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
استفاده از روش GIS-AHP برای ارزیابی مستعد بودن زمین در زراعت ذرت در منطقه نیمه خشک ، ایران
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 17
هدف از این مطالعه تهیه نقشه های در زمینه استعداد زمین در زراعت ذرت در خاک های آهکی و شور در دشت مرودشت ، ایران است. برای تخمین وزنی ویژگی های خاک ، اقلیم و توپوگرافی از روش چندمعیاره ای از فرآیند سلسله مراتبی تحلیلی (AHP) استفاده شده است. طبق نتایج، بافت خاک بیشترین ضریب وزنی ویژه (0.20) را در زراعت ذرت نشان داد و پس از آن، هدایت الکتریکی (121/0) ، شیب (1 2 0) و pH (1111/0) بیشترین ضریب وزنی را نشان داد. نقشه مستعد بودن اراضی نشان داد که 38.72٪ (76.646.7 هکتار) از اراضی کشاورزی مورد مطالعه، خاک مستعدی در تولید ذرت داشتند یعنی در طبقه مناسب ، 26.89٪ (53216.0 هکتار) در طبقه متوسط و 9/23٪ (47473 هکتار) در طبقه کمی مناسب قرار گرفتند. حدود ٪ 41/10 (4/2086٪) منطقه مورد مطالعه مناسب زراعت ذرت نبود. می توان دریافت که داده های مربوط به ویژگی های خاک ، آب و هوا و توپوگرافی به نظر متخصصان محلی، اولین قدم در کشت محصولات زراعی است.
کلمات کلیدی: داده های خاک | مدل سازی | توپوگرافی | روش AHP | GIS | مرودشت
|مقاله ترجمه شده|
Automatic human identification from panoramic dental radiographs using the convolutional neural network
شناسایی خودکار انسان از رادیوگرافی دندانپزشکی پانوراما با استفاده از شبکه عصبی کانولوشن-2020
Human identification is an important task in mass disaster and criminal investigations. Although several automatic dental identification systems have been proposed, accurate and fast identification from panoramic dental radiographs (PDRs) remains a challenging issue. In this study, an automatic human identification system (DENT-net) was developed using the customized convolutional neural network (CNN). The DENT-net was trained on 15,369 PDRs from 6300 individuals. The PDRs were preprocessed by affine transformation and histogram equalization. The DENT-net took 128 128 7 square patches as input, including the whole PDR and six details extracted from the PDR. Using the DENT-net, the feature extraction took around 10 milliseconds per image and the running time for retrieval was 33.03 milliseconds in a 2000-individual database, promising an application on larger databases. The visualization of CNN showed that the teeth, maxilla, and mandible all contributed to human identification. The DENT-net achieved Rank-1 accuracy of 85.16% and Rank-5 accuracy of 97.74% for human identification. The present results demonstrated that human identification can be achieved from PDRs by CNN with high accuracy and speed. The present system can be used without any special equipment or knowledge to generate the candidate images. While the final decision should be made by human specialists in practice. It is expected to aid human identification in mass disaster and criminal investigation
Keywords: Forensic odontology | Human identification | Panoramic dental radiographs | Deep learning | Convolutional neural network
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
Interactive Transport Enquiry with AI Chatbot
استعلام حمل و نقل تعاملی با هوش مصنوعی Chatbot-2020
Public transportation is used efficiently by millions of people all over the world. People tend to travel to different places and at certain times they may feel completely lost in a new place. Our chatbot comes to rescue at this time. A Chatbot is often described as one of the most promising tools for communication between humans and machines using artificial intelligence. It is a software application that is used to conduct an online chat conversation via text by using natural language processing (NLP) and deep learning techniques. It provides direct contact with a live human agent in the form of GUI. This AI chatbot confirms the current location and the final destination of the user by asking a few questions. It examines the user’s query and extracts the appropriate entries from the database. The deep learning techniques that are used in this chatbot are responsible for understanding the user intents accurately to avoid any misconceptions. Once the user’s intention has been recognized, the chatbot provides the most relevant response for the user’s query request. Then the user gets all the information about the bus names along with their numbers so that the person can travel safely to the desired location. Our chatbot is implemented in pythons Keras library and used Tkinter for GUI.
