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susceptibility mapping</title> <meta name="description" content="Search results for: quantitative susceptibility mapping"> <meta name="keywords" content="quantitative susceptibility mapping"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="quantitative susceptibility mapping"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 5082</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: quantitative susceptibility mapping</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5082</span> Landslide Susceptibility Mapping: A Comparison between Logistic Regression and Multivariate Adaptive Regression Spline Models in the Municipality of Oudka, Northern of Morocco</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Benchelha">S. Benchelha</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20C.%20Aoudjehane"> H. C. Aoudjehane</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Hakdaoui"> M. Hakdaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20El%20Hamdouni"> R. El Hamdouni</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Mansouri"> H. Mansouri</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Benchelha"> T. Benchelha</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Layelmam"> M. Layelmam</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Alaoui"> M. Alaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The logistic regression (LR) and multivariate adaptive regression spline (MarSpline) are applied and verified for analysis of landslide susceptibility map in Oudka, Morocco, using geographical information system. From spatial database containing data such as landslide mapping, topography, soil, hydrology and lithology, the eight factors related to landslides such as elevation, slope, aspect, distance to streams, distance to road, distance to faults, lithology map and Normalized Difference Vegetation Index (NDVI) were calculated or extracted. Using these factors, landslide susceptibility indexes were calculated by the two mentioned methods. Before the calculation, this database was divided into two parts, the first for the formation of the model and the second for the validation. The results of the landslide susceptibility analysis were verified using success and prediction rates to evaluate the quality of these probabilistic models. The result of this verification was that the MarSpline model is the best model with a success rate (AUC = 0.963) and a prediction rate (AUC = 0.951) higher than the LR model (success rate AUC = 0.918, rate prediction AUC = 0.901). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=landslide%20susceptibility%20mapping" title="landslide susceptibility mapping">landslide susceptibility mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20logistic" title=" regression logistic"> regression logistic</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20adaptive%20regression%20spline" title=" multivariate adaptive regression spline"> multivariate adaptive regression spline</a>, <a href="https://publications.waset.org/abstracts/search?q=Oudka" title=" Oudka"> Oudka</a>, <a href="https://publications.waset.org/abstracts/search?q=Taounate" title=" Taounate"> Taounate</a> </p> <a href="https://publications.waset.org/abstracts/107250/landslide-susceptibility-mapping-a-comparison-between-logistic-regression-and-multivariate-adaptive-regression-spline-models-in-the-municipality-of-oudka-northern-of-morocco" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107250.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">188</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5081</span> Comparing Stability Index MAPping (SINMAP) Landslide Susceptibility Models in the Río La Carbonera, Southeast Flank of Pico de Orizaba Volcano, Mexico</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gabriel%20Legorreta%20Paulin">Gabriel Legorreta Paulin</a>, <a href="https://publications.waset.org/abstracts/search?q=Marcus%20I.%20Bursik"> Marcus I. Bursik</a>, <a href="https://publications.waset.org/abstracts/search?q=Lilia%20Arana%20Salinas"> Lilia Arana Salinas</a>, <a href="https://publications.waset.org/abstracts/search?q=Fernando%20Aceves%20Quesada"> Fernando Aceves Quesada</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In volcanic environments, landslides and debris flows occur continually along stream systems of large stratovolcanoes. This is the case on Pico de Orizaba volcano, the highest mountain in Mexico. The volcano has a great potential to impact and damage human settlements and economic activities by landslides. People living along the lower valleys of Pico de Orizaba volcano are in continuous hazard by the coalescence of upstream landslide sediments that increased the destructive power of debris flows. These debris flows not only produce floods, but also cause the loss of lives and property. Although the importance of assessing such process, there is few landslide inventory maps and landslide susceptibility assessment. As a result in México, no landslide susceptibility models assessment has been conducted to evaluate advantage and disadvantage of models. In this study, a comprehensive study of landslide susceptibility models assessment using GIS