An improved Henry gas solubility optimization for optimization tasks

Applied Intelligence - The henry gas solubility optimization (HGSO) is a new nature-inspired algorithm that mimics Henry Gas Solubility to solve global optimization problems. The main changes of...     

Prediction of NSCLC recurrence from microarray data with GEP

Lung cancer is one of the deadliest diseases in the world. Non‐small cell lung cancer (NSCLC) is the most common and dangerous type of lung cancer. Despite the fact that NSCLC is preventable and curable for some cases if diagnosed at early stages, the vast majority of patients are diagnosed very late. Furthermore, NSCLC usually recurs sometime after treatment. Therefore, it is of paramount importance to predict NSCLC recurrence, so that specific and suitable treatments can be sought. Nonetheless, conventional methods of predicting cancer recurrence rely solely on histopathology data and predictions are not reliable in many cases. The microarray gene expression (GE) technology provides a promising and reliable way to predict NSCLC recurrence by analysing the GE of sample cells. This study proposes a new model from GE programming to use microarray datasets for NSCLC recurrence prediction. To this end, the authors also propose a hybrid method to rank and select relevant prognostic genes that are related to NSCLC recurrence prediction. The proposed model was evaluated on real NSCLC microarray datasets and compared with other representational models. The results demonstrated the effectiveness of the proposed model.Inspec keywords: lung, cancer, lab‐on‐a‐chip, genetics, patient diagnosisOther keywords: NSCLC recurrence prediction, microarray data, GE programming, nonsmall cell lung cancer, cancer recurrence, histopathology data, microarray gene expression, prognostic genes     

Epitaxial growth and thermal-conductivity limit of single-crystalline Bi2Se3/In2Se3 superlattices on mica

Nano Research - Thermal transport in superlattices is governed by various phonon-scattering processes. For extracting the phonon-scattering contribution of hetero-interfaces in chalcogenide...     

动态公路自动衡器动态示值误差测量的不确定度评定

结合工作实际,阐述了在长期检测工作中发现一些影响其计量准确性的因素,它对动态公路车辆自动衡器单轴称量示值误差不确定度的影响。     

基于数字兆欧表自动切换档位的研究与实现

随着电力、电气设备的日益复杂化,对于其安全性能也有了更高的要求,数字兆欧表作为测量绝缘阻值的重要工具,已在工业领域中广泛应用;为了满足测量精度以及兆欧表的可靠性,通过基于S3C2440的嵌入式平台,提出了一种基于数字兆欧表的自动切换档位的实现方案,以达到更加精确的测量;此方案通过判断采集到的电压值选择相应的采样电阻来实现,并结合硬件平台和软件设计,分别介绍了驱动程序和应用程序的编写与测试,并很好地应用到了实际电路中;经测试,能够达到预期的效果,测量精度高,测量误差控制在±2%。     

Neural Machine Translation by Fusing Key Information of Text

When the Transformer proposed by Google in 2017, it was first used for machine translation tasks and achieved the state of the art at that time. Although the current neural machine translation model can generate high quality translation results, there are still mistranslations and omissions in the translation of key information of long sentences. On the other hand, the most important part in traditional translation tasks is the translation of key information. In the translation results, as long as the key information is translated accurately and completely, even if other parts of the results are translated incorrect, the final translation results’ quality can still be guaranteed. In order to solve the problem of mistranslation and missed translation effectively, and improve the accuracy and completeness of long sentence translation in machine translation, this paper proposes a key information fused neural machine translation model based on Transformer. The model proposed in this paper extracts the keywords of the source language text separately as the input of the encoder. After the same encoding as the source language text, it is fused with the output of the source language text encoded by the encoder, then the key information is processed and input into the decoder. With incorporating keyword information from the source language sentence, the model’s performance in the task of translating long sentences is very reliable. In order to verify the effectiveness of the method of fusion of key information proposed in this paper, a series of experiments were carried out on the verification set. The experimental results show that the Bilingual Evaluation Understudy (BLEU) score of the model proposed in this paper on the Workshop on Machine Translation (WMT) 2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test dataset. The experimental results show the advantages of the model proposed in this paper.     

医院网络安全的技术实现与改进

随着信息科技的高速发展,网络在越来越多地为人们生活提供便利的同时,也为医院节省了大量的人力和物力,如何在现有的条件下,搞好局域网络的安全,是医院内网络管理人员的一个重要课题.本文从系统安全、网络运行安全和内部网络安全分析,并应用到实践中,以保证网络安全.     

SZ绞成缆设备电控系统的构成及变频器故障分析

SZ绞设备是将单元绞制和缆芯绞制在一道工序完成的一种专业电缆生产设备。SZ绞设备采用了先进的变频调速技术和PLC控制技术,有的还采用了伺服驱动,使其控制快速、精确和可靠。本文就SZ绞设备的电控系统构成及在调试运行中遇到的变频器故障进行了分析。