Search results for: deep mixing column

Authors: Megha Gupta, Nupur Prakash

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Identification of plant diseases has been performed using machine learning and deep learning models on the datasets containing images of healthy and diseased plant leaves. The current study carries out an evaluation of some of the deep learning models based on convolutional neural network (CNN) architectures for identification of plant diseases. For this purpose, the publicly available New Plant Diseases Dataset, an augmented version of PlantVillage dataset, available on Kaggle platform, containing 87,900 images has been used. The dataset contained images of 26 diseases of 14 different plants and images of 12 healthy plants. The CNN models selected for the study presented in this paper are AlexNet, ZFNet, VGGNet (four models), GoogLeNet, and ResNet (three models). The selected models are trained using PyTorch, an open-source machine learning library, on Google Colaboratory. A comparative study has been carried out to analyze the high degree of accuracy achieved using these models. The highest test accuracy and F1-score of 99.59% and 0.996, respectively, were achieved by using GoogLeNet with Mini-batch momentum based gradient descent learning algorithm.

Keywords: comparative analysis, convolutional neural networks, deep learning, plant disease identification

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Authors: Gee-Cheol Kim, Joo-Won Kang

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Structural characteristics of spatial structure are different from that of rahmen structures and it has many factors that are unpredictable experientially. Both horizontal and vertical earthquake should be considered because of seismic behaviour characteristics of spatial structures. This experimental study is conducted about seismic response characteristics of roof structure according to the effect of columns or walls, through scale model of arch structure that has the basic dynamic characteristics of spatial structure. Though remarkable response is not occurred for horizontal direction in the region of higher frequency than the region of frequency that seismic energy is concentrated, relatively large response is occurred in vertical direction. It is proved that seismic response of arch structure with column is varied according to property of column.

Keywords: arch structure, seismic response, shaking table, spatial structure

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Authors: T. Damak, O. Kriaa, A. Baccar, M. A. Ben Ayed, N. Masmoudi

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In the last few years, Automatic Number Plate Recognition (ANPR) systems have become widely used in the safety, the security, and the commercial aspects. Forethought, several methods and techniques are computing to achieve the better levels in terms of accuracy and real time execution. This paper proposed a computer vision algorithm of Number Plate Localization (NPL) and Characters Segmentation (CS). In addition, it proposed an improved method in Optical Character Recognition (OCR) based on Deep Learning (DL) techniques. In order to identify the number of detected plate after NPL and CS steps, the Convolutional Neural Network (CNN) algorithm is proposed. A DL model is developed using four convolution layers, two layers of Maxpooling, and six layers of fully connected. The model was trained by number image database on the Jetson TX2 NVIDIA target. The accuracy result has achieved 95.84%.

Keywords: ANPR, CS, CNN, deep learning, NPL

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Authors: Shuo Wang

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The hydrogeological parameters of the engineering site and the hydraulic connection between the aquifers can be obtained by the pumping test. Through the recovery test, the characteristics of water level recovery and the law of surface subsidence recovery can be understood. The above two tests can provide the basis for subsequent engineering design. At present, the deformation of deep soil caused by pumping tests is often neglected. However, some studies have shown that the maximum settlement subject to groundwater drawdown is not necessarily on the surface but in the deep soil. In addition, the law of settlement recovery of each soil layer subject to water level recovery is not clear. If the deformation-sensitive structure is deep in the test site, safety accidents may occur. In this study, the pumping test and recovery test of a confined aquifer in Shanghai are introduced. The law of measured groundwater changes and surface subsidence are analyzed. In addition, the fluid-solid coupling model was established by ABAQUS based on the Biot consolidation theory. The models are verified by comparing the computed and measured results. Further, the variation law of water level and the deformation law of deep soil during pumping and recovery tests under different site conditions and different times and spaces are discussed through the above model. It is found that the maximum soil settlement caused by pumping in a confined aquifer is related to the permeability of the overlying aquitard and pumping time. There is a lag between soil deformation and groundwater changes, and the recovery rate of settlement deformation of each soil layer caused by the rise of water level is different. Finally, some possible research directions are proposed to provide new ideas for academic research in this field.

