Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.     

TiO2/MXene-Assisted LDI-MS for Urine Metabolic Profiling in Urinary Disease

Cancer remains an intractable medical problem. Rapid diagnosis and identification of cancer are critical to differentiate it from nonmalignant diseases. High-throughput biofluid metabolic analysis has potential for cancer diagnosis. Nevertheless, the present metabolite analysis method does not meet the demand for high-throughput screening of diseases. Herein, a high-throughput, cost-effective, and noninvasive urine metabolic profiling method based on TiO₂/MXene-assisted laser desorption/ionization mass spectrometry (LDI-MS) is presented for the efficient screening of bladder cancer (BC) and nonmalignant urinary disease. Combined with machine learning, TiO₂/MXene-assisted LDI-MS enables high diagnostic accuracy (96.8%) for the classification of patient groups (including 47 BC and 46 ureteral calculus (UC) patients) from healthy controls (113 cases). In addition, BC patients can also be identified from noncancerous UC individuals with an accuracy of 88.3% in the independent test cohort. Furthermore, metabolite variations between BC and UC individuals are investigated based on relative quantification, and related pathways are also discussed. These results suggest that this method, based on urine metabolic patterns, provides a potential tool for rapidly distinguishing urinary diseases and it may pave the way for precision medicine.     

Simulation of thermal barrier coating spallation induced by the initiation/growth/coalescence of internal crack and interfacial crack based on a real image model

In the furnace cycle test, the growth of oxide film leads to the propagation and coalescence of multiple cracks near the interface, which should be responsible for the spallation of thermal barrier coatings (TBCs). A TBC model with real interface morphology is created, and the near-interface large pore is retained. The purpose of this work is to clarify the mechanism of TBC spallation caused by successive initiation, propagation, and linkage of cracks near the interface during thermal cycle. The dynamic growth of thermally grown oxide (TGO) is carried out by applying a stress-free strain. The crack nucleation and arbitrary path propagation in YSZ and TGO are simulated by the extended finite element method (XFEM). The debonding along the YSZ/TGO/BC interface is evaluated using a surface-based cohesive behavior. The large-scale pore in YSZ near the interface can initiate a new crack. The ceramic crack can propagate to the YSZ/TGO interface, which will accelerate the interfacial damage and debonding. For the TGO/BC interface, the normal compressive stress and small shear stress at the valley hinder the further crack propagation. The growth of YSZ crack and the formation of through-TGO crack are the main causes of TBC delamination. The accelerated BC oxidation increases the lateral growth strain of TGO, which will promote crack propagation and coalescence. The optimization design proposed in this work can provide another option for developing TBC with high durability.     

Towards artificial intelligence at scale in the chemical industry

In the Industry 4.0 era, the chemical industry is embracing broad adoption of artificial intelligence (AI) and machine learning (ML) methods. This article provides a holistic view of how the industry is transforming digitally towards AI at scale. First, a historical perspective on how the industry used AI to aid humans in better decision-making is shown. Then state-of-the-art AI research addressing industrial needs on reliability and safety, process optimization, supply chain, material discovery, and reaction engineering is highlighted. Finally, a vision of the plant of the future is illustrated with critical components of AI-ready culture, model life cycle management, and renewed role of humans in chemical manufacturing.     

镁合金网格壁板压弯成形数值模拟及实验研究

为探索镁合金整体壁板压弯成形的可行性,以及镁合金壁板压弯成形过程中金属的流动规律,对AZ31镁合金网格壁板压弯成形进行了数值模拟和实验研究。建立了有限元数值模拟的几何模型,采用有限元计算软件对AZ31镁合金网格壁板压弯成形过程进行了数值模拟研究,分析了镁合金网格壁板压弯成形中的温度场、应变场、应力场、破坏系数等的分布规律。确定了合适的AZ31镁合金壁板压弯成形工艺参数,并对镁合金网格壁板压弯成形进行了实验研究,获得了合格的镁合金网格壁板弯曲件,并分析了镁合金网格壁板成形件尺寸精度,模拟结果与实验结果相吻合,最大相对误差为16.7%。     

User idea implementation in open innovation communities: Evidence from a new product development crowdsourcing community

