基于多通道的ICU脑血管疾病死亡风险预测模型

死亡风险预测指根据病人临床体征监测数据来预测未来一段时间的死亡风险。对于ICU病患，通过死亡风险预测可以有针对性地对病人做出临床诊断，以及合理安排有限的医疗资源。基于临床使用的MEWS和Glasgow昏迷评分量表，针对ICU病人临床监测的17项生理参数，提出一种基于多通道的ICU脑血管疾病死亡风险预测模型。引入多通道概念应用于BiLSTM模型，用于突出每个生理参数对死亡风险预测的作用。采用Attention机制用于提高模型预测精度。实验数据来自MIMIC [Ⅲ]数据库，从中提取3?080位脑血管疾病患者的16?260条记录用于此次研究，除了六组超参数实验之外，将所提模型与LSTM、Multichannel-BiLSTM、逻辑回归（logistic regression）和支持向量机（support vector machine, SVM）四种模型进行了对比分析，准确率Accuracy、灵敏度Sensitive、特异性Specificity、AUC-ROC和AUC-PRC作为评价指标，实验结果表明，所提模型性能优于其他模型，AUC值达到94.3%。     

4D Printing of Shape Memory Materials for Textiles: Mechanism,Mathematical Modeling,and Challenges

Shape memory materials (SMMs) in 3D printing (3DP) technology garnered much attention due to their ability to respond to external stimuli, which direct this technology toward an emerging area of research, “4D printing (4DP) technology.” In contrast to classical 3D printed objects, the fourth dimension, time, allows printed objects to undergo significant changes in shape, size, or color when subjected to external stimuli. Highly precise and calibrated 4D materials, which can perform together to achieve robust 4D objects, are in great demand in various fields such as military applications, space suits, robotic systems, apparel, healthcare, sports, etc. This review, for the first time, to the best of the authors’ knowledge, focuses on recent advances in SMMs (e.g., polymers, metals, etc.) based wearable smart textiles and fashion goods. This review integrates the basic overview of 3DP technology, fabrication methods, the transition of 3DP to 4DP, the chemistry behind the fundamental working principles of 4D printed objects, materials selection for smart textiles and fashion goods. The central part summarizes the effect of major external stimuli on 4D textile materials followed by the major applications. Lastly, prospects and challenges are discussed, so that future researchers can continue the progress of this technology.     

The Function of Sialidase Revealed by Sialidase Activity Imaging Probe

Sialidase cleaves sialic acid residues from glycans such as glycoproteins and glycolipids. In the brain, desorption of the sialic acid by sialidase is essential for synaptic plasticity, learning and memory and synaptic transmission. BTP3-Neu5Ac has been developed for sensitive imaging of sialidase enzyme activity in mammalian tissues. Sialidase activity in the rat hippocampus detected with BTP3-Neu5Ac increases rapidly by neuronal depolarization. It is presumed that an increased sialidase activity in conjunction with neural excitation is involved in the formation of the neural circuit for memory. Since sialidase inhibits the exocytosis of the excitatory neurotransmitter glutamate, the increased sialidase activity by neural excitation might play a role in the negative feedback mechanism against the glutamate release. Mammalian tissues other than the brain have also been stained with BTP3-Neu5Ac. On the basis of information on the sialidase activity imaging in the pancreas, it was found that sialidase inhibitor can be used as an anti-diabetic drug that can avoid hypoglycemia, a serious side effect of insulin secretagogues. In this review, we discuss the role of sialidase in the brain as well as in the pancreas and skin, as revealed by using a sialidase activity imaging probe. We also present the detection of influenza virus with BTP3-Neu5Ac and modification of BTP3-Neu5Ac.     

Cognitive abilities,digital games and arithmetic performance enhancement: A study comparing the effects of a math game and paper exercises

Besides entertainment, games have shown to have the potential to impact a broader variety of cognitive abilities. Research has consistently shown that several aspects in cognition such as visual short-memory, multitasking and spatial skills can be enhanced by game play. In a previous study, it was found that playing Monkey Tales, a game aimed at training arithmetic skills, helped second grade pupils to increase their accuracy in mental calculation as compared to paper exercises. In this follow up study we explore whether traditional methods and game training differ in terms of the cognitive processes that both are able to impact. We incorporated standardized measures of working memory and visuo-motor skills. Additionally, the mathematics game was modified and its contents extracted to allow precise comparison between the gaming and paper exercises condition. Thus each single math exercise, type of question (e.g., multiple choice), quantity and order was perfectly matched in the game training and the traditional training conditions. Gains in arithmetical performance, and self-reported measures of enjoyment were also investigated. We found some evidence suggesting that arithmetic performance enhancement induced by game play and paper exercises differ not only in terms of enjoyment but also of working memory capacity improvements.     