Keywords: coupled hydro-mechanical analysis, deep ground settlement, numerical simulation, pumping test, recovery test

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Authors: Smitha Haridasan, Zelalem Demissie, Atri Dutta, Ajita Rattani

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Aided by the wind, all it takes is one ember and a few minutes to create a wildfire. Wildfires are growing in frequency and size due to climate change. Wildfires and its consequences are one of the major environmental concerns. Every year, millions of hectares of forests are destroyed over the world, causing mass destruction and human casualties. Thus early detection of wildfire becomes a critical component to mitigate this threat. Many computer vision-based techniques have been proposed for the early detection of forest fire using video surveillance. Several computer vision-based methods have been proposed to predict and detect forest fires at various spectrums, namely, RGB, HSV, and YCbCr. The aim of this paper is to propose a multi-spectral deep learning model that combines information from different spectrums at intermediate layers for accurate fire detection. A heterogeneous dataset assembled from publicly available datasets is used for model training and evaluation in this study. The experimental results show that multi-spectral deep learning models could obtain an improvement of about 4.68 % over those based on a single spectrum for fire detection.

Keywords: deep learning, forest fire detection, multi-spectral learning, natural hazard detection

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Authors: A. Čelan, M. Ćosić, D. Rušić, N. Kuzmanić

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Conducting crystallization in an agitated vessel requires a proper selection of mixing parameters that would result in a production of crystals of specific properties. In dual impeller systems, which are characterized by a more complex hydrodynamics due to the possible fluid flow interactions, revealing a clear link between mixing parameters and crystallization kinetics is still an open issue. The aim of this work is to establish this connection by investigating how fluid flow patterns, generated by two impellers mounted on the same shaft, reflect on metastable zone width of borax decahydrate, one of the most important parameters of the crystallization process. Investigation was carried out in a 15-dm³ bench scale batch cooling crystallizer with an aspect ratio (H/T) equal to 1.3. For this reason, two radial straight blade turbines (4-SBT) were used for agitation. Experiments were conducted at different impeller spacings at the state of complete suspension. During the process of an unseeded batch cooling crystallization, solution temperature and supersaturation were continuously monitored what enabled a determination of the metastable zone width. Hydrodynamic conditions in the vessel achieved at different impeller spacings investigated were analyzed in detail. This was done firstly by measuring the mixing time required to attain the desired level of homogeneity. Secondly, fluid flow patterns generated in a described dual impeller system were both photographed and simulated by VisiMix Turbulent software. Also, a comparison of these two visualization methods was performed. Experimentally obtained results showed that metastable zone width is definitely affected by the hydrodynamics in the crystallizer. This means that this crystallization parameter can be controlled not only by adjusting the saturation temperature or cooling rate, as is usually done, but also by choosing a suitable impeller spacing that will result in a formation of crystals of wanted size distribution.

Keywords: dual impeller crystallizer, fluid flow pattern, metastable zone width, mixing time, radial impeller

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Authors: Neda Azimi, Masoud Rahimi, Faezeh Mohammadi

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Computational fluid dynamics (CFD) simulation was performed to investigate the effect of ferrofluid stimulation on hydrodynamic and mass transfer characteristics of two immiscible liquid phases in a Y-micromixer. The main purpose of this work was to develop a numerical model that is able to simulate hydrodynamic of the ferrofluid flow under magnetic field and determine its effect on mass transfer characteristics. A uniform external magnetic field was applied perpendicular to the flow direction. The volume of fluid (VOF) approach was used for simulating the multiphase flow of ferrofluid and two-immiscible liquid flows. The geometric reconstruction scheme (Geo-Reconstruct) based on piecewise linear interpolation (PLIC) was used for reconstruction of the interface in the VOF approach. The mass transfer rate was defined via an equation as a function of mass concentration gradient of the transported species and added into the phase interaction panel using the user-defined function (UDF). The magnetic field was solved numerically by Fluent MHD module based on solving the magnetic induction equation method. CFD results were validated by experimental data and good agreements have been achieved, which maximum relative error for extraction efficiency was about 7.52 %. It was showed that ferrofluid actuation by a magnetic field can be considered as an efficient mixing agent for liquid-liquid two-phase mass transfer in microdevices.

Keywords: CFD modeling, hydrodynamic, micromixer, ferrofluid, mixing

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Authors: Tanios Saliba, Jad Wakim, Elie Awwad