Keywords: artificial intelligence | chatbot | natural language processing | deep learning | Keras | GUI | Tkinter
Nudging and citizen science: The effectiveness of feedback in energy-demand management
برهنگی و علم شهروندی: اثربخشی بازخورد در مدیریت تقاضای انرژی-2020
Nudging is a framework for directing individuals toward better behavior, both for personal and societal benefits, through heuristics that drive the decision-making process but without preventing any available choice. Considering the Grand Challenges that our society faces today, nudging represents an effective framework to tackle some of these pressing issues. In this work, we assessed the effectiveness of informational nudges in the form of detailed, customized feedback, within an energy-demand-management project. The project aligns energy production and demand, thereby reducing greenhouse gases and pollutant emissions to mitigate climate change. We also offered evidence that this kind of feedback is efficacious in involving individuals as citizen scientists, who volunteer their efforts toward the success of the environmentally-related aim of the project. The results of this research – based on surveys, electroencephalography measurements and online participation measures – indicate that feedback can be an effective tool to steer participants’ behavior under the libertarian paternalistic view of nudging, increase their motivation to contribute to citizen science, and improve their awareness about environmentally-related issues. In so doing, we provide evidence that nudging and citizen science can be jointly adopted toward the mitigation of pressing environmental issues.
Keywords: Nudging | Citizen science | Crowd | Energy-demand management | Grand challenges | Electroencephalography
Tabu search for min-max edge crossing in graphs
جستجوی تابو برای عبور از لبه های حداقل حداکثر در گراف ها -2020
Graph drawing is a key issue in the field of data analysis, given the ever-growing amount of information available today that require the use of automatic tools to represent it. Graph Drawing Problems (GDP) are hard combinatorial problems whose applications have been widely relevant in fields such as social network analysis and project management. While classically in GDPs the main aesthetic concern is re- lated to the minimization of the total sum of crossing in the graph (min-sum), in this paper we focus on a particular variant of the problem, the Min-Max GDP, consisting in the minimization of the maximum crossing among all egdes. Recently proposed in scientific literature, the Min-Max GDP is a challenging variant of the original min-sum GDP arising in the optimization of VLSI circuits and the design of in- teractive graph drawing tools. We propose a heuristic algorithm based on the tabu search methodology to obtain high-quality solutions. Extensive experimentation on an established benchmark set with both previous heuristics and optimal solutions shows that our method is able to obtain excellent solutions in short computation time.
Keywords: Combinatorial optimization | Graph drawing | Metaheuristics
Boosting solar steam generation by structure enhanced energy management
افزایش تولید بخار خورشیدی توسط ساختار پیشرفته مدیریت انرژی -2020
Interfacial solar-steam generation is a promising and cost-effective technology for both desalination and wastewater treatment. This process uses a photothermal evaporator to absorb sunlight and convert it into heat for water evaporation. However solar-steam generation can be somewhat inefficient due to energy losses via conduction, convection and radiation. Thus, efficient energy management is crucial for optimizing the performance of solar-steam generation. Here, via elaborate design of the configuration of photothermal materials, as well as warm and cold evaporation surfaces, performance in solar evaporation was significantly enhanced. This was achieved via a simultaneous reduction in energy loss with a net increase in energy gain from the environment, and recycling of the latent heat released from vapor condensation, diffusive reflectance, thermal radiation and convection from the evaporation surface. Overall, by using the new strategy, an evaporation rate of 2.94 kg m−2 h−1, with a corresponding energy efficiency of solar-steam generation beyond theoretical limit was achieved.
