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نتیجه جستجو - اشعه ایکس

تعداد مقالات یافته شده: 25
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1 بهبود تولید بیودیزل با کمک اولتراسونیک حاصل از ضایعات صنعت گوشت (چربی خوک) با استفاده از نانوکاتالیزور اکسید مس سبز: مقایسه سطح پاسخ و مدل سازی شبکه عصبی
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 11 - تعداد صفحات فایل doc فارسی: 25
سوخت زیستی سبز ، تمیز و پایدار تنها گزینه به منظور کاهش کابرد سوخت های فسیلی ، پاسخگویی به تقاضای زیاد انرژی و کاهش آلودگی هوا است. تولید بیودیزل زمانی ارزان می شود که از یک پیش ماده ارزان ، کاتالیزور سازگار با محیط زیست و فرآیند مناسب استفاده کنیم. پیه خوک از صنعت گوشت حاوی اسید چرب بالا است و به عنوان یک پیش ماده موثر برای تهیه بیودیزل کاربرد دارد. این مطالعه بیودیزل را از روغن پیه خوک از طریق فرآیند استری سازی دو مرحله ای با کمک اولتراسونیک و کاتالیزور تولید می کند. عصاره Cinnamomum tamala (C. tamala) برای تهیه نانوذرات CuO مورد استفاده قرار گرفت و با استفاده از طیف مادون قرمز ، پراش اشعه ایکس ، توزیع اندازه ذرات ، میکروسکوپ الکترونی روبشی و انتقال مشخص شد. تولید بیودیزل با استفاده از طرح Box-Behnken (BBD) و شبکه عصبی مصنوعی (ANN) ، در محدوده متغیرهای زمان اولتراسونیک (us )(20-40 min)، بارگیری نانوکاتالیزور 1-3) CuO درصد وزنی( ، و متانول به قبل از نسبت مولی PTO (10:1e30:1) مدلسازی شد. آنالیز آماری ثابت کرد که مدل سازی شبکه عصبی بهتر از BBD است. عملکرد بهینه 97.82٪ با استفاده از الگوریتم ژنتیک (GA) در زمان US: 35.36 دقیقه ، بار کاتالیزور CuO: 2.07 درصد وزنی و نسبت مولی: 29.87: 1 به دست آمد. مقایسه با مطالعات قبلی ثابت کرد که اولتراسونیک به میزان قابل توجهی موجب کاهش بار نانوکاتالیزور CuO می شود ، و نسبت مولی را افزایش می دهد و این فرایند را بهبود می بخشد.
کلمات کلیدی: چربی خوک | التراسونیک | اکسید مس | سنتز سبز | شبکه عصبی | سطح پاسخ
مقاله ترجمه شده
2 Identification and differentiation of commercial and military explosives via high performance liquid chromatography – high resolution mass spectrometry (HPLC-HRMS), X-ray diffractometry (XRD) and X-ray fluorescence spectroscopy (XRF): Towards a forensic substance database on explosives
شناسایی و تمایز مواد منفجره تجاری و نظامی از طریق کروماتوگرافی مایع با کارایی بالا - طیف سنجی جرمی با وضوح بالا (HPLC-HRMS) ، پراش سنجی اشعه ایکس (XRD) و طیف سنجی فلورسانس اشعه ایکس (XRF): به سمت پایگاه داده مواد پزشکی قانونی در مورد مواد منفجره-2020
The identification of confiscated commercial and military explosives is a crucial step not only in the uncovering of distribution pathways, but it also aids investigating officers in criminal casework. Even though commercial and military explosives mainly rely on a small number of high-energy compounds, a great variety of additives and synthesis by-products can be found that can differ depending on the brand, manufacturer and application. This makes the identification of commercial and military explosives based on their overall composition a promising approach that can be used to establish a pan-European Forensic Substance Database on Explosives. In this work, three analytical techniques were employed to analyze 36 samples of commercial and military explosives from Germany and Switzerland. An HPLC-HRMS method was developed, using 27 analytes of interest that encompass high-energy compounds, synthesis by-products and additives. HPLCHRMS and XRD were used to gather and confirm molecular information on each sample and XRF analyses were carried out to gain insight on the elemental composition. Combining the results from all three techniques, 41 different additives could be identified as being diagnostic analytes and all samples showed a unique analytical fingerprint, which allows for a differentiation of the samples. Therefore, this work presents a set of methods that can be used as a foundation for the creation and population of a database on explosives that enables the assigning of specific formulations to certain brands, manufacturers and countries of origin.