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Clayey soils exhibit undesirable problems in civil engineering project: poor bearing soil capacity, shrinkage, cracking, …etc. On the other hand, the increasing production of bottom ash and its disposal in an eco-friendly manner is a matter of concern. Soil stabilization using bottom ash is a new technic in the geo-environmental engineering. It can be used wherever a soft clayey soil is encountered in foundations or road subgrade, instead of using old technics such as cement-soil mixing. This new technology can be used for road embankments and clayey foundations platform (shallow or deep foundations) instead of replacing bad soil or using old technics which aren’t eco-friendly. Moreover, applying this new technic in our geotechnical engineering projects can reduce the disposal of the bottom ash problem which is getting bigger day after day. The research consists of mixing clayey soil with different percentages of bottom ash at different values of water content, and evaluates the mechanical properties of every mix: the percentages of bottom ash are 10% 20% 30% 40% and 50% with values of water content of 25% 35% and 45% of the mix’s weight. Before testing the different mixes, clayey soil’s properties were determined: Atterbeg limits, soil’s cohesion and friction angle and particle size distribution. In order to evaluate the mechanical properties and behavior of every mix, different tests are conducted: -Direct shear test in order to determine the cohesion and internal friction angle of every mix. -Unconfined compressive strength (stress strain curve) to determine mix’s elastic modulus and compressive strength. Soil samples are prepared in accordance with the ASTM standards, and tested at different times, in order to be able to emphasize the influence of the curing period on the variation of the mix’s mechanical properties and characteristics. As of today, the results obtained are very promising: the mix’s cohesion and friction angle vary in function of the bottom ash percentage, water content and curing period: the cohesion increases enormously before decreasing for a long curing period (values of mix’s cohesion are larger than intact soil’s cohesion) while internal friction angle keeps on increasing even when the curing period is 28 days (the tests largest curing period), which give us a better soil behavior: less cracks and better soil bearing capacity.

Keywords: bottom ash, Clayey soil, mechanical properties, tests

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Authors: Ijaz Ahmed, Anwar Zada, Muhammad Waqas, M. U. Ashraf

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We demonstrate the performance of a very efficient tagger applies on hadronically decaying top quark pairs as signal based on deep neural network algorithms and compares with the QCD multi-jet background events. A significant enhancement of performance in boosted top quark events is observed with our limited computing resources. We also compare modern machine learning approaches and perform a multivariate analysis of boosted top-pair as well as single top quark production through weak interaction at √s = 14 TeV proton-proton Collider. The most relevant known background processes are incorporated. Through the techniques of Boosted Decision Tree (BDT), likelihood and Multlayer Perceptron (MLP) the analysis is trained to observe the performance in comparison with the conventional cut based and count approach

Keywords: top tagger, multivariate, deep learning, LHC, single top

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Authors: Xiao Zhou, Jianlin Cheng

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A single amino acid mutation can have a significant impact on the stability of protein structure. Thus, the prediction of protein stability change induced by single site mutations is critical and useful for studying protein function and structure. Here, we presented a deep learning network with the dropout technique for predicting protein stability changes upon single amino acid substitution. While using only protein sequence as input, the overall prediction accuracy of the method on a standard benchmark is >85%, which is higher than existing sequence-based methods and is comparable to the methods that use not only protein sequence but also tertiary structure, pH value and temperature. The results demonstrate that deep learning is a promising technique for protein stability prediction. The good performance of this sequence-based method makes it a valuable tool for predicting the impact of mutations on most proteins whose experimental structures are not available. Both the downloadable software package and the user-friendly web server (DNpro) that implement the method for predicting protein stability changes induced by amino acid mutations are freely available for the community to use.

Keywords: bioinformatics, deep learning, protein stability prediction, biological data mining

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Authors: Qasim Zaheer, Jehanzeb Masud

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The understanding of entrainment and mixing phenomenon in the ejector pump is of pivotal importance for designing and performance estimation. In this paper, the existence of turbulent vortical structures due to Kelvin-Helmholtz instability at the free surface between the motive and the entrained fluids streams are simulated using Embedded LES methodology. The efficacy of Embedded LES for simulation of complex flow field of ejector pump is evaluated using ANSYS Fluent®. The enhanced mixing and entrainment process due to breaking down of larger eddies into smaller ones as a consequence of Vortex Stretching phenomenon is captured in this study. Moreover, the flow field characteristics of ejector pump like pressure velocity fields and mass flow rates are analyzed and validated against the experimental results.

Keywords: Kelvin Helmholtz instability, embedded LES, complex flow field, ejector pump

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Authors: Sowmiyaa V. S., Tiruvengala Padma, Dhanasekaran B.

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Due to urbanization, traffic congestion, air pollution and fuel consumption underground metros are constructed in urban cities nowadays. These metros reduce the commutation time and makes the daily transportation in urban cities hassle free. To construct the underground metros deep excavations are to be carried out. These excavations should be supported by an appropriate earth retaining structures to provide stability and to prevent deformation failures. The failure of deep excavations is catastrophic and hence appropriate caution need to be carried out during design and construction stages. This paper covers the construction aspects, equipment, quality control, design aspects of one of the earth retaining systems the Diaphragm Walls.