Keywords: Solar-steam generation | photothermal | energy management | latent heat recycling | reduced graphene oxide | desalination
Identifying regionalized co-variate driving factors to assess spatial distributions of saturated soil hydraulic conductivity using multivariate and state-space analyses
شناسایی عوامل محرک متغیر منطقه ای برای ارزیابی توزیع مکانی هدایت هیدرولیکی خاک اشباع شده با استفاده از تجزیه و تحلیل چند متغیره فضای دولت-2020
Saturated soil hydraulic conductivity (Ksat) is a key factor in hydrological management projects and its variability along the landscape hinders its correct use in the formulation of such projects. Ksat varies under different climatic and hydrological conditions at spatial scales as reported in several studies. However, co-regionalization of Ksat remains a challenging aspect with regard to identifying supportive co-variates and suitable spatial models. The objectives of this study were to (i) identify factors that relate Ksat with soil and topographic attributes and land-use systems along a 15-km transect using principal component analysis, and (ii) describe the spatial continuum of Ksat across the transect through co-regionalization with autoregressive state-space models. The transect was established in the Fragata River Watershed (FRW), Southern Brazil. One hundred soil sampling points were distributed along the transect at equal distances (150 m). Clay and sand fractions, soil organic carbon content, soil bulk density, soil macroporosity, Ksat, and the soil water retention curve were determined for the 0–20 cm layer at each point. Topographic attributes were derived from the digital elevation model and a land-use map was derived from satellite images. The highest and lowest spatial variabilities were exhibited by Ksat and soil organic carbon content, respectively. Applying the state-space approach, spatial relationships among Ksat and soil and topographic attributes, and land-use systems along the transect, could be found. Principal component analysis used jointly with state-space showed that macroporosity could be used as a proxy to estimate the spatial variation of Ksat in the FRW watershed, assessing surface and subsurface runoff potentials at areas of different land-use. Further studies should be carried out to investigate the use of the type of land-use system as a soil structural predictor of the spatial variations of Ksat at the watershed scale since it is nowadays an “easy-to-measure” variable from satellite images.
Keywords: Ksat | Soil and topographic attributes | Spatial variability | Land-use system
What electrophysiology tells us about Alzheimer’s disease: a window into the synchronization and connectivity of brain neurons
آنچه الکتروفیزیولوژی در مورد بیماری آلزایمر به ما می گوید: پنجره ای برای هماهنگ سازی و اتصال نورون های مغز-2020
Electrophysiology provides a real-time readout of neural functions and network capability in different brain states, on temporal (fractions of milliseconds) and spatial (micro, meso, and macro) scales unmet by other methodologies. However, current international guidelines do not endorse the use of electroencephalographic (EEG)/magnetoencephalographic (MEG) biomarkers in clinical trials performed in patients with Alzheimer’s disease (AD), despite a surge in recent validated evidence. This position paper of the ISTAART Electrophysiology Professional Interest Area endorses consolidated and translational electrophysiological techniques applied to both experimental animal models of AD and patients, to probe the effects of AD neuropathology (i.e., brain amyloidosis, tauopathy, and neurodegeneration) on neurophysiological mechanisms underpinning neural excitation/inhibition and neurotransmission as well as brain network dynamics, synchronization, and functional connectivity, reflecting thalamocortical and corticocortical residual capacity. Converging evidence shows relationships between abnormalities in EEG/MEG markers and cognitive deficits in groups of AD patients at different disease stages. The supporting evidence for the application of electrophysiology in AD clinical research as well as drug discovery pathways warrants an international initiative to include the use of EEG/MEG biomarkers in the main multicentric projects planned in AD patients, to produce conclusive findings challenging the present regulatory requirements and guidelines for AD studies.
Keywords: The Alzheimer’s Association International | Society to Advance Alzheimer’s Research | and Treatment (ISTAART) | Alzheimer’s disease (AD) | Electroencephalography and | magnetoencephalography (EEG and MEG) | Resting-state condition | Event-related potentials and magnetic fields | Preclinical and clinical research