Keywords: HPLC-HRMS | Powder XRD | XRF | Explosives | Commercial explosives | Military explosives
مقاله انگلیسی
3 Validation of a fully automated liver segmentation algorithm using multi-scale deep reinforcement learning and comparison versus manual segmentation
اعتبار سنجی الگوریتم تقسیم کبدی کاملاً خودکار با استفاده از یادگیری تقویتی عمیق چند مقیاس و مقایسه در مقابل تقسیم بندی دستی-2020
Purpose: To evaluate the performance of an artificial intelligence (AI) based software solution tested on liver volumetric analyses and to compare the results to the manual contour segmentation. Materials and methods: We retrospectively obtained 462 multiphasic CT datasets with six series for each patient: three different contrast phases and two slice thickness reconstructions (1.5/5 mm), totaling 2772 series. AI-based liver volumes were determined using multi-scale deep-reinforcement learning for 3D body markers detection and 3D structure segmentation. The algorithm was trained for liver volumetry on approximately 5000 datasets. We computed the absolute error of each automatically- and manually-derived volume relative to the mean manual volume. The mean processing time/dataset and method was recorded. Variations of liver volumes were compared using univariate generalized linear model analyses. A subgroup of 60 datasets was manually segmented by three radiologists, with a further subgroup of 20 segmented three times by each, to compare the automatically-derived results with the ground-truth. Results: The mean absolute error of the automatically-derived measurement was 44.3 mL (representing 2.37 % of the averaged liver volumes). The liver volume was neither dependent on the contrast phase (p=0.697), nor on the slice thickness (p=0.446). The mean processing time/dataset with the algorithm was 9.94 s (sec) compared to manual segmentation with 219.34 s. We found an excellent agreement between both approaches with an ICC value of 0.996. Conclusion: The results of our study demonstrate that AI-powered fully automated liver volumetric analyses can be done with excellent accuracy, reproducibility, robustness, speed and agreement with the manual segmentation.
Keywords: Artificial intelligence | Algorithms | Reproducibility of results | Tomography | X-ray computed | Liver
مقاله انگلیسی
4 The Forensic Discrimination of Quartz Sands from the Swan Coastal Plain, Western Australia
تبعیض قانونی ماسه های کوارتز از دشت ساحلی قو ، استرالیای غربی-2020
Soil is a valuable resource in many criminal cases. Used to link crime scenes, suspects, vehicles and tools, it has undergone a resurgence in recent years with multiple groups focusing on methods to allow for better differentiation. Many times the methods are focused on soil fractions, such as the clay or organic fractions. However, very sandy soils, such as the quartz sands of the Swan Coastal Plain, present a challenge to these methods. Clays and organic materials are very limited in these soils and hence, another characteristic needs to be added to the examination process. The authors here present a potential technique useful for the differentiation of soils where the soil texture is dominated by quartz sand. The quartz sand grains are used as a sampling medium, where the fine coatings (particles <20 μm in size) are recovered and analysed using X-ray diffraction (XRD). The peak intensities for a total of 7 commonly occurring minerals, converted to relative percentages, were used to compare 52 sub-surface soils from known locations, and a further 339 soils from various stratigraphies, based on an algorithm for examining similarity. Results indicate this technique allows a degree of discrimination sufficient for inclusion in a standard soil comparison protocol, specifically for cases where the traditional fractionated analyses are not appropriate.
Keywords : (max 6) soil analysis | X-ray diffraction | grain coating
مقاله انگلیسی
5 Effectiveness assessing of softwares with AI for chest area x-ray images post-processing
ارزیابی اثربخشی نرم افزارها با هوش مصنوعی برای تصاویر اشعه ایکس ناحیه قفسه سینه پس از پردازش-2020
Diagnostic radiology is a branch of medicine describes of ionizing radiation using to study the structure and functions of normal and pathological altered human organs and systems for the prevention and detection of diseases. X-ray irradiation is the most common diagnostic method in Radiology and it makes possible to identify and diagnose diseases and injuries for further treatment of patients. In particular, development of artificial intelligence (AI) has led the creation of different software for X-ray images processing to improve pathologists recognition by operator. At this moment there are lots of means and methods have been developed by different software engineers to find out "the perfect solution" for this problem. Respiratory diseases occupy one of the leading places in Ukraine. The main share of pathologies is acute viral infections, bronchitis, pneumonia and tuberculosis. In one year, about 15.5 million chest X-ray studies perform in Ukraine only for tuberculosis detection. It was the reason to select chest X-ray studies for processing’s effectiveness assessing. For effectiveness assessing we have made quantitative contrast measurements for selected areas on original and processed chest X-ray images.
