دانلود و نمایش مقالات مرتبط با SEM::صفحه 1
نتیجه جستجو - SEM

تعداد مقالات یافته شده: 783
ردیف عنوان نوع
1 Estimating monthly wet sulfur (S) deposition flux over China using an ensemble model of improved machine learning and geostatistical approach
برآورد شار رسوب ماهانه گوگرد مرطوب (S) بر روی چین با استفاده از مدل گروهی از یادگیری ماشین پیشرفته و روش زمین آماری-2019
The wet S deposition was treated as a key issue because it played the negative on the soil acidification, biodiversity loss, and global climate change. However, the limited ground-level monitoring sites make it difficult to fully clarify the spatiotemporal variations of wet S deposition over China. Therefore, an ensemble model of improved machine learning and geostatistical method named fruit fly optimization algorithm-random forestspatiotemporal Kriging (FOA-RF-STK) model was developed to estimate the nationwide S deposition based on the emission inventory, meteorological factors, and other geographical covariates. The ensemble model can capture the relationship between predictors and S deposition flux with the better performance (R2=0.68, root mean square error (RMSE)=7.51 kg ha−1 yr−1) compared with the original RF model (R2=0.52, RMSE=8.99 kg ha−1 yr−1). Based on the improved model, it predicted that the highest and lowest S deposition flux were mainly concentrated on the Southeast China (69.57 kg S ha−1 yr−1) and Inner Mongolia (42.37 kg S ha−1 yr−1), respectively. The estimated wet S deposition flux displayed the remarkably seasonal variation with the highest value in summer (22.22 kg S ha−1 sea−1), follwed by ones in autumn (18.30 kg S ha−1 sea−1), spring (16.27 kg S ha−1 sea−1), and the lowest one in winter (14.71 kg S ha−1 sea−1), which was closely associated with the rainfall amounts. The study provides a novel approach for the S deposition estimation at a national scale.
Keywords: Wet S deposition | Machine learning | Geostatistical approach | China
مقاله انگلیسی
2 2DToonShade: A stroke based toon shading system
2DToonShade: ضربه بر اساس سیستم سایه toon-2019
We present 2DToonShade: a semi-automatic method for creating shades and self-shadows in cel animation. Besides producing attractive images, shades and shadows provide important visual cues about depth, shapes, movement and lighting of the scene. In conventional cel animation, shades and shadows are drawn by hand. As opposed to previous approaches, this method does not rely on a complex 3D reconstruction of the scene: its key advantages are simplicity and ease of use. The tool was designed to stay as close as possible to the natural 2D creative environment and therefore provides an intuitive and user-friendly interface. Our system creates shading based on hand-drawn objects or characters, given very limited guidance from the user. The method employs simple yet very efficient algorithms to create shading directly out of drawn strokes. We evaluate our system through a subjective user study and provide qualitative comparison of our method versus existing professional tools and recent state of the art.
Keywords: Toon shading | Cel shading | Hand-drawn animation | Image-based rendering | Non-photorealistic-rendering
مقاله انگلیسی
3 Shape analysis of 3D nanoscale reconstructions of brain cell nuclear envelopes by implicit and explicit parametric representations
تجزیه و تحلیل 3D بازسازی شکل در مقیاس نانو سلول های مغز پاکت های هسته ای توسط نمایندگی پارامتری ضمنی و صریح-2019
Shape analysis of cell nuclei is becoming increasingly important in biology and medicine. Recent results have identified that large variability in shape and size of nuclei has an important impact on many biological processes. Current analysis techniques involve automatic methods for detection and segmentation of histology and microscopy images, but are mostly performed in 2D. Methods for 3D shape analysis, made possible by emerging acquisition methods capable to provide nanometric-scale 3D reconstructions, are still at an early stage, and often assume a simple spherical shape. We introduce here a framework for analyzing 3D nanoscale reconstructions of nuclei of brain cells (mostly neurons), obtained by semiautomatic segmentation of electron micrographs. Our method considers two parametric representations: the first one customizes the implicit hyperquadrics formulation and it is particularly suited for convex shapes, while the latter considers a spherical harmonics decomposition of the explicit radial representation. Point clouds of nuclear envelopes, extracted from image data, are fitted to the parameterized models which are then used for performing statistical analysis and shape comparisons. We report on the analysis of a collection of 121 nuclei of brain cells obtained from the somatosensory cortex of a juvenile rat.