technology is carried out on the SE flank of Pico de Orizaba volcano. A detailed multi-temporal landslide inventory map in the watershed is used as framework for the quantitative comparison of two landslide susceptibility maps. The maps are created based on 1) the Stability Index MAPping (SINMAP) model by using default geotechnical parameters and 2) by using findings of volcanic soils geotechnical proprieties obtained in the field. SINMAP combines the factor of safety derived from the infinite slope stability model with the theory of a hydrologic model to produce the susceptibility map. It has been claimed that SINMAP analysis is reasonably successful in defining areas that intuitively appear to be susceptible to landsliding in regions with sparse information. The validations of the resulting susceptibility maps are performed by comparing them with the inventory map under LOGISNET system which provides tools to compare by using a histogram and a contingency table. Results of the experiment allow for establishing how the individual models predict the landslide location, advantages, and limitations. The results also show that although the model tends to improve with the use of calibrated field data, the landslide susceptibility map does not perfectly represent existing landslides. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GIS" title="GIS">GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=landslide" title=" landslide"> landslide</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling" title=" modeling"> modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=LOGISNET" title=" LOGISNET"> LOGISNET</a>, <a href="https://publications.waset.org/abstracts/search?q=SINMAP" title=" SINMAP"> SINMAP</a> </p> <a href="https://publications.waset.org/abstracts/62772/comparing-stability-index-mapping-sinmap-landslide-susceptibility-models-in-the-rio-la-carbonera-southeast-flank-of-pico-de-orizaba-volcano-mexico" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62772.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">313</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5080</span> Mapping Iron Content in the Brain with Magnetic Resonance Imaging and Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gabrielle%20Robertson">Gabrielle Robertson</a>, <a href="https://publications.waset.org/abstracts/search?q=Matthew%20Downs"> Matthew Downs</a>, <a href="https://publications.waset.org/abstracts/search?q=Joseph%20Dagher"> Joseph Dagher</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Iron deposition in the brain has been linked with a host of neurological disorders such as Alzheimer’s, Parkinson’s, and Multiple Sclerosis. While some treatment options exist, there are no objective measurement tools that allow for the monitoring of iron levels in the brain in vivo. An emerging Magnetic Resonance Imaging (MRI) method has been recently proposed to deduce iron concentration through quantitative measurement of magnetic susceptibility. This is a multi-step process that involves repeated modeling of physical processes via approximate numerical solutions. For example, the last two steps of this Quantitative Susceptibility Mapping (QSM) method involve I) mapping magnetic field into magnetic susceptibility and II) mapping magnetic susceptibility into iron concentration. Process I involves solving an ill-posed inverse problem by using regularization via injection of prior belief. The end result from Process II highly depends on the model used to describe the molecular content of each voxel (type of iron, water fraction, etc.) Due to these factors, the accuracy and repeatability of QSM have been an active area of research in the MRI and medical imaging community. This work aims to estimate iron concentration in the brain via a single step. A synthetic numerical model of the human head was created by automatically and manually segmenting the human head on a high-resolution grid (640x640x640, 0.4mm³) yielding detailed structures such as microvasculature and subcortical regions as well as bone, soft tissue, Cerebral Spinal Fluid, sinuses, arteries, and eyes. Each segmented region was then assigned tissue properties such as relaxation rates, proton density, electromagnetic tissue properties and iron concentration. These tissue property values were randomly selected from a Probability Distribution Function derived from a thorough literature review. In addition to having unique tissue property values, different synthetic head realizations also possess unique structural geometry created by morphing the boundary regions of different areas within normal physical constraints. This model of the human brain is then used to create synthetic MRI measurements. This is repeated thousands of times, for different head shapes, volume, tissue properties and noise realizations. Collectively, this constitutes a training-set that is similar to in vivo data, but larger than datasets available from clinical measurements. This 3D convolutional U-Net neural network architecture was used to train data-driven Deep Learning models to solve for iron concentrations from raw MRI measurements. The performance was then tested on both synthetic data not used in training as well as real in vivo data. Results showed that the model trained on synthetic MRI measurements is able to directly learn iron concentrations in areas of interest more effectively than other existing QSM reconstruction methods. For comparison, models trained on random geometric shapes (as proposed in the Deep QSM method) are less effective than models trained on realistic synthetic head models. Such an accurate method for the quantitative measurement of iron deposits in the brain would be of important value in clinical studies aiming to understand the role of iron in neurological disease. