功能性小麦品种的概念、类别和发展前景（网络首发、推荐阅读）

结合全球倡导的营养导向型农业和功能性食品的内容，首次提出“功能性小麦品种”的概念，将其定义为“含有对人体健康有益的活性成分，可调节人体有益代谢，能给人体健康带来某种益处或满足特定人群的特殊需求，同时可以作为日常食物的口感正常、无毒副作用的小麦品种类型”；结合疫情警示和我国进入后工业时代后，人们需求必将由“吃得饱”、“吃得好”向“吃得健康”转变，因而提出继高产品种、优质品种之后培育“功能性小麦品种”的育种目标。根据多年关于小麦淀粉、蛋白、酯类和其他成分的功能研究结果，介绍新育成的“麦黄酮”、“高色素”、“高抗性淀粉”、“富锌”、“低醇溶蛋白”和“低植酸”等功能性小麦新品种（系）的营养特性和农艺产量状况；根据“健康中国2030”规划等国家战略，进行“功能性品种培育是解决我国功能性食品‘卡脖子’的关键基础，一种功能性品种可以形成一类功能性食品，多种功能性品种可以形成我国功能性面制品产业，推动我国整个食品工业的发展”的前景展望；根据功能性品种及其食品的稳定性和可靠性是产品和市场的“生命线”，从对消费者负责的高度，提出关于“功能性农作物品种审定导向和组建功能性成分检测机构；编制有关功能性品种和食品的国家或行业标准，设立功能性食品和功能性农作物品种的商业标志，保证我国功能性农作物品种及其食品健康发展”等方面的具体建议。     

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.     

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.     

A rapid detection method for the surface defects of mosaic ceramic tiles

Due to its unique artistic value, mosaic ceramics are widely used in construction-related fields. To meet the artist's demand for high-quality mosaic ceramic to create artistic works, it is necessary to meet the needs for efficient screening of mosaic ceramic tiles. Different from the ordinary large-target ceramics, mosaic ceramics exhibit characteristics of small tile sizes, a variety of colors, large demand for quantities, and easy reflection on the surface. Common manual detection methods show problems of low efficiency or accuracy, easy to fatigue, and many others. To solve these problems, this paper proposes a new detection method to identify surface defects of mosaic ceramic tiles and designs a detection system platform to achieve rapid detection. The experiment proves that the detection system has a detection rate of 93.99% for small defects on the surface of mosaic ceramic tiles, and the detection time of a single mosaic ceramic tile is less than 0.06 s. The detection method can quickly and accurately screen out high-quality, defect-free mosaic ceramic tiles, which can effectively improve the quality and artistic value of mosaic ceramic art creation.     

Machine fault diagnosis with small sample based on variational information constrained generative adversarial network

In actual engineering scenarios, limited fault data leads to insufficient model training and over-fitting, which negatively affects the diagnostic performance of intelligent diagnostic models. To solve the problem, this paper proposes a variational information constrained generative adversarial network (VICGAN) for effective machine fault diagnosis. Firstly, by incorporating the encoder into the discriminator to map the deep features, an improved generative adversarial network with stronger data synthesis capability is established. Secondly, to promote the stable training of the model and guarantee better convergence, a variational information constraint technique is utilized, which constrains the input signals and deep features of the discriminator using the information bottleneck method. In addition, a representation matching module is added to impose restrictions on the generator, avoiding the mode collapse problem and boosting the sample diversity. Two rolling bearing datasets are utilized to verify the effectiveness and stability of the presented network, which demonstrates that the presented network has an admirable ability in processing fault diagnosis with few samples, and performs better than state-of-the-art approaches.     

离线式带钢双面翻转检查站方案设计及实践

针对某酸轧机组检查站穿带困难、检查效率低、产生废品多、故障率高的问题,开发了一种离线式带钢双面检查站。该检查站新增了带钢翻转装置,将在入口分切剪处完成定尺剪切的带钢试样由两侧压板夹紧,中间可升降皮带机落下,翻转装置将带钢试样张紧,并翻转90°或者180°,实现带钢的双面检查。皮带机两侧布置有可升降、可横移的活动操作平台,检测人员站在平台上对带钢进行打磨检查,可同时对带钢上下表面进行检测。由于缩短了带钢的传输距离,有效减少了其他擦划伤的影响,提高了带钢表面质量检测准确性。     

The effects of washing a collagen sample prior to TEM examination

Transmission electron microscopy (TEM) is an important analysis technique to visualize (bio)macromolecules and their assemblies, including collagen fibers. Many protocols for TEM sample preparation of collagen involve one or more washing steps to remove excess salts from the dispersion that could hamper analysis when dried on a TEM grid. Such protocols are not standardized and washing times as well as washing solvents vary from procedure to procedure, with each research group typically having their own protocol. Here, we investigate the influence of washing with water, ethanol, but also methanol and 2-propanol, for both mineralized and unmineralized collagen samples via a protocol based on centrifugation. Washing with water maintains the hydrated collagen structure and the characteristic banding pattern can be clearly observed. Conversely, washing with ethanol results in dehydration of the fibrils, often leading to aggregation of the fibers and a less obvious banding pattern, already within 1 min of ethanol exposure. As we show, this process is fully reversible. Similar observations were made for methanol and propanol. Based on these results, a standardized washing protocol for collagenous samples is proposed.     

消防监督检查中对火灾隐患的认定探析

文章将重点阐述消防监督检查中对火灾隐患的认定,并从实际角度出发,充分的考虑可能存在的问题,具体问题具体分析,制定相应的解决对策,以保证消防监督检查工作能够顺利的开展.     

岳纸PM8 Optisizer生产实践

Optisizer在生产实践过程中的维护和操作控制关系到其是否正常稳定运行,本文主要介绍了施胶辊更换后的调试和断纸后的操作注意事项,以及运行过程中存在的亮线问题的一些探讨。