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|
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
Looking in the Right Place for Anomalies: Explainable Ai Through Automatic Location Learning
جستجوی مکان مناسب برای ناهنجاری ها: هوش مصنوعی قابل توضیح از طریق یادگیری خودکار مکان-2020
Deep learning has now become the de facto approach to the recognition of anomalies in medical imaging. Their ’black box’ way of classifying medical images into anomaly labels poses problems for their acceptance, particularly with clinicians. Current explainable AI methods offer justifications through visualizations such as heat maps but cannot guarantee that the network is focusing on the relevant image region fully containing the anomaly. In this paper we develop an approach to explainable AI in which the anomaly is assured to be overlapping the expected location when present. This is made possible by automatically extracting location-specific labels from textual reports and learning the association of expected locations to labels using a hybrid combination of Bi-Directional Long Short-Term Memory Recurrent Neural Networks (Bi- LSTM) and DenseNet-121. Use of this expected location to bias the subsequent attention-guided inference network based on ResNet101 results in the isolation of the anomaly at the expected location when present. The method is evaluated on a large chest X-ray dataset.
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
Advancing characterisation with statistics from correlative electron diffraction and X-ray spectroscopy, in the scanning electron microscope
پیشبرد خصوصیات با آمار پراش الکترون همبستگی و طیف سنجی اشعه X ، در میکروسکوپ الکترونی روبشی-2020
The routine and unique determination of minor phases in microstructures is critical to materials science. In metallurgy alone, applications include alloy and process development and the understanding of degradation in service. We develop a correlative method, exploring superalloy microstructures which are examined in the scanning electron microscope (SEM) using simultaneous energy dispersive X-ray spectroscopy (EDS) and electron backscatter diffraction (EBSD). This is performed at an appropriate length scale for characterisation of carbide phases’ shape, size, location, and distribution. EDS and EBSD data are generated using two different physical processes, but each provide a signature of the material interacting with the incoming electron beam. Recent advances in post-processing, driven by ‘big data’ approaches, include use of principal component analysis (PCA). Components are subsequently characterised to assign labels to a mapped region. To provide physically meaningful signals, the principal components may be rotated to control the distribution of variance. In this work, we develop this method further through a weighted PCA approach. We use the EDS and EBSD signals concurrently, thereby labelling each region using both EDS (chemistry) and EBSD (crystal structure) information. This provides a new method of amplifying signal-to-noise for very small phases in mapped regions, especially where the EDS or EBSD signal is not unique enough alone for classification.
Keywords: Principal component analysis | EBSD | EDS | microstructure | carbides | superalloy
Explainable AI and Mass Surveillance System-Based Healthcare Framework to Combat COVID-19 Like Pandemics
چارچوب بهداشتی مبتنی بر سیستم نظارت گسترده و هوش مصنوعی برای مبارزه با COVID-19 مانند موارد همه گیر-2020
Tactile edge technology that focuses on 5G or beyond 5G reveals an exciting approach to control infectious diseases such as COVID-19 internationally. The control of epidemics such as COVID-19 can be managed effectively by exploiting edge computation through the 5G wireless connectivity network. The implementation of a hierarchical edge computing system provides many advantages, such as low latency, scalability, and the protection of application and training model data, enabling COVID-19 to be evaluated by a dependable local edge server. In addition, many deep learning (DL) algorithms suffer from two crucial disadvantages: first, training requires a large COVID-19 dataset consisting of various aspects, which will pose challenges for local councils; second, to acknowledge the outcome, the findings of deep learning require ethical acceptance and clarification by the health care sector, as well as other contributors. In this article, we propose a B5G framework that utilizes the 5G network’s low-latency, high-bandwidth functionality to detect COVID-19 using chest X-ray or CT scan images, and to develop a mass surveillance system to monitor social distancing, mask wearing, and body temperature. Three DL models, ResNet50, Deep tree, and Inception v3, are investigated in the proposed framework. Furthermore, blockchain technology is also used to ensure the security of healthcare data.