Keywords: underground metros, diaphragm wall, quality control of diaphragm wall, design aspects of diaphragm wall

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Authors: Yongrong Li, Ralf Domroes

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Due to significant energy savings, enablement of higher machining speed as well as environmentally friendly features, Minimum Quantity Lubrication (MQL) has been used for many machining processes efficiently. However, in the deep hole drilling field (small tool diameter D < 5 mm) and long tool (length L > 25xD) it is always a bottle neck for a single channel MQL system. The single channel MQL, based on the Venturi principle, faces a lack of enough oil quantity caused by dropped pressure difference during the deep hole drilling process. In this paper, a system concept based on a bypass design has explored its possibility to dynamically reach the required pressure difference between the air inlet and the inside of aerosol generator, so that the deep hole drilling demanded volume of oil can be generated and delivered to tool tips. The system concept has been investigated in static and dynamic laboratory testing. In the static test, the oil volume with and without bypass control were measured. This shows an oil quantity increasing potential up to 1000%. A spray pattern test has demonstrated the differences of aerosol particle size, aerosol distribution and reaction time between single channel and bypass controlled single channel MQL systems. A dynamic trial machining test of deep hole drilling (drill tool D=4.5mm, L= 40xD) has been carried out with the proposed system on a difficult machining material AlSi7Mg. The tool wear along a 100 meter drilling was tracked and analyzed. The result shows that the single channel MQL with a bypass control can overcome the limitation and enhance deep hole drilling with a small tool. The optimized combination of inlet air pressure and bypass control results in a high quality oil delivery to tool tips with a uniform and continuous aerosol flow.

Keywords: deep hole drilling, green production, Minimum Quantity Lubrication (MQL), near dry machining

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Authors: Sajeeha Ansar, Asad Ali Safi, Sheikh Ziauddin, Ahmad R. Shahid, Faraz Ahsan

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Gliomas are most challenging and aggressive type of tumors which appear in different sizes, locations, and scattered boundaries. CNN is most efficient deep learning approach with outstanding capability of solving image analysis problems. A fully automatic deep learning based 2D-CNN model for brain tumor segmentation is presented in this paper. We used small convolution filters (3 x 3) to make architecture deeper. We increased convolutional layers for efficient learning of complex features from large dataset. We achieved better results by pushing convolutional layers up to 16 layers for HGG model. We achieved reliable and accurate results through fine-tuning among dataset and hyper-parameters. Pre-processing of this model includes generation of brain pipeline, intensity normalization, bias correction and data augmentation. We used the BRATS-2015, and Dice Similarity Coefficient (DSC) is used as performance measure for the evaluation of the proposed method. Our method achieved DSC score of 0.81 for complete, 0.79 for core, 0.80 for enhanced tumor regions. However, these results are comparable with methods already implemented 2D CNN architecture.

Keywords: brain tumor segmentation, convolutional neural networks, deep learning, HGG

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Authors: Nabiyeva Jamala

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We divide water into drinking, mineral, industrial, technical and thermal-energetic types according to its use and purpose. Drinking water must comply with sanitary requirements and norms according to organoleptic devices and physical and chemical properties. Mineral water - must comply with the norms due to some components having therapeutic properties. Industrial water must fulfill its normative requirements by being used in the industrial field. Technical water should be suitable for use in the field of agriculture, household, and irrigation, and the normative requirements should be met. Heat-energy water is used in the national economy, and it consists of thermal and energy water. Water is a filter-accumulator of all types of pollutants entering the environment. This is explained by the fact that it has the property of dissolving compounds of mineral and gaseous water and regular water circulation. Environmentally clean, pure, non-toxic water is vital for the normal life activity of humans, animals and other living beings. Chemical pollutants enter water basins mainly with wastewater from non-ferrous and ferrous metallurgy, oil, gas, chemical, stone, coal, pulp and paper and forest materials processing industries and make them unusable. Wastewater from the chemical, electric power, woodworking and machine-building industries plays a huge role in the pollution of water sources. Chlorine compounds, phenols, and chloride-containing substances have a strong lethal-toxic effect on organisms when mixed with water. Heavy metals - lead, cadmium, mercury, nickel, copper, selenium, chromium, tin, etc. water mixed with ingredients cause poisoning in humans, animals and other living beings. Thus, the mixing of selenium with water causes liver diseases in people, the mixing of mercury with the nervous system, and the mixing of cadmium with kidney diseases. Pollution of the World's ocean waters and other water basins with oil and oil products is one of the most dangerous environmental problems facing humanity today. So, mixing even the smallest amount of oil and its products in drinking water gives it a bad, unpleasant smell. Mixing one ton of oil with water creates a special layer that covers the water surface in an area of 2.6 km2. As a result, the flood of light, photosynthesis and oxygen supply of water is getting weak and there is a great danger to the lives of living beings.

