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.     

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster’s inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.     

Construction and analysis of photovoltaic directly coupled conditions in PEM electrolyzer

The Markov model and the PEM electrolyzer system model for directly coupled photovoltaic are combined to construct an efficient and reliable working condition that fits the fluctuation characteristics of solar energy. The working condition is designed through genetic algorithm so that the average coupling efficiency of the system can reach 98.8%. Then, the durability and recovery test are conducted on the basis of the constructed conditions. It is found that the attenuation rate at the current density of 1A/cm² under the photovoltaic fluctuating condition reached 7.8mV/h, which is twice that under the constant current condition. The charge transfer impedance (R_ct) is the main factor leading to the degradation. It is proved by the recovery experiment that the increase of R_ct is related to the pollution of metal ions. After pickling to remove some metal ions, R_ct can be significantly reduced by 46.8% and 65.2%, respectively. After the durability test, the voltammetric charges under the photovoltaic fluctuating condition and the constant current condition are reduced by 48.3% and 19.1% It indicates that the photovoltaic fluctuation condition will accelerate the attenuation of the effective reaction area of MEA, which is irreversible even after pickling. It can be observed from the SEM images that the catalyst layer of MEA has more obvious peeling under the photovoltaic fluctuation condition, which is not conducive to material transmission and destroys the transmission channel of ions and electrons. This result can provide a reliable reference for the coupling design of PEM electrolyzer and renewable energy in the future.     

地表径流速度对城市内涝影响规律

为分析地表径流速度对城市内涝的影响,采用一维地下排水管网与二维城区地形的动态耦合模型,选取大连市某排水区块作为研究区域,设置4种地表径流速度10种设计降雨场景,模拟分析在不同重现期设计降雨及不同地表径流速度下研究区的内涝积水特性。结果表明:随着地表径流速度降低,管道排水压力变小,管道排水达标率最高可提升48.05%,且降雨重现期越短,地表径流流速对管道排水压力的削减效果越明显;地表径流流速对检查井溢流量影响显著,随着地表径流速度降低,检查井溢流量峰值最高可减小2 750 m~3,峰现时间最长可滞后56 min,同时随着降雨重现期增长,地表径流流速对检查井溢流量的削减效果减弱;研究区低、高风险区淹没面积随地表径流速度降低,最高可分别减小1.64万、8.37万m~2,但中风险区淹没面积变化反复。     

Analytical Modeling for Translating Statistical Changes to Circuit Variability by Ultra-Deep Submicron Digital Circuit Design

This paper presents a physics-based compact gate delay model that includes all short-channel phenomena prevalent at the ultra-deep submicron technology node of 32 nm. To simplify calculations, the proposed model is connected to a compact α-power law-based (Sakurai-Newton) model. The model has been tested on a wide range of supply voltages. The model accurately predicts nominal delays and the delays under process variations. It has been shown that at lower technology nodes, the delay is more sensitive to threshold voltage variations, specifically at the sub-threshold operating region as compared with effective channel length variations above the threshold region.     

Thermal shock effects on the impact resistance,tolerance and flexural strength of alumina based oxide/oxide ceramic matrix composites

In this study the effects of thermal shock on the impact damage resistance, damage tolerance and flexural strength of Nextel 610/alumina silicate ceramic matrix composites were experimentally evaluated. Composite laminates with balanced and symmetric layup were gradually heated to 1200°C in an air-based furnace and held for at least 30 min before being removed and immersed in water at room temperature. The laminates were then subjected to low velocity impacts via a hemispherical steel impactor. The resultant damage was characterized non-destructively, following which the laminates were subjected to compression tests. Three-point bend tests were also performed to evaluate the effect of thermal shock on the flexural strength and related failure modes of the laminates. Thermally shocked laminates showed smaller internal damage and larger external damage areas in comparison to their pristine counterparts. For the impact energy and resultant damage size considered, the residual compressive strengths for the thermally shocked and pristine laminates were similar.     

Adaptation to Endoplasmic Reticulum Stress Enhances Resistance of Oral Cancer Cells to Cisplatin by Up-Regulating Polymerase η and Increasing DNA Repair Efficiency

Endoplasmic reticulum (ER) stress response is an adaptive program to cope with cellular stress that disturbs the function and homeostasis of ER, which commonly occurs during cancer progression to late stage. Late-stage cancers, mostly requiring chemotherapy, often develop treatment resistance. Chemoresistance has been linked to ER stress response; however, most of the evidence has come from studies that correlate the expression of stress markers with poor prognosis or demonstrate proapoptosis by the knockdown of stress-responsive genes. Since ER stress in cancers usually persists and is essentially not induced by genetic manipulations, we used low doses of ER stress inducers at levels that allowed cell adaptation to occur in order to investigate the effect of stress response on chemoresistance. We found that prolonged tolerable ER stress promotes mesenchymal–epithelial transition, slows cell-cycle progression, and delays the S-phase exit. Consequently, cisplatin-induced apoptosis was significantly decreased in stress-adapted cells, implying their acquisition of cisplatin resistance. Molecularly, we found that proliferating cell nuclear antigen (PCNA) ubiquitination and the expression of polymerase η, the main polymerase responsible for translesion synthesis across cisplatin-DNA damage, were up-regulated in ER stress-adaptive cells, and their enhanced cisplatin resistance was abrogated by the knockout of polymerase η. We also found that a fraction of p53 in stress-adapted cells was translocated to the nucleus, and that these cells exhibited a significant decline in the level of cisplatin-DNA damage. Consistently, we showed that the nuclear p53 coincided with strong positivity of glucose-related protein 78 (GRP78) on immunostaining of clinical biopsies, and the cisplatin-based chemotherapy was less effective for patients with high levels of ER stress. Taken together, this study uncovers that adaptation to ER stress enhances DNA repair and damage tolerance, with which stressed cells gain resistance to chemotherapeutics.     

Radiation- and Photo-Induced Oxidation Pathways of Methionine in Model Peptide Backbone under Anoxic Conditions

Within the reactive oxygen species (ROS) generated by cellular metabolisms, hydroxyl radicals (HO^•) play an important role, being the most aggressive towards biomolecules. The reactions of HO^• with methionine residues (Met) in peptides and proteins have been intensively studied, but some fundamental aspects remain unsolved. In the present study we examined the biomimetic model made of Ac-Met-OMe, as the simplest model peptide backbone, and of HO^• generated by ionizing radiation in aqueous solutions under anoxic conditions. We performed the identification and quantification of transient species by pulse radiolysis and of final products by LC-MS and high-resolution MS/MS after γ-radiolysis. By parallel photochemical experiments, using 3-carboxybenzophenone (CB) triplet with the model peptide, we compared the outcomes in terms of short-lived intermediates and stable product identification. The result is a detailed mechanistic scheme of Met oxidation by HO^•, and by CB triplets allowed for assigning transient species to the pathways of products formation.