IOP SciNotes最新文献_第10页

Nematic liquid crystal/dimethylsulphoxide mixture based tuning condenser 向列液晶/二甲基亚砜混合物调谐电容器

IOP SciNotes Pub Date : 2020-06-04 DOI: 10.1088/2633-1357/ab8bc2

A. Parveen, Depanshu Varshney, J. Prakash

引用次数: 2

Predicting radiation treatment planning evaluation parameter using artificial intelligence and machine learning 利用人工智能和机器学习预测放射治疗计划评估参数

IOP SciNotes Pub Date : 2020-06-01 DOI: 10.1088/2633-1357/ab805d

Frederick F. C. Ng, R. Jiang, J. Chow

{"title":"Predicting radiation treatment planning evaluation parameter using artificial intelligence and machine learning","authors":"Frederick F. C. Ng, R. Jiang, J. Chow","doi":"10.1088/2633-1357/ab805d","DOIUrl":"https://doi.org/10.1088/2633-1357/ab805d","url":null,"abstract":"Purpose: This study suggested a new method predicting the dose-volume parameter for radiation treatment planning evaluation using machine learning, and to evaluate the performance of different learning algorithms in the parameter prediction. Methods: Dose distribution index (DDI) for fifty prostate volumetric modulated arc therapy plans were calculated, and compared to results predicted by machine learning using algorithms, namely, linear regression, tree regression, support vector machine (SVM) and Gaussian process regression (GPR). Root mean square error (RMSE), prediction speed and training time were determined to evaluate the performance of each algorithm. Results: From the results, it is found that the square exponential GPR algorithm had the smallest RMSE, relatively high prediction speed and short training time of 0.0038, 4,100 observation/s and 0.18 s, respectively. All linear regression, SVM and GPR algorithms performed well according to their RMSE in the range of 0.0038–0.0193. However, RMSE of the medium and coarse tree regression algorithms were found larger than 0.03, showing that they are not suitable for predicting DDI in this study. Conclusion: Machine learning can be used to predict dose-volume parameter such as DDI in radiation treatment planning QA. Selection of a suitable machine learning algorithm is important to determine the parameter effectively.","PeriodicalId":93771,"journal":{"name":"IOP SciNotes","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47155275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

Numerical simulation on the effect of sample geometry size on the metal flow behaviour during the forging process 锻造过程中试样几何尺寸对金属流动特性影响的数值模拟

IOP SciNotes Pub Date : 2020-05-29 DOI: 10.1088/2633-1357/ab828c

J. Obiko, F. Mwema

引用次数: 3

Welcome to IOP SciNotes—a new open access journal for short research outputs across the physical and environmental sciences 欢迎来到IOP sciinotes，这是一份新的开放获取期刊，提供物理和环境科学领域的短期研究成果

IOP SciNotes Pub Date : 2020-05-29 DOI: 10.1088/2633-1357/ab9221

Lauren Carter

引用次数: 0

Molecular spectroscopy and adverse optical properties of N-(p-hexyloxy-benzylidene)–p-toluidine (HBT) liquid crystal molecule studied by DFT methodology DFT方法研究N-（对己基氧基-亚苄基）-对甲苯苯胺（HBT）液晶分子的分子光谱和不利光学性质

IOP SciNotes Pub Date : 2020-05-29 DOI: 10.1088/2633-1357/ab7f83

Narinder Kumar, Pawan Singh, K. Thapa, Devesh Kumar

引用次数: 11

An Internet of Things app for monitor unit calculation in superficial and orthovoltage skin therapy 物联网应用程序，用于表面和正电压皮肤治疗中的监测单元计算

IOP SciNotes Pub Date : 2020-05-29 DOI: 10.1088/2633-1357/ab8be0

J. Pearse, J. Chow

{"title":"An Internet of Things app for monitor unit calculation in superficial and orthovoltage skin therapy","authors":"J. Pearse, J. Chow","doi":"10.1088/2633-1357/ab8be0","DOIUrl":"https://doi.org/10.1088/2633-1357/ab8be0","url":null,"abstract":"Purpose: We developed an app for Internet of Things (IoT) device such as smartphone or tablet to calculate the monitor unit in superficial and orthovoltage skin therapy. The app can run both on the Windows and Android operation system. Methods: The IoT app was created based on the formula to calculate the monitor unit in skin therapy using kV photon beams. The calculation was based on databases of dose variables such as relative exposure factor and backscatter factor. The calculation also considered the stand-off and stand-in correction according to the inverse-square and inverse-cube law. Verification of the app was carried out by comparing the monitor unit results with those from hand calculations. Results: The frontend window of the app provided a user-friendly interface to the user for inputting prescription dose, beam and treatment setup variables. The user could save the calculation record electronically, generate a printout or send it to other radiation staff using the IoT. Verification of the app showing that deviation between the monitor units calculated by the app and by hand is insignificant. Conclusion: The verified IoT app can effectively calculate the monitor unit in superficial and orthovoltage skin therapy. The app takes advantages of all innate features of IoT such as real time communication, Internet access, data transfer and sharing.","PeriodicalId":93771,"journal":{"name":"IOP SciNotes","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1088/2633-1357/ab8be0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44324928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Graphite fabric reinforced PAEK composites by novel impregnation-co-film technique 新型浸渍共膜技术在石墨织物增强PAEK复合材料中的应用