Keywords: chemical pollutants, wastewater, SSAM, polyacrylamide

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Authors: Guantao Bai

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Public space is an indispensable and important component of the urban built environment. How to more accurately evaluate the usage characteristics of public space can help improve its spatial quality. Compared to traditional survey methods, computer vision technology based on deep learning has advantages such as dynamic observation and low cost. This study takes the public space of Shaping Park as an example and, based on deep learning computer vision technology, processes and analyzes the image data of the public space to obtain the spatial usage characteristics and spatiotemporal characteristics of the public space. Research has found that the spontaneous activity time in public spaces is relatively random with a relatively short average activity time, while social activities have a relatively stable activity time with a longer average activity time. Computer vision technology based on deep learning can effectively describe the spatial usage characteristics of the research area, making up for the shortcomings of traditional research methods and providing relevant support for creating a good public space.

Keywords: computer vision, deep learning, public spaces, using features

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Authors: Nathenal Thomas Lambamo

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Based on the fact that more than 85% of the labor force and 90% of the export earnings are taken by agriculture in Ethiopia and it can be said that it is the backbone of the overall socio-economic activities in the country. Among the cereal crops that the agriculture sector provides for the country, wheat is the third-ranking one preceding teff and maize. In the present day, wheat is in higher demand related to the expansion of industries that use them as the main ingredient for their products. The local supply of wheat for these companies covers only 35 to 40% and the rest 60 to 65% percent is imported on behalf of potential customers that exhaust the country’s foreign currency reserves. The above facts show that the need for this crop in the country is too high and in reverse, the productivity of the crop is very less because of these reasons. Wheat disease is the most devastating disease that contributes a lot to this unbalance in the demand and supply status of the crop. It reduces both the yield and quality of the crop by 27% on average and up to 37% when it is severe. This study aims to detect the most frequent and degrading wheat diseases, Septoria and Leaf rust, using the most efficiently used subset of machine learning technology, deep learning. As a state of the art, a deep learning class classification technique called Convolutional Neural Network (CNN) has been used to detect diseases and has an accuracy of 99.01% is achieved.

Keywords: septoria, leaf rust, deep learning, CNN

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Authors: Maulik Parmar, Sumeet Sandhu

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The online fashion industry experiences high product return rates. Many returns are because of size/fit mismatches -the size scale on labels can vary across brands, the size parameters may not capture all fit measurements, or the product may have manufacturing defects. Warehouse quality check of garment sizes can be semi-automated to improve speed and accuracy. This paper presents an approach for automatically measuring garment sizes from a single image of the garment -using Deep Learning to learn garment keypoints. The paper focuses on the waist size measurement of jeans and can be easily extended to other garment types and measurements. Experimental results show that this approach can greatly improve the speed and accuracy of today’s manual measurement process.

Keywords: convolutional neural networks, deep learning, distortion, garment measurements, image warping, keypoints

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Authors: Viet-Hung Truong, Seung-Eock Kim

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This paper presents a robust software package for practical advanced analysis of space steel framed structures. The pre- and post-processors of the presented software package are coded in the C++ programming language while the solver is written by using the FORTRAN programming language. A user-friendly graphical interface of the presented software is developed to facilitate the modeling process and result interpretation of the problem. The solver employs the stability functions for capturing the second-order effects to minimize modeling and computational time. Both the plastic-hinge and fiber-hinge beam-column elements are available in the presented software. The generalized displacement control method is adopted to solve the nonlinear equilibrium equations.

