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.     

Efficient fault diagnosis method of PEMFC thermal management system for various current densities

The temperature of a fuel cell has a considerable impact on the saturation of a membrane, electrochemical reaction speed, and durability. So thermal management is considered one of the critical issues in polymer electrolyte membrane fuel cells. Therefore, the reliability of the thermal management system is also crucial for the performance and durability of a fuel cell system. In this work, a methodology for component-level fault diagnosis of polymer electrolyte membrane fuel cell thermal management system for various current densities is proposed. Specifically, this study suggests fault diagnosis using limited data, based on an experimental approach. Normal and five component-level fault states are diagnosed with a support vector machine model using temperature, pressure, and fan control signal data. The effects of training data at different operating current densities on fault diagnosis are analyzed. The effects of data preprocessing method are investigated, and the cause of misdiagnosis is analyzed. On this basis, diagnosis results show that the proposed methodology can realize efficient component-level fault diagnosis using limited data. The diagnosis accuracy is over 92% when the residual basis scaling method is used, and data at the highest operating current density is used to train the support vector machine.     

Distinctive Microbial Signatures and Gut-Brain Crosstalk in Pediatric Patients with Coeliac Disease and Type 1 Diabetes Mellitus

Coeliac disease (CD) and Type 1 diabetes mellitus (T1DM) are immune-mediated diseases. Emerging evidence suggests that dysbiosis in the gut microbiome plays a role in the pathogenesis of both diseases and may also be associated with the development of neuropathy. The primary goal in this cross-sectional pilot study was to identify whether there are distinct gut microbiota alterations in children with CD (n = 19), T1DM (n = 18) and both CD and T1DM (n = 9) compared to healthy controls (n = 12). Our second goal was to explore the relationship between neuropathy (corneal nerve fiber damage) and the gut microbiome composition. Microbiota composition was determined by 16S rRNA gene sequencing. Corneal confocal microscopy was used to determine nerve fiber damage. There was a significant difference in the overall microbial diversity between the four groups with healthy controls having a greater microbial diversity as compared to the patients. The abundance of pathogenic proteobacteria Shigella and E. coli were significantly higher in CD patients. Differential abundance analysis showed that several bacterial amplicon sequence variants (ASVs) distinguished CD from T1DM. The tissue transglutaminase antibody correlated significantly with a decrease in gut microbial diversity. Furthermore, the Bacteroidetes phylum, specifically the genus Parabacteroides was significantly correlated with corneal nerve fiber loss in the subjects with neuropathic damage belonging to the diseased groups. We conclude that disease-specific gut microbial features traceable down to the ASV level distinguish children with CD from T1DM and specific gut microbial signatures may be associated with small fiber neuropathy. Further research on the mechanisms linking altered microbial diversity with neuropathy are warranted.     

继电保护中多路模拟量采集系统的设计

介绍了一种基于高性能浮点DSP芯片TMS320C32、CPLD芯片XC95288和A/D采样芯片AD976组成的多路采集系统的工作原理以及设计方法。通过对第一路施加特殊的电压量,在CCS开发环境下读取采样缓冲区的值,并利用Matlab对采样数据进行了全波傅氏变换。此外,该系统已在继电保护中得到广泛应用,实践表明,该系统能较好地解决多路模拟量的采集,并确保了采样数据的安全可靠性。     

C语言与汇编语言的接口技术在DSP芯片中的应用

对于DSP芯片的控制程序的开发,采用C语言和汇编语言混合编程具有较高的效率。该文阐述了两种语言的接口方式和接口协议,并给出了采用这种接口技术实现混合编程在DSP芯片TM320C24x中的几种典型应用实例。     

基于TMS320LF2407A的三相低频电源设计

较好地解决了在三相交-交变频电源设计中所涉及的主回路结构、主回路环流控制方式、晶闸管触发角控制和调制等问题。并以DSP为控制器制作了一套低频电源实验装置,编制了相应的控制软件。实验结果成功实现了对输出电压频率和幅值的改变。     

数据挖掘与数据库知识发掘

介绍了数据库析近发展起来的数据挖掘（MD）和数据库发掘（KDD）技术，阐述了DM和KDD之间的区别和联系，综述了DM技术的常用方法，剖析了在处理DM问题时应注意的问题，并指出DM技术的优势和希望所在，最后对这两门交叉学科的发展提出了建议。     

Ersatz der entrichteten Pauschalgebühren auch bei teilweiser Klaglosstellung

Es w?re sachlich nicht gerechtfertigt, wenn zwar einem teilweise obsiegenden, nicht aber auch einem w?hrend des Nachprüfungsverfahrens teilweise klaglos gestellten Nachprüfungswerber ein Anspruch auf Ersatz der entrichteten Pauschalgebühren zustünde.     

基于正反HFM信号的水下高速小目标处理算法与试验分析

针对水下高速小目标的特点,提出了基于宽带正反双曲调频（HFM）信号的水下高速小目标检测与参数估计快速算法,并完成了仿真。通过在信号发射端加入时间伸缩因子来模拟目标运动,实现了水下高速小目标检测与参数估计的动态试验。并采集了雷体的反射回波数据。设计的以TMS320C6701为核心的信号处理系统,通过本文提出的快速算法实现了低信噪比下雷体反射回波检测与参数估计。试验结果表明,基于正反HFM信号的快速处理算法及数字信号处理（DSP）系统能有效完成低信噪比下的高速目标回波的检测与参数估计,与仿真结果具有较好的一致性．