In collaborative crowdsourcing communities for open innovation, users generate and submit ideas as idea co‐creators. Firms then select and implement valuable ideas for new product development. Despite the popularity and success of these open innovation communities, relatively little is known about the factors that determine the implementation of the user‐generated ideas. Based on research on individual creativity, we propose a conceptual model integrating users' previous experience, idea presentation characteristics and feedback valence to explain the likelihood of idea implementation. We validate our research model with a panel data analysis of 43 550 ideas submitted by 16 360 users in the MIUI new product development community hosted by Xiaomi, a large electronics manufacturing company in China. We find an inverted U‐shaped relationship between users' past successful experience and idea implementation. Furthermore, the length of ideas is positively associated with the likelihood of idea implementation. There is also an inverted U‐shaped relationship between supporting evidence and idea implementation. Finally, we demonstrate the negative effect of positive feedback and the positive effect of negative feedback on idea implementation. These findings offer rich insights to understand the phenomenon of open innovation better. Theoretical and practical implications are discussed.     

基于概率分析的核电传热管降质失效评估方法研究

发展核电是国家能源安全战略的重要组成部分，核电安全是对核电事业发展的最基本要求。依据核电传热管的检测历史数据和专家数据，利用全概率理论和Bayes公式提出了对传热管降质完整性评估的概率计算方法。通过对应力腐蚀、疲劳损伤等影响因素的分析，实现了在不同应力腐蚀和疲劳损伤条件下对传热管降质分布的概率计算。该评估方法可以将多个影响因素共同作用的复杂问题，简化为单个影响因素的逐一修正问题，简化了计算和处理方法。该方法可为核电蒸汽发生器传热管的降质概率评估提供借鉴。     

基于改进奇异值分解滤波和谱峭度的滚动轴承故障诊断

针对含噪信号的有效奇异值个数难以确定的问题，提出了一种改进的奇异值分解降噪方法--奇异值累积法。该方法通过计算奇异值的实际下降值与奇异值平均下降速度累积量的差值，并取该差值最大值点的位置作为有效奇异值的分界点来确定有效奇异值的个数。在此基础上，提出了一种基于奇异值累积法与快速谱峭度的滚动轴承故障诊断方法。采用奇异值累积法对原信号进行降噪处理，然后利用快速谱峭度确定滤波器中心频率及带宽，通过分析频段包络谱中明显的频率成分来诊断故障。该方法可以有效去除信号中的噪声，使得到的峭度值所反映的故障冲击更接近实际情况。对含内圈、外圈故障的滚动轴承实验数据进行分析，实验结果表明，相比快速谱峭度的故障诊断方法，该方法具有更好的故障识别效果。     

Time dependent failure probability estimation of the solid oxide fuel cell by a creep-damage related Weibull distribution model

In present paper, a new model is proposed and embedded into the finite element software ABAQUS to estimate the time dependent failure probability of the solid oxide fuel cell stack. The results show that sealant is the potential failure region of the solid oxide fuel cell stack, while the failure probability of the anode, electrolyte and cathode are very small within the operation time of 50,000 h. The creep and damage distribution of the components reflect that the proposed model can reasonably predict the time dependent failure probability of the solid oxide fuel cell stack. Increasing either the characteristic strain, Weibull modulus or decreasing the operating temperature can decrease the failure probability of the SOFC stack. For the sealant, to ensure the high temperature integrity of the SOFC stack, the characteristic strain should be larger than 0.01 or Weibull modulus should be higher than 8.0 under the operating temperature of 600 °C.     

基于主元分析的煤矿空压机故障诊断研究

针对目前煤矿空压机监测出现故障诊断复杂、人工成本高等问题，采用主元分析方法，对煤矿空压机故障诊断方法进行研究。研究了煤矿空压机主元故障预警方法以及煤矿空压化故障诊断检测方法，并以某煤矿为例，系统在正常运行的情况下，采集传感器的样本数据，建立预警模型，然后采取实验测试数据进行检验认证，验证结果显示主元分析方法，能够有效准确地把故障信息准确提取出来，确保煤矿空压机能够准确进行故障诊断，从而达到矿井安全生产的目的。