Keywords: advanced analysis, beam-column, fiber-hinge, plastic hinge, steel frame

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Authors: In-Tsang Lin, Chiching Wei

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Reading order is an important task in many digitization scenarios involving the preservation of the logical structure of a document. From the paper survey, it finds that the state-of-the-art algorithm could not fulfill to get the accurate reading order in the portable document format (PDF) files with rich formats, diverse layout arrangement. In recent years, most of the studies on the analysis of reading order have targeted the specific problem of associating layout components with logical labels, while less attention has been paid to the problem of extracting relationships the problem of detecting the reading order relationship between logical components, such as cross-references. Over 3 years of development, the company Foxit has demonstrated the layout recognition (LR) engine in revision 20601 to eager for the accuracy of the reading order. The bounding box of each paragraph can be obtained correctly by the Foxit LR engine, but the result of reading-order is not always correct for single-column, and two-column layout format due to the table issue, formula issue, and multiple mini separated bounding box and footer issue. Thus, the algorithm is developed to improve the accuracy of the reading order based on the Foxit LR structure. In this paper, a creative observation method (Here called the MESH method) is provided here to open a new chance in the research of the reading-order field. Here two important parameters are introduced, one parameter is the number of the bounding box on the right side of the present bounding box (NRight), and another parameter is the number of the bounding box under the present bounding box (Nunder). And the normalized x-value (x/the whole width), the normalized y-value (y/the whole height) of each bounding box, the x-, and y- position of each bounding box were also put into consideration. Initial experimental results of single column layout format demonstrate a 19.33% absolute improvement in accuracy of the reading-order over 7 PDF files (total 150 pages) using our proposed method based on the LR structure over the baseline method using the LR structure in 20601 revision, which its accuracy of the reading-order is 72%. And for two-column layout format, the preliminary results demonstrate a 44.44% absolute improvement in accuracy of the reading-order over 2 PDF files (total 18 pages) using our proposed method based on the LR structure over the baseline method using the LR structure in 20601 revision, which its accuracy of the reading-order is 0%. Until now, the footer issue and a part of multiple mini separated bounding box issue can be solved by using the MESH method. However, there are still three issues that cannot be solved, such as the table issue, formula issue, and the random multiple mini separated bounding boxes. But the detection of the table position and the recognition of the table structure are out of the scope in this paper, and there is needed another research. In the future, the tasks are chosen- how to detect the table position in the page and to extract the content of the table.

Keywords: document processing, reading order, observation method, layout recognition

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Authors: Carlos Roncero-Parra, Alfonso Parreño-Torres, Jorge Mateo Sotos, Alejandro L. Borja

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In this research, different methods based on machine/deep learning algorithms are presented for the classification and diagnosis of patients with mental disorders such as alzheimer. For this purpose, the signals obtained from 32 unipolar electrodes identified by non-invasive EEG were examined, and their basic properties were obtained. More specifically, different well-known machine learning based classifiers have been used, i.e., support vector machine (SVM), Bayesian linear discriminant analysis (BLDA), decision tree (DT), Gaussian Naïve Bayes (GNB), K-nearest neighbor (KNN) and Convolutional Neural Network (CNN). A total of 668 patients from five different hospitals have been studied in the period from 2011 to 2021. The best accuracy is obtained was around 93 % in both ADM and ADA classifications. It can be concluded that such a classification will enable the training of algorithms that can be used to identify and classify different mental disorders with high accuracy.

Keywords: alzheimer, machine learning, deep learning, EEG

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Authors: R. Fegas, S. Zerkout, S. Taberkokt, M. Righezza

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The present work demonstrate the potential of Betacyclodextrine (BCD) for the chiral analysis of a drug .Various separation mechanisms were applied and several parameters affecting the separation were studied, including the type and concentration of chiral selector, and pH of buffer. A simple and sensitive high-performance liquid chromatography (HPLC) method was developed as an assay for fexofenadine enantiomers in pharmaceutical preparation. Fexofenadine enantiomers were separated using a mobile phase of 0.25mM NaH2PO4–acetonitrile (65:35, v/v) – Betacyclodextrine on achiral phenyl-urea column at a flow rate of 1ml/min and measurement at 220nm. The chiral mechanism of separation was mainly based on specific interaction between the solute and the stationary phase. The retention was directly controlled by mobile phase composition but not the selectivity which results of the two mechanisms, electrostatic interactions and partition mechanism.

Keywords: fexofenadine enantiomer, HPLC, achiral phenyl-urea column

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Authors: S. Doyle, G. A. Aggidis

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Wave energy converter technologies continue to show good progress in worldwide research. One of the most researched technologies, the Oscillating Water Column (OWC), is arguably one of the most popular categories within the converter technologies due to its robustness, simplicity and versatility. However, the versatility of the OWC is still largely untapped with most deployments following similar trends with respect to applications and operating systems. As the competitiveness of the energy market continues to increase, the demand for wave energy technologies to be innovative also increases. For existing wave energy technologies, this requires identifying areas to diversify for lower costs of energy with respect to applications and synergies or integrated systems. This paper provides a review of all OWCs systems integrated into alternative applications in the past and present. The aspects and variation in their design, deployment and system operation are discussed. Particular focus is given to the Multi-OWCs (M-OWCs) and their great potential to increase capture on a larger scale, especially in synergy applications. It is made clear that these steps need to be taken in order to make wave energy a competitive and viable option in the renewable energy mix as progression to date shows that stand alone single function devices are not economical. Findings reveal that the trend of development is moving toward these integrated applications in order to reduce the Levelised Cost of Energy (LCOE) and will ultimately continue in this direction in efforts to make wave energy a competitive option in the renewable energy mix.