Keywords: Shape analysis | Nanoscale cell reconstruction | Nuclear envelopes | Cell classification
مقاله انگلیسی
4 Estimation of the degree of hydration of concrete through automated machine learning based microstructure analysis – A study on effect of image magnification
Estimation of the degree of hydration of concrete through automated machine learning based microstructure analysis – A study on effect of image magnification-2019
The scanning electron microscopy (SEM) images are commonly used to understand the microstructure of the concrete. With the advancements in the field of computer vision, many researchers have adopted the image processing technique for the microstructure analysis. Most of the previous methods are not adaptable, nonreproducible, semi-automated, and most importantly all these methods are highly influenced by image magnification. Therefore, to overcome these challenges, this paper presents a machine learning based image segmentation method for microstructure analysis and degree of hydration measurement using SEM images. In addition, the authors looked into the impact of magnification of SEM images on the model accuracy and classifier training for the degree of hydration measurement considering two scenarios. First, the image segmentation was performed using a classifier of specific magnification, and then a common classifier is trained using the image of different magnification. The results show that the Random Forest classifier algorithm is suitable for microstructure analysis using SEM images. Through the statistical analysis, it has been proved that there is no significant effect of magnification on model training and accuracy for the degree of hydration measurement. So, a single classifier can be used to process the images of different magnification of a specimen which reduces the effort of training and computational time. The proposed method can generate highly accurate and reliable results in a shorter time and lower cost. Moreover, the findings in this research can be useful for researchers to determine the optimum magnification required for the microstructure analysis.
Keywords: Concrete microstructure analysis | Degree of hydration | Machine learning | Image segmentation
مقاله انگلیسی
5 تعداد مورنیاز برای توئیت کردن: شبکه های اجتماعی و تاثیر آن روی علم جراحی
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 4 - تعداد صفحات فایل doc فارسی: 12
پزشکان جراح با استفاده از فیسبوک، توئیتر، لینکدین و اینستاگرام هم برای اهداف فردی و هم اهداف حرفه ای وارد شبکه های اجتماعی می شوند. در یک عصر دسترسی جهانی به هرچیز، خطر سرریز شدن اطلاعات وجود دارد و بنابراین نیاز به مقابله و همیاری داریم. هشتگ ها به صورت ویروسی تاثیر عظیم اجتماعی داشته اند مثلا" هشتگ #ILookLikeASurgeon. SoMe تبدیل به یک ابزاری برای برقراری ارتباط، به اشتراک گذاری و راهنمایی و آموزش شده است. این یک ابزاری برای آموزش نسل بعدی جراحان می باشد. برای محققان و مجلات، این سوال باقی مانده است که آیا ورودی مورد نیاز برای وارد شدن به بستر SoMe با یک بهره مشابه در خروجی، مثل شهرت و درمعرض دید قرار گرفتن جبران می شود یا خیر. اطلاعات خلاصه شده در چکیده های بصری می تواند به انتشار نتایج مطالعه برای طیف گسترده ای از مخاطبان کمک کند اما تاثیر یک هشتگ #visualabstract می تواند خاص و تخصصی باشد. درحال حاضر، اطلاعات و دانش اندکی درباره "تعداد موردنیاز برای توئیت کردن" به منظور اثرگذاشتن روی مواردی مثل دانلودها، ارجاع دهی ها و نهایتا" ضریب تاثیر وجود دارد.