Experimental study of crystal anisotropy based on ultra-precision cylindrical turning of single-crystal calcium fluoride

To realize ultimately efficient signal processing, it is necessary to replace electrical signal processing circuits with optical ones. The optical micro-resonator, which localizes light at a certain spot, is an essential component in optical signal processing. Single-crystal calcium fluoride (CaF₂) is the most suitable material for a highly efficient optical micro-resonator. The CaF₂ resonator can only be manufactured by ultra-precision machining processes, because its crystal anisotropy does not allow the application of chemical etching. However, the optical micro-resonator's performance depends definitely on the surface integrity.This study investigated the relationship between surface quality after ultra-precision machining and crystal anisotropy. Firstly, crack initiation was investigated on the (1 0 0), (1 1 0), and (1 1 1) planes using the micro-Vickers hardness test. Secondly, brittle-ductile transition was investigated by orthogonal cutting tests. Finally, cutting performance of cylindrical turning was evaluated, which could be a suitable method for manufacturing the CaF₂ resonator. The most difficult point in cylindrical turning of CaF₂ is that the crystalline plane and cutting direction vary continuously. In order to manufacture the CaF₂ optical micro-resonator more efficiently, analysis was conducted on crack initiation and surface quality of all crystallographic orientations from the perspective of slip system and cleavage.     

Resistive switching characteristics of ZnO–graphene quantum dots and their use as an active component of an organic memory cell with one diode-one resistor architecture

We investigated the resistive switching characteristics of a polystyrene:ZnO–graphene quantum dots system and its potential application in a one diode-one resistor architecture of an organic memory cell. The log–log I–V plot and the temperature-variable I–V measurements revealed that the switching mechanism in a low-current state is closely related to thermally activated transport. The turn-on process was induced by a space-charge-limited current mechanism resulted from the ZnO–graphene quantum dots acting as charge trap sites, and charge transfer through filamentary path. The memory device with a diode presented a ∼10³ I_ON/I_OFF ratio, stable endurance cycles (10² cycles) and retention times (10⁴ s), and uniform cell-to-cell switching. The one diode-one resistor architecture can effectively reduce cross-talk issue and realize a cross bar array as large as ∼3 kbit in the readout margin estimation. Furthermore, a specific word was encoded using the standard ASCII character code.     

冷轧平整机机前甩尾轧制工艺的开发与应用

本文介绍了柳钢冷轧板带厂1550 mm单机架平整机机前甩尾轧制的工艺。改进前轧制每卷钢卷生产效率低,钢卷成材率低,改进后生产效率、成材率明显提高,部分规格的钢卷能实现带尾剪切长度为零。     

Influences of chip serration on micro-topography of machined surface in high-speed cutting

Saw-tooth chip changes from macroscopically continuous ribbon to separated segments with the increase of cutting speed. The aim of this study is to find the correlations between chip morphology and machined surface micro-topography at different chip serration stages encountered in high speed cutting. High strength alloy steel AerMet100 was employed in orthogonal cutting experiments to obtain chips at different serration stages and corresponding machined surfaces. The chips and machined surfaces obtained were then examined with optical microscope (OM), scanning electron microscope (SEM), and white light interferometer (WLI). The result shows that chip serration causes micro-waves on machined surface, which increases machined surface roughness. However, wave amplitudes (surface roughness) at different serration stages are different. The principal factor influencing wave amplitude is the thickness of the sawed segment (tooth) of saw-tooth chip. With cutting parameters in this study, surface roughness contributed by chip serration ranges from 0.39 μm to 1.85 μm. This may bring on serious problems in the case of trying to replace grinding with high-speed cutting in rough machining. Some suggestions have been proposed to control the chip serration-caused surface roughness in high-speed cutting based on the results of the current study.     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.