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20imaging" title="magnetic resonance imaging">magnetic resonance imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=MRI" title=" MRI"> MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=iron%20deposition" title=" iron deposition"> iron deposition</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=quantitative%20susceptibility%20mapping" title=" quantitative susceptibility mapping"> quantitative susceptibility mapping</a> </p> <a href="https://publications.waset.org/abstracts/111451/mapping-iron-content-in-the-brain-with-magnetic-resonance-imaging-and-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/111451.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">137</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5079</span> Building a Parametric Link between Mapping and Planning: A Sunlight-Adaptive Urban Green System Plan Formation Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chenhao%20Zhu">Chenhao Zhu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Quantitative mapping is playing a growing role in guiding urban planning, such as using a heat map created by CFX, CFD2000, or Envi-met, to adjust the master plan. However, there is no effective quantitative link between the mappings and planning formation. So, in many cases, the decision-making is still based on the planner's subjective interpretation and understanding of these mappings, which limits the improvement of scientific and accuracy brought by the quantitative mapping. Therefore, in this paper, an effort has been made to give a methodology of building a parametric link between the mapping and planning formation. A parametric planning process based on radiant mapping has been proposed for creating an urban green system. In the first step, a script is written in Grasshopper to build a road network and form the block, while the Ladybug Plug-in is used to conduct a radiant analysis in the form of mapping. Then, the research creatively transforms the radiant mapping from a polygon into a data point matrix, because polygon is hard to engage in the design formation. Next, another script is created to select the main green spaces from the road network based on the criteria of radiant intensity and connect the green spaces' central points to generate a green corridor. After that, a control parameter is introduced to adjust the corridor's form based on the radiant intensity. Finally, a green system containing greenspace and green corridor is generated under the quantitative control of the data matrix. The designer only needs to modify the control parameter according to the relevant research results and actual conditions to realize the optimization of the green system. This method can also be applied to much other mapping-based analysis, such as wind environment analysis, thermal environment analysis, and even environmental sensitivity analysis. The parameterized link between the mapping and planning will bring about a more accurate, objective, and scientific planning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=parametric%20link" title="parametric link">parametric link</a>, <a href="https://publications.waset.org/abstracts/search?q=mapping" title=" mapping"> mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20green%20system" title=" urban green system"> urban green system</a>, <a href="https://publications.waset.org/abstracts/search?q=radiant%20intensity" title=" radiant intensity"> radiant intensity</a>, <a href="https://publications.waset.org/abstracts/search?q=planning%20strategy" title=" planning strategy"> planning strategy</a>, <a href="https://publications.waset.org/abstracts/search?q=grasshopper" title=" grasshopper"> grasshopper</a> </p> <a href="https://publications.waset.org/abstracts/109538/building-a-parametric-link-between-mapping-and-planning-a-sunlight-adaptive-urban-green-system-plan-formation-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/109538.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">142</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5078</span> Landslide Susceptibility Mapping Using Soft Computing in Amhara Saint</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Semachew%20M.%20Kassa">Semachew M. Kassa</a>, <a href="https://publications.waset.org/abstracts/search?q=Africa%20M%20Geremew"> Africa M Geremew</a>, <a href="https://publications.waset.org/abstracts/search?q=Tezera%20F.