Keywords: X-ray | tomosynthesis | digital radiology | contrast deviations | AI
مقاله انگلیسی
6 Data-mining the diaryl(thio)urea conformational landscape: Understanding the contrasting behavior of ureas and thioureas with quantum chemistry
داده کاوی چشم انداز تطابقی ادرار (تیو) اوره: درک رفتار متضاد اوره و تیوره با شیمی کوانتومی-2019
The conformations adopted by urea and thiourea functional groups influence catalysis and binding. We combine data-mining with quantum chemical calculations to understand the differences in conformational behavior for these two important structural motifs. We developed a Python tool to automate the compilation of X-ray structural information and perform conformational clustering and visualization, based on SMILES input. While diarylureas have an overwhelming preference for the anti,anti-conformer, diarylthioureas adopt a mixture of anti,anti- and anti,syn-conformers. Computations show the anti,antithiourea conformer is destabilized by out-of-plane rotations which avoid a steric clash with the sulfur atom. These conformational preferences were studied computationally under a variety of conditions, and apart from in the gas-phase, a preference for anti,anti-ureas was found. Consistent with experiments, this preference increases in more polar environments. Quantitative predicted ratios are sensitive to the computational treatment of solvation effects, with COSMO-RS giving more realistic amounts of the anti,anti-conformer in THF and DMSO.
Keywords: Conformational analysis | Density functional theory | Urea | Thiourea | Organocatalyst | X-ray structure | Solvation | Data-mining
مقاله انگلیسی
7 Breast mass detection from the digitized X-ray mammograms based on the combination of deep active learning and self-paced learning
تشخیص توده سینه از ماموگرافی های دیجیتالی شده اشعه ایکس بر اساس ترکیبی از یادگیری عمیق فعال و یادگیری خود گام-2019
Breast mass detection is a challenging task in mammogram, since mass is usually embedded and surrounded by various normal tissues with similar density. Recently, deep learning has achieved impressive performance on this task. However, most deep learning methods require large amounts of well-annotated datasets. Generally, the training datasets is generated through manual annotation by experienced radiologists. However, manual annotation is very time-consuming, tedious and subjective. In this paper, for the purpose of minimizing the annotation efforts, we propose a novel learning framework for mass detection that incorporates deep active learning (DAL) and self-paced learning (SPL) paradigm. The DAL can significantly reduce the annotation efforts by radiologists, while improves the efficiency of model training by obtaining better performance with fewer overall annotated samples. The SPL is able to alleviate the data ambiguity and yield a robust model with generalization capability in various scenarios. In detail, we first employ a few of annotated easy samples to initialize the deep learning model using Focal Loss. In order to find out the most informative samples, we propose an informativeness query algorithm to rank the large amounts of unannotated samples. Next, we propose a self-paced sampling algorithm to select a number of the most informative samples. Finally, the selected most informative samples are manually annotated by experienced radiologists, which are added into the annotated samples for the model updating. This process is looped until there are not enough most informative samples in the unannotated samples. We evaluate the proposed learning framework on 2223 digitized mammograms, which are accompanied with diagnostic reports containing weakly supervised information. The experimental results suggest that our proposed learning framework achieves superior performance over the counterparts. Moreover, our proposed learning framework dramatically reduces the requirement of the annotated samples, i.e., about 20% of all training data.