IOP SciNotes Pub Date : 2020-05-26 DOI: 10.1088/2633-1357/ab8d1a

U. Marathe, Meghashree Padhan, J. Bijwe

{"title":"Graphite fabric reinforced PAEK composites by novel impregnation-co-film technique","authors":"U. Marathe, Meghashree Padhan, J. Bijwe","doi":"10.1088/2633-1357/ab8d1a","DOIUrl":"https://doi.org/10.1088/2633-1357/ab8d1a","url":null,"abstract":"High performance polymers such as Poly(ether ether ketone) (PEEK), Poly(aryl ether ketone) (PAEK), Poly(ether ketone ketone) (PEKK), etc exhibit high melting points, 343 °C, 373 °C and 338 °C respectively and very high melt viscosity. They do not have appropriate solvents, which could open the ways to process their fabric reinforced composites; by impregnation technique followed by compression molding. Film stacking and powder sprinkling are the only possible techniques, which lead to weak crossover points due to non-wetting by the resin and finally resulting in inferior mechanical and interfacial properties and high amount of voids. In order to solve these problems, a novel technique called impregnation-co-film (ICF) is explored in this work. Polyetherimide (PEI) (10 wt% in dichloromethane solution) pre-impregnated graphite fabric prepregs were used before applying film technique followed by compression molding. The study compares properties such as density, void content, flexural strength, impact strength, Interlaminar shear strength (ILSS), thermal stability (in air) and interface by field emission scanning electron microscopy for the composite prepared with film stacking technique and that by impregnation-co-film stacking technique. It was confirmed that the new ICF technique proved to be significantly promising than the older technique almost in all aspects with 120% improvement in ILSS, 127% in flexural strength and 200% decrease in void content.","PeriodicalId":93771,"journal":{"name":"IOP SciNotes","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47792008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

S-band electron spin resonance spectroscopy using a short-circuited coplanar waveguide resonator 利用短路共面波导谐振器的s波段电子自旋共振光谱

IOP SciNotes Pub Date : 2020-04-17 DOI: 10.1088/2633-1357/abc870

Subhadip Roy, A. Nandi, P. Das, C. Mitra

{"title":"S-band electron spin resonance spectroscopy using a short-circuited coplanar waveguide resonator","authors":"Subhadip Roy, A. Nandi, P. Das, C. Mitra","doi":"10.1088/2633-1357/abc870","DOIUrl":"https://doi.org/10.1088/2633-1357/abc870","url":null,"abstract":"In this work, we study the development of a coplanar waveguide (CPW) resonator and its use in an electron spin resonance (ESR) spectrometer. The CPW resonator is designed to operate in S-band (2-4 GHz), with a short circuit configuration leading to miniaturization. It is so constructed such that it has a characteristic impedance of 50 ohms. The resonator supports quasi-TEM mode of propagation owing to its uniplanar nature, demanding detailed electromagnetic simulation. The design parameters and the electromagnetic field distribution are obtained from the simulation. The resonator is fabricated using optical lithography with a rapid prototyping technique. The characteristic response of the resonator is measured by coupling it to a Vector Network Analyzer (VNA). The ESR absorption spectrum of free radical 2,2-diphenyl-1-picrylhydrazyl (DPPH) is captured by using this resonator in reflection geometry. The microwave magnetic field distribution at the sample position is investigated. The ascertained value of Lande g-factor is consistent with that reported in the literature. Spin sensitivity of the order of 1015 spins/gauss is achieved upon using this resonator at room temperature. The quality factor of this resonator is found to be low and this makes it suitable for use in a Pulsed ESR spectrometer.","PeriodicalId":93771,"journal":{"name":"IOP SciNotes","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41578206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

MNIST-MIX: a multi-language handwritten digit recognition dataset mist - mix:多语言手写数字识别数据集

IOP SciNotes Pub Date : 2020-04-08 DOI: 10.1088/2633-1357/abad0e

Weiwei Jiang

引用次数: 11

Multi-Class classification of vulnerabilities in smart contracts using AWD-LSTM, with pre-trained encoder inspired from natural language processing 使用AWD-LSTM对智能合约中的漏洞进行多类分类，预训练编码器的灵感来自自然语言处理

IOP SciNotes Pub Date : 2020-03-21 DOI: 10.1088/2633-1357/abcd29

Ajay K. Gogineni, S. Swayamjyoti, Devadatta Sahoo, K. Sahu, R. Kishore

{"title":"Multi-Class classification of vulnerabilities in smart contracts using AWD-LSTM, with pre-trained encoder inspired from natural language processing","authors":"Ajay K. Gogineni, S. Swayamjyoti, Devadatta Sahoo, K. Sahu, R. Kishore","doi":"10.1088/2633-1357/abcd29","DOIUrl":"https://doi.org/10.1088/2633-1357/abcd29","url":null,"abstract":"Vulnerability detection and safety of smart contracts are of paramount importance because of their immutable nature. Symbolic tools like OYENTE and MAIAN are typically used for vulnerability prediction in smart contracts. As these tools are computationally expensive, they are typically used to detect vulnerabilities until some predefined invocation depth. These tools require more search time as the invocation depth increases. Since the use of smart contracts increases rapidly, their analysis becomes difficult using these traditional tools. Recently, a machine learning technique called Long Short Term Memory (LSTM) has been used to predict the vulnerability of a smart contract. In the present article, we present how to classify smart contracts into Suicidal, Prodigal, Greedy, or Normal categories using Average Stochastic Gradient Descent Weight-Dropped LSTM (AWD-LSTM), a variant of LSTM. We reduced the class imbalance by considering only distinct opcode combinations for normal contracts and achieved a weighted average F1 score of 90.0%. Such techniques can be utilized in real-time to analyze a large number of smart contracts and to improve their security.","PeriodicalId":93771,"journal":{"name":"IOP SciNotes","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44773316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15