%20Azmatch"> Tezera F. Azmatch</a>, <a href="https://publications.waset.org/abstracts/search?q=Nandyala%20Darga%20Kumar"> Nandyala Darga Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Frequency ratio (FR) and analytical hierarchy process (AHP) methods are developed based on past landslide failure points to identify the landslide susceptibility mapping because landslides can seriously harm both the environment and society. However, it is still difficult to select the most efficient method and correctly identify the main driving factors for particular regions. In this study, we used fourteen landslide conditioning factors (LCFs) and five soft computing algorithms, including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Artificial Neural Network (ANN), and Naïve Bayes (NB), to predict the landslide susceptibility at 12.5 m spatial scale. The performance of the RF (F1-score: 0.88, AUC: 0.94), ANN (F1-score: 0.85, AUC: 0.92), and SVM (F1-score: 0.82, AUC: 0.86) methods was significantly better than the LR (F1-score: 0.75, AUC: 0.76) and NB (F1-score: 0.73, AUC: 0.75) method, according to the classification results based on inventory landslide points. The findings also showed that around 35% of the study region was made up of places with high and very high landslide risk (susceptibility greater than 0.5). The very high-risk locations were primarily found in the western and southeastern regions, and all five models showed good agreement and similar geographic distribution patterns in landslide susceptibility. The towns with the highest landslide risk include Amhara Saint Town's western part, the Northern part, and St. Gebreal Church villages, with mean susceptibility values greater than 0.5. However, rainfall, distance to road, and slope were typically among the top leading factors for most villages. The primary contributing factors to landslide vulnerability were slightly varied for the five models. Decision-makers and policy planners can use the information from our study to make informed decisions and establish policies. It also suggests that various places should take different safeguards to reduce or prevent serious damage from landslide events. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=landslide%20susceptibility" title=" landslide susceptibility"> landslide susceptibility</a>, <a href="https://publications.waset.org/abstracts/search?q=na%C3%AFve%20Bayes" title=" naïve Bayes"> naïve Bayes</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/174470/landslide-susceptibility-mapping-using-soft-computing-in-amhara-saint" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/174470.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">82</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5077</span> Application of Machine Learning on Google Earth Engine for Forest Fire Severity, Burned Area Mapping and Land Surface Temperature Analysis: Rajasthan, India</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alisha%20Sinha">Alisha Sinha</a>, <a href="https://publications.waset.org/abstracts/search?q=Laxmi%20Kant%20Sharma"> Laxmi Kant Sharma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forest fires are a recurring issue in many parts of the world, including India. These fires can have various causes, including human activities (such as agricultural burning, campfires, or discarded cigarettes) and natural factors (such as lightning). This study presents a comprehensive and advanced methodology for assessing wildfire susceptibility by integrating diverse environmental variables and leveraging cutting-edge machine learning techniques across Rajasthan, India. The primary goal of the study is to utilize Google Earth Engine to compare locations in Sariska National Park, Rajasthan (India), before and after forest fires. High-resolution satellite data were used to assess the amount and types of changes caused by forest fires. The present study meticulously analyzes various environmental variables, i.e., slope orientation, elevation, normalized difference vegetation index (NDVI), drainage density, precipitation, and temperature, to understand landscape characteristics and assess wildfire susceptibility. In addition, a sophisticated random forest regression model is used to predict land surface temperature based on a set of environmental parameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wildfire%20susceptibility%20mapping" title="wildfire susceptibility mapping">wildfire susceptibility mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=LST" title=" LST"> LST</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=GEE" title=" GEE"> GEE</a>, <a href="https://publications.waset.org/abstracts/search?q=MODIS" title=" MODIS"> MODIS</a>, <a href="https://publications.waset.org/abstracts/search?q=climatic%20parameters" title=" climatic parameters"> climatic parameters</a> </p> <a href="https://publications.waset.org/abstracts/191316/application-of-machine-learning-on-google-earth-engine-for-forest-fire-severity-burned-area-mapping-and-land-surface-temperature-analysis-rajasthan-india" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191316.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">22</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5076</span> Comparison of FASTMAP and B0 Field Map Shimming for 4T MRI</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohan%20L.