Keywords: wave energy converter, oscillating water column, ocean energy, renewable energy

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Authors: Mahdi Akhbardeh

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Synchronization of neuronal firing is a hallmark of several neurological diseases. Recently, stimulation techniques have been developed which make it possible to desynchronize oscillatory neuronal activity in a mild and effective way, without suppressing the neurons' firing. As yet, these techniques are being used to establish demand-controlled deep brain stimulation (DBS) techniques for the therapy of movement disorders like severe Parkinson's disease or essential tremor. We here present a first conceptualization suggesting that the nucleus accumbens is a promising target for the standard, that is, permanent high-frequency, DBS in patients with severe and chronic obsessive-compulsive disorder (OCD). In addition, we explain how demand-controlled DBS techniques may be applied to the therapy of OCD in those cases that are refractory to behavioral therapies and pharmacological treatment.

Keywords: stereotactic neurosurgery, deep brain stimulation, obsessive-compulsive disorder, phase resetting

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Authors: Amin Mojiri, Lou Ziyang

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Constructed wetland (CW) is a reasonable method to treat waste water. Current study was carried out to co-treat landfill leachate and domestic waste water using a CW system. Typha domingensis was transplanted to CW, which encloses two substrate layers of adsorbents named ZELIAC and zeolite. Response surface methodology and central composite design were employed to evaluate experimental data. Contact time (h) and leachate to waste water mixing ratio (%; v/v) were selected as independent factors. Phenols and manganese removal were selected as dependent responses. At optimum contact time (48.7 h) and leachate to waste water mixing ratio (20.0%), removal efficiencies of phenols and manganese removal efficiencies were 90.5%, and 89.4%, respectively.

Keywords: constructed wetland, Manganese, phenols, Thypha domingensis

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Authors: Rahmat Ali, Amjad Naseer, Abid A. Shah

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This paper is an attempt to presents the performance of building observed during 26th Oct, 2015 earthquake in District Swat and Shangla region. Most of the buildings in the earthquake hit areas were built with Rubble stone masonry, dress Stone Masonry, brick masonry with and without RC column, Brick masonry with RC beams and column, Block Masonry with and without RC column. It was found that most of the buildings were built without proper supervision and without following any codes. A majority of load bearing masonry walls were highly affected during the earthquake. The load bearing walls built with rubble stone masonry were collapsed resulting huge damages and loss of property and life. Load bearing bricks masonry walls were also affected in most of the region. In some residential buildings the bricks were crushed in a single brick walls. Severe cracks were also found in double brick masonry walls. In RC frame structure beams and columns were also seriously affected. A majority of building structures were non-engineered. Some buildings designed by unskilled local consultants were also affected during the earthquake. Several architectural and structural mistakes were also found in various buildings designed by local consultant. It was found that the structures were collapsed prematurely either because of unskillful labor and using substandard materials or avoiding delicate repair, maintenance, and health monitoring activities because of lack of available sophisticated technology in our country.

Keywords: cracks, collapse, earthquake, masonry, repair

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Authors: Sai Maniveer Adapa, Sai Guptha Perla, Adithya Reddy P.

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Thyroid nodules can often be diagnosed with ultrasound imaging, although differentiating between benign and malignant nodules can be challenging for medical professionals. This work suggests a novel approach to increase the precision of thyroid nodule identification by combining machine learning and deep learning. The new approach first extracts information from the ultrasound pictures using a deep learning method known as a convolutional autoencoder. A support vector machine, a type of machine learning model, is then trained using these features. With an accuracy of 92.52%, the support vector machine can differentiate between benign and malignant nodules. This innovative technique may decrease the need for pointless biopsies and increase the accuracy of thyroid nodule detection.