مقاله ترجمه شده
6 The sensitive prosecutor: Emotional experiences of prosecutors in managing criminal proceedings
دادستان حساس: تجربیات عاطفی دادستان در مدیریت دادرسی کیفری-2019
For over three decades, therapeutic jurisprudence (TJ) has produced rich scholarship highlighting the inseparable connection between law and personal wellbeing. Only recently, however, have TJ scholars begun to explore the influence that the lawhas on those practicing it. The current research aimsto contribute to this developing area of study. It explores the “emotional map” of public prosecutors in relation to defendants and crime victims, their awareness to these emotions and the impact that these emotions have on their professional decisions. The research involves in-depth interviews with 14 public prosecutors handling criminal cases in Israeli courts. The qualitative, phenomenological analysis of the documented interviews revealed three exposure levels in which interviewees discussed the emotional aspects of their work. The tension between resisting emotions and accepting them was lurking upon each one of the subjects. Their descriptions of specific raw emotions emerged at the deepest level of exposure, and at that level, anger was the most prominent emotion. Our findings raise some skepticism regarding the prosecutor image as a completely rational and provide the insight that prosecutors emotional world is boiling underneath the surface. Moreover, the exposure of the continuous tension between acceptance and rejection of emotions provides an explanation for the prosecutors difficulty in acknowledging their emotions in full. This tension negatively impacts the prosecutors personal and professional lives inways that resemble psychological symptoms of secondary trauma. The findings may contribute to the development of a “knowledge base” of emotional experiences of prosecutors that could enable the creation of models for regulating and managing emotions of legal agents, for the benefit of litigants, legal agents, and the legal process more broadly
Keywords: Therapeutic jurisprudence | Emotions | Prosecutors | Qualitative research | Anger | Secondary trauma
مقاله انگلیسی
7 مروری بر تجمیع دستگاه های مدل سازی اطلاعات ساختمانی (BIM) و اینترنت اشیاء (IoT): وضعیت کنونی و روند آینده
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 13 - تعداد صفحات فایل doc فارسی: 56
تجمیع مدل سازی اطلاعات ساختمانی (BIM) با داده های زمان واقعی(بلادرنگ) دستگاه های اینترنت اشیاء (IoT)، نمونه قوی را برای بهبود ساخت وساز و بهره وری عملیاتی ارائه می دهد. اتصال جریان-های داده های زمان واقعی که بر گرفته از مجموعه هایی از شبکه های حسگرِ اینترنت اشیاء (که این جریان های داده ای، به سرعت در حال گسترش هستند) می باشند، با مدل های باکیفیت BIM، در کاربردهای متعددی قابل استفاده می باشد. با این حال، پژوهش در زمینه ی تجمیع BIM و IOT هنوز در مراحل اولیه ی خود قرار دارد و نیاز است تا وضعیت فعلی تجمیع دستگاه های BIM و IoT درک شود. این مقاله با هدف شناسایی زمینه های کاربردی نوظهور و شناسایی الگوهای طراحی رایج در رویکردی که مخالف با تجمیع دستگاه BIM-IoT می باشد، مرور جامعی در این زمینه انجام می دهد و به بررسی محدودیت های حاضر و پیش بینی مسیرهای تحقیقاتی آینده می پردازد. در این مقاله، در مجموع، 97 مقاله از 14 مجله مربوط به AEC و پایگاه داده های موجود در صنایع دیگر (در دهه گذشته)، مورد بررسی قرار گرفتند. چندین حوزه ی رایج در این زمینه تحت عناوین عملیات ساخت-وساز و نظارت، مدیریت ایمنی و بهداشت، لجستیک و مدیریت ساختمان، و مدیریت تسهیلات شناسایی شدند. نویسندگان، 5 روش تجمیع را همراه با ذکر توضیحات، نمونه ها و بحث های مربوط به آنها به طور خلاصه بیان کرده اند. این روش های تجمیع از ابزارهایی همچون واسط های برنامه نویسی BIM، پایگاه داده های رابطه ای، تبدیل داده های BIM به پایگاه داده های رابطه ای با استفاده از طرح داده های جدید، ایجاد زبان پرس وجوی جدید، فناوری های وب معنایی و رویکردهای ترکیبی، استفاده می کنند. براساس محدودیت های مشاهده شده، با تمرکز بر الگوهای معماری سرویس گرا (SOA) و راهبردهای مبتنی بر وب برای ادغام BIM و IoT، ایجاد استانداردهایی برای تجمیع و مدیریت اطلاعات، حل مسئله همکاری و محاسبات ابری، مسیرهای برجسته ای برای تحقیقات آینده پیشنهاد شده است.
کلمه های کلیدی: مدل سازی اطلاعات ساختمانی (BIM) | دستگاه اینترنت اشیاء (IoT) | حسگرها | ساختمان هوشمند | شهر هوشمند | محیط ساخته شده هوشمند | تجمیع.
مقاله ترجمه شده
8 MalDy: Portable, data-driven malware detection using natural language processing and machine learning techniques on behavioral analysis reports
MalDy: تشخیص بدافزارهای قابل حمل ، داده محور با استفاده از تکنیک های پردازش زبان طبیعی و یادگیری ماشین در گزارش های تحلیل رفتاری-2019
In response to the volume and sophistication of malicious software or malware, security investigators rely on dynamic analysis for malware detection to thwart obfuscation and packing issues. Dynamic analysis is the process of executing binary samples to produce reports that summarise their runtime behaviors. The investigator uses these reports to detect malware and attribute threat types leveraging manually chosen features. However, the diversity of malware and the execution environments make manual approaches not scalable because the investigator needs to manually engineer fingerprinting features for new environments. In this paper, we propose, MalDy (mal die), a portable (plug and play) malware detection and family threat attribution framework using supervised machine learning techniques. The key idea of MalDy portability is the modeling of the behavioral reports into a sequence of words, along with advanced natural language processing (NLP) and machine learning (ML) techniques for automatic engineering of relevant security features to detect and attribute malware without the investigator intervention. More precisely, we propose to use bag-of-words (BoW) NLP model to formulate the behavioral reports. Afterward, we build ML ensembles on top of BoW features. We extensively evaluate MalDy on various datasets from different platforms (Android and Win32) and execution environments. The evaluation shows the effectiveness and the portability of MalDy across the spectrum of the analyses and settings.