A noble electrochemical sensor based on TiO2@CuO-N-rGO and poly (L-cysteine) nanocomposite applicable for trace analysis of flunitrazepam
یک حسگر الکتروشیمیایی اصیل مبتنی بر نانوکامپوزیت TiO2 @ CuO-N-rGO و پلی (L-سیستئین) قابل استفاده برای آنالیز ردیابی فلونیتراسپام-2020
Flunitrazepam or date rape medication with trade name of Rohypnol belongs to the benzodiazepines branch that is used as a sedative, anesthetic, anticonvulsant, muscle relaxant, and antianxiety drug. It is known as "drug of aggression" because of its very strong and longlasting effects on the central nervous system. The sedative influence of flunitrazepam drug increases with alcohol drinking, which causes mental and motor disorders and causes the victim to become silent. Due to its criminals use, its accurate measurement is crucial. In this work, a novel electrochemical sensor based on TiO2@CuO-N doped rGO, TiO2@CuO-N-rGO, nanocomposite and poly (L-cysteine), poly (L-Cys), is presented for trace analysis of flunitrazepam in aqueous solution. At first, TiO2@CuO-N-rGO nano-composite was synthesized by the sol-gel method and characterized by Raman spectroscopy, Fourier transform infrared, field emission scanning electron microscope, and X-ray diffraction analysis. Then, the suspension of the TiO2@CuO-N-rGO nano-composite was drop casted on the surface of the glassy carbon electrode (GCE/TiO2@CuO-N-rGO). After that, electro-polymerization of l-cysteine on the GCE/TiO2@CuO-N-rGO surface was performed by cyclic voltammetry (CV) method. The electrochemical characteristics of the GCE/TiO2@CuO-N-rGO/poly (L-Cys) surface was evaluated in the solution of ferri/ferrocyanide by electrochemical impedance spectroscopy (EIS) and CV techniques. The increase in current, change in oxidation peak potential, and the appearance of two reduction peaks indicated higher electron transfer rate with well-performed electrochemical process of flunitrazepam at the modified electrode surface compared to the bare GCE. These improvements originate from the synergistic effect of TiO2@CuO-N-rGO nanocomposite and poly (L-Cys). Finally, a linear relationship was resulted between the oxidation peak current and the concentration of flunitrazepam in the wide concentration range of 1 nM to 50 μM with a detection limit of 0.3 nM
Keywords: Flunitrazepam | Rohynol | Benzodiazepines | TiO2@CuO-N-rGO | L-cysteine | synergistic effect | Voltammetry
AI Chest 4 All
AI Chest 4 All-2020
AIChest4All is the name of the model used to label and screening diseases in our area of focus, Thailand, including heart disease, lung cancer, and tuberculosis. This is aimed to aid radiologist in Thailand especially in rural areas, where there is immense staff shortages. Deep learning is used in our methodology to classify the chest X-ray images from datasets namely, NIH set, which is separated into 14 observations, and the Montgomery and Shenzhen set, which contains chest X-ray images of patients with tuberculosis, further supplemented by the dataset from Udonthani Cancer hospital and the National Chest Institute of Thailand. The images are classified into six categories: no finding, suspected active tuberculosis, suspected lung malignancy, abnormal heart and great vessels, Intrathoracic abnormal findings, and Extrathroacic abnormal findings. A total of 201,527 images were used. Results from testing showed that the accuracy values of the categories heart disease, lung cancer, and tuberculosis were 94.11%, 93.28%, and 92.32%, respectively with sensitivity values of 90.07%, 81.02%, and 82.33%, respectively and the specificity values were 94.65%, 94.04%, and 93.54%, respectively. In conclusion, the results acquired have sufficient accuracy, sensitivity, and specificity values to be used. Currently, AIChest4All is being used to help several of Thailand’s government funded hospitals, free of charge.
Imaging of microdefects in ZnGeP2 single crystals by X-ray topography
تصویربرداری از ریزگردها در بلورهای تک ZnGeP2 توسط توپوگرافی با اشعه X-2020
The contrast from microdefects in ZnGeP2 crystals is studied. Simulation of images in X-ray topography based on the Borrmann effect is carried out for a model of a coherent inclusion of spherical form in an infinite isotropic matrix. For this simulation, a semiphenomenological theory of contrast from defects with a slowly changing deformation field is applied. It is shown that the contrast from the inclusion is a complex function, depending on the nature of defect (sign of the deformation of the matrix), the magnitude of the deformation caused by the defect, its depth in the crystal, the modulus of the diffraction vector g and the topography used (reflection or transmission). The most common images are intensity rosettes of double or triple contrast, whose lobes are elongated along the diffraction vector. These are created by inclusions, located near the X-ray exit surface of the sample. Analysis of experimental data shows that the majority of microdefects in ZnGeP2 revealed by Borrmann method (~96%) show good agreement with proposed model. All the features of the experimental images are explained by the theory. Additionally, the contrast from dislocation loops and from groups of big inclusions which have non-Coulombic deformation fields is observed
Keywords: B2. Nonlinear optic materials | A2. Bridgman technique | A2. Seed crystals | A1. Xray topography | A1. Defects| A1. Computer simulation
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