Keywords: Breast cancer | Mammography | Mass detection | Deep active learning | Self-paced learning
مقاله انگلیسی
8 Machine learning application to automatically classify heavy minerals in river sand by using SEM/EDS data
کاربرد یادگیری ماشین برای طبقه بندی خودکار مواد معدنی سنگین در ماسه رودخانه با استفاده از داده های SEM / EDS-2019
Heavy minerals are generally trace components of sand or sandstone. Fast and accurate heavy mineral classification has become a necessity. Energy Dispersive X-ray Spectrometers (EDS) integrated with Scanning Electron Microscopy (SEM) were used to obtain rapid heavy mineral elemental compositions. However, mineral identification is challenging since there are wide ranges of spectral datasets for natural minerals. This study aimed to find a reliable, machine learning classifier for identifying various heavy minerals based on EDS data. After selecting 22 distinct heavy minerals from modern river sands, we obtained their elemental data by SEM/EDS. The elemental data from a total of 3067 mineral grains were collected under various instrumental conditions. We compared the classification performance of four classifiers (Decision Tree, Random Forest, Support Vector Machine, Bayesian Network). Our results indicated that machine learning methods, especially Random Forest, can be used as the most effective classifier for heavy mineral classification.
Keywords: Heavy mineral | Machine learning | Energy dispersive X-ray spectrometers | Sand | Classification | Sedimentology | Geology
مقاله انگلیسی
9 A neuro-heuristic approach for recognition of lung diseases from X-ray images
یک روش عصبی و اکتشافی برای شناخت بیماری های ریه از تصاویر اشعه ایکس-2019
Background and objective: The X-ray screening is one of the most popular methodologies in detection of respiratory system diseases. Chest organs are screened on the film or digital file which go to the doctor for evaluation. However, the analysis of x-ray images requires much experience and time. Clinical decision support is very important for medical examinations. The use of Computational Intelligence can simulate the evaluation and decision processes of a medical expert. We propose a method to provide a decision support for the doctor in order to help to consult each case faster and more precisely. Methods: We use image descriptors based on the spatial distribution of Hue, Saturation and Brightness values in x-ray images, and a neural network co-working with heuristic algorithms (Moth-Flame, Ant Lion) to detect degenerated lung tissues in x-ray image. The neural network evaluates the image and if the possibility of a respiratory disease is detected, the heuristic method identifies the degenerated tissues in the x-ray image in detail based on the use of the proposed fitness function. Results: The average accuracy is 79.06% in pre-detection stage, similarly the sensitivity and the specificity averaged for three pre-classified diseases are 84.22% and 66.7%, respectively. The misclassification errors are 3.23% for false positives and 3.76% for false negatives. Conclusions: The proposed neuro-heuristic approach addresses small changes in the structure of lung tissues, which appear in pneumonia, sarcoidosis or cancer and some consequences that may appear after the treatment. The results show high potential of the newly proposed method. Additionally, the method is flexible and has low computational burden.
Keywords: Medical image processing | Clinical decision support | Neural networks | Heuristic methods
مقاله انگلیسی
10 بررسی دندانه ها برای رنگریزی نخ در فرش‌های ایرانی باستان (قرن ۱۵ - ۱۷) با استفاده از روش‌های IBA
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 5 - تعداد صفحات فایل doc فارسی: 15
این تحقیق، کاربرد پروتون انتشار اشعه ایکس (PIXE) را به مطالعه رشته ‌های فرش که از فرش‌های ایرانی باستان متعلق به دوره صفویه اقتباس شده، نشان می‌دهد (۱۴۹۹ - ۱۷۲۲ CE). غلظت‌های اولیه در انواع پارچه‌های گرانبها اندازه‌گیری شده و با داده‌های مربوطه استخراج ‌شده از یک مجموعه از الیاف پشمی تهیه ‌شده از یک فرآیند رنگرزی متعارف اندازه‌گیری و مقایسه شد. نتایج نشان‌دهنده احتمالات شناسایی رنگ‌های مورد استفاده و دندانه و همچنین ناخالصی‌های غیر آلی موجود در نخ فرش هستند. این امر می‌تواند به مورخان هنری کمک کند تا تاریخ فرش و تنوع ریشه‌های تشکیل‌ دهنده آن را در طول سالیان درک کنند. با توجه به اطلاعات جمع‌آوری ‌شده، می توان توصیه کرد تا ترمیم کنندگان در موزه ها از تکنیک‌های رنگرزی دقیق مناسب برای احیای کیفیت بالای این فرش‌های منحصر به فرد استفاده کنند.
کلمات کلیدی : فرش ایرانی | دوره صفوی | پیکسی (آنالیز به روش پیکسی) | رنگ های طبیعی | دندانه
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