Keywords: Malware | Android | Win32 | Behavioral analysis | Machine learning | NLP
مقاله انگلیسی
9 Extender osmolality, glycerol and egg yolk on the cryopreservation of epididymal spermatozoa for gamete banking of the Cantabric Chamois (Rupicapra pyrenaica parva)
Extender osmolality, glycerol and egg yolk on the cryopreservation of epididymal spermatozoa for gamete banking of the Cantabric Chamois (Rupicapra pyrenaica parva)-2019
Germplasm banking is a key technology enabling the ex-situ conservation of wild species. However, cryopreservation protocols must be tested to assure the applicability of the banked material. The objective of this study was defining a range of parameters for the composition of a semen extender for Cantabrian chamois epididymal spermatozoa (post-mortem collection). The freezing extender was based in a TES-Tris-fructose buffer, modifying its composition in three experiments: Osmolality of the buffer (320, 380 or 430 mOsm/kg, 8% glycerol, 15% egg yolk), glycerol (4 or 8%, 430 mOsm/kg, 15% egg yolk), egg yolk (10 or 15%, 430 mOsm/kg, 4% glycerol). Sperm was extended at 100 mill. spermatozoa/ml, cooled at 5 ○ C and frozen at —20 ○ C/min. Sperm quality was assessed pre and post-thawing (CASA, HOS test, abnormal forms, cytoplasmic droplets, and viability and acrosomal damage by flow cytometry). Freez- ability was good overall, with total motility of 65.5% ± 2.4 initial and 55.8% ± 2.4 post-thawing. The ex- tenders affected the post-thaw sperm quality marginally. Whereas osmolalities and glycerol concentrations seemed not to differ, 430 mOsm/kg and 4% glycerol might be preferred. Egg yolk con- centrations only differed on sperm velocity (VCL: 84.0 ± 6.7 mm/s in 10% vs. 70.7 ± 6.2 mm/s in 15%, P < 0.05). Our results suggest a good cryotolerance of chamois epididymal spermatozoa, with a preferred extender composition of hyperosmotic buffer, glycerol in the 4% range and lower egg yolk (10% range) than other ruminants.© 2018 Elsevier Inc. All rights reserved.
Keywords : Cantabrian chamois | Cryopreservation | Epididymal spermatozoa | Extender | Osmolality | Glycerol
مقاله انگلیسی
10 Global discovery of stable and non-toxic hybrid organic-inorganic perovskites for photovoltaic systems by combining machine learning method with first principle calculations
کشف جهانی پروسکوتیتهای آلی غیر آلی ترکیبی پایدار و غیر سمی برای سیستم های فتوولتائیک با ترکیب روش یادگیری ماشین با محاسبات اصلی-2019
Traditional trial-and-error methods seriously restrict and hinder the searching of high-performance functional materials, especially when the search space is large. Rapid searching for advanced functional materials has always been a hot research topic, and attracted a lot of experimental and theoretical research attention. Here, by combining machine learning method with density functional theory (DFT) calculations, a target-driven method is proposed here to speed up the discovery of hidden hybrid organic-inorganic perovskites (HOIPs) for photovoltaic applications from 230808 HOIPs candidates which is almost two orders larger than previous studied. After imposing two criterions, i.e., charge neutrality condition and stability condition, on potential HOIPs candidates, followed by a machine learning (ML) screening, 686 orthorhombic-like HOIPs with proper bandgap are selected. In machine learning screening, ensemble learning using three ML models, including gradient boosting regression (GBR), supporting vector regression (SVR) and kernel ridge regression (KRR), are applied to predict the bandgap of 38086 HOIPs candidates. 132 stable and non-toxic (Cd-, Pb- and Hg-free) orthorhombiclike HOIPs are finally verified by DFT calculations with appropriate band gap for solar cells. In the present study, not only a series of unexplored stable and non-toxic HOIPs are discovered for further experimental synthesis, a new HOIPs database is constructed as well, thus beneficial to future functional material design.
Keywords: Machine learning | Hybrid organic-inorganic perovskites | First principle calculations | Photovoltaics
مقاله انگلیسی
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