态调度

南水北调中线总干渠无在线调蓄水库,对藻类生态调度过程中出现的问题开展生态调度实现策略和实施方式研究。主要实现策略包括:划定自身的调蓄区,隔离生态调度对下游的影响;采用高效的渠池运行方式,减少生态调度时蓄量的反复调整;综合考虑安全、快速、平稳等需求,设定生态调度实施进程和方式。具体实施方式包括:将总干渠划分为流速调控区、调蓄区和正常运行区,分别实施等体积、控制蓄量和闸前常水位方式运行;将生态调度过程划分为充水阶段和泄水阶段,基于流速调控目标值、持续时长和水位降幅约束条件,确定各阶段时长和各分区的闸门群调控方案等。基于2018年3月输水工况,采用明渠一维非恒定流模型,仿真总干渠上游15个渠池的藻类生态调度过程。结果表明,生态调度可在3.5 d内完成,各渠池的平均流速由0.48 m/s增至0.93 m/s,持续时间超过2 h。在整个生态调度过程中,水位变化平稳,水位变幅符合安全阈值要求,下游渠道的正常运行未受生态调度明显影响。