Keywords: thyroid tumor diagnosis, ultrasound images, deep learning, machine learning, convolutional auto-encoder, support vector machine

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Authors: Dara Satyadileep, Abdallah S. Berrouk

Abstract:

Selective absorption of Hydrogen Sulphide (H2S) using methyldiethanol amine (MDEA) has become a point of interest as means of minimizing capital and operating costs of gas sweetening plants. This paper discusses the prominence of optimum design of column internals to best achieve H2S selectivity using MDEA. To this end, a kinetics-based process simulation model has been developed for a commercial gas sweetening unit. Trends of sweet gas H2S & CO2 contents as function of fraction active area (and hence residence time) have been explained through analysis of interdependent heat and mass transfer phenomena. Guidelines for column internals design in order to achieve desired degree of H2S selectivity are provided. Also the effectiveness of various operating conditions in achieving H2S selectivity for an industrial absorber with fixed internals is investigated.

Keywords: gas sweetening, H2S selectivity, methyldiethanol amine, process simulation, residence time

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Authors: Javad Saeidaskari, Mohammad Hassan Baziar

Abstract:

Soil structures have been frequently damaged during piping, earthquake and other types of failures. As far as adverse circumstances were developed subsequent to piping or other similar failure types, hydraulic parameters of soil such as hydraulic conductivity should be considered. As a result, acquiring an approach to diminish soil permeability is inevitable. There are many ground improvement methods to reduce seepage, which are classified under soil treatment and stabilization methods. Recently, one of the soil improvement methods is known as nanogeotechnology. This study aims to investigate the influence of Cloisite 30B nanoclay on permeability of compacted clayey sand soils. The samples are prepared by mixing two soil types, including Kaolin clay and Firouzkooh sand, in 1:9 and 1:5 clay:sand (by mass) proportions. In experimental procedure, initially, the optimum water content and maximum dry unit weight of each samples were obtained for compaction. Then, series of permeability tests were conducted by triaxial apparatus on prepared specimens with identical relative density of 95% of maximum dry density and water content of 1% wet of optimum for different weight percentages of nanoclay (1% to 4%). Therefore, in this paper, the effect of time on treated specimen was appraised, as well as two approaches of manual mixing and ball milling were compared to reveal the importance of dispersion issue. The results show that adding nanoclay up to 3%, as its optimum content, causes notable reduction in permeability (1.60e-03 to 5.51e-05 cm/s and 3.32e-04 to 8.44e-07 cm/s in samples with 1:9 and 1:5 mixture proportions, respectively). The hydraulic conductivity of treated clayey sand (1:5 mixture proportion with 3% nanoclay) decreases gradually from 8.44e-07 to 3.00e-07 cm/s within 90 days and then tends to be consistent. The influence of mixing method on permeability results shows that the utilization of ball mill mixing effectively leads to lower values than those of manual mixing, in other words, by adding 3% nanoclay, hydraulic conductivity of specimen declines from 8.44e-07 to 2.00e-07 cm/s. In order to evaluate the interaction between soil particles and, to ensure proper dispersion of nanoparticles through clayey sand mixture, they were magnified by means of scanning electron microscope (SEM). In conclusion, the nanoclay particles in vicinity of moisture can cause soil stabilization to prevent water penetration, which eventually result in lower usage of clay and operation costs.

Keywords: nanoclay, cloisite 30b, clayey sand, hydraulic conductivity

Procedia PDF Downloads 326

Authors: Xiaodong Li, Peng Gao, Chao-Jung Huang, Shiying Hao, Xuefeng B. Ling, Yongxia Han, Yaqi Zhang, Le Zheng, Chengyin Ye, Modi Liu, Minjie Xia, Changlin Fu, Bo Jin, Karl G. Sylvester, Eric Widen

Abstract:

Predicting the risk of Pancreatic Adenocarcinoma (PA) in advance can benefit the quality of care and potentially reduce population mortality and morbidity. The aim of this study was to develop and prospectively validate a risk prediction model to identify patients at risk of new incident PA as early as 3 months before the onset of PA in a statewide, general population in Maine. The PA prediction model was developed using Deep Neural Networks, a deep learning algorithm, with a 2-year electronic-health-record (EHR) cohort. Prospective results showed that our model identified 54.35% of all inpatient episodes of PA, and 91.20% of all PA that required subsequent chemoradiotherapy, with a lead-time of up to 3 months and a true alert of 67.62%. The risk assessment tool has attained an improved discriminative ability. It can be immediately deployed to the health system to provide automatic early warnings to adults at risk of PA. It has potential to identify personalized risk factors to facilitate customized PA interventions.

Keywords: cancer prediction, deep learning, electronic health records, pancreatic adenocarcinoma

Procedia PDF Downloads 128