%20Jayatiake">Mohan L. Jayatiake</a>, <a href="https://publications.waset.org/abstracts/search?q=Judd%20Storrs"> Judd Storrs</a>, <a href="https://publications.waset.org/abstracts/search?q=Jing-Huei%20Lee"> Jing-Huei Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The optimal MRI resolution relies on a homogeneous magnetic field. However, local susceptibility variations can lead to field inhomogeneities that cause artifacts such as image distortion and signal loss. The effects of local susceptibility variation notoriously increase with magnetic field strength. Active shimming improves homogeneity by applying corrective fields generated from shim coils, but requires calculation of optimal current for each shim coil. FASTMAP (fast automatic shimming technique by mapping along projections) is an effective technique for finding optimal currents works well at high-field, but is restricted to shimming spherical regions of interest. The 3D gradient-echo pulse sequence was modified to reduce sensitivity to eddy currents and used to obtain susceptibility field maps at 4T. Measured fields were projected onto first-and second-order spherical harmonic functions corresponding to shim hardware. A spherical phantom was used to calibrate the shim currents. Susceptibility maps of a volunteer’s brain with and without FASTMAP shimming were obtained. Simulations indicate that optimal shim currents derived from the field map may provide better overall shimming of the human brain. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=shimming" title="shimming">shimming</a>, <a href="https://publications.waset.org/abstracts/search?q=high-field" title=" high-field"> high-field</a>, <a href="https://publications.waset.org/abstracts/search?q=active" title=" active"> active</a>, <a href="https://publications.waset.org/abstracts/search?q=passive" title=" passive"> passive</a> </p> <a href="https://publications.waset.org/abstracts/33673/comparison-of-fastmap-and-b0-field-map-shimming-for-4t-mri" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33673.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">510</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5075</span> Exploring the Capabilities of Sentinel-1A and Sentinel-2A Data for Landslide Mapping</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ismayanti%20Magfirah">Ismayanti Magfirah</a>, <a href="https://publications.waset.org/abstracts/search?q=Sartohadi%20Junun"> Sartohadi Junun</a>, <a href="https://publications.waset.org/abstracts/search?q=Samodra%20Guruh"> Samodra Guruh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Landslides are one of the most frequent and devastating natural disasters in Indonesia. Many studies have been conducted regarding this phenomenon. However, there is a lack of attention in the landslide inventory mapping. The natural condition (dense forest area) and the limited human and economic resources are some of the major problems in building landslide inventory in Indonesia. Considering the importance of landslide inventory data in susceptibility, hazard, and risk analysis, it is essential to generate landslide inventory based on available resources. In order to achieve this, the first thing we have to do is identify the landslides' location. The presence of Sentinel-1A and Sentinel-2A data gives new insights into land monitoring investigation. The free access, high spatial resolution, and short revisit time, make the data become one of the most trending open sources data used in landslide mapping. Sentinel-1A and Sentinel-2A data have been used broadly for landslide detection and landuse/landcover mapping. This study aims to generate landslide map by integrating Sentinel-1A and Sentinel-2A data use change detection method. The result will be validated by field investigation to make preliminary landslide inventory in the study area. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=change%20detection%20method" title="change detection method">change detection method</a>, <a href="https://publications.waset.org/abstracts/search?q=landslide%20inventory%20mapping" title=" landslide inventory mapping"> landslide inventory mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=Sentinel-1A" title=" Sentinel-1A"> Sentinel-1A</a>, <a href="https://publications.waset.org/abstracts/search?q=Sentinel-2A" title=" Sentinel-2A"> Sentinel-2A</a> </p> <a href="https://publications.waset.org/abstracts/94105/exploring-the-capabilities-of-sentinel-1a-and-sentinel-2a-data-for-landslide-mapping" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94105.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">171</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5074</span> Different Data-Driven Bivariate Statistical Approaches to Landslide Susceptibility Mapping (Uzundere, Erzurum, Turkey)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Azimollah%20Aleshzadeh">Azimollah Aleshzadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Enver%20Vural%20Yavuz"> Enver Vural Yavuz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main goal of this study is to produce landslide susceptibility maps using different data-driven bivariate statistical approaches; namely, entropy weight method (EWM), evidence belief function (EBF), and information content model (ICM), at Uzundere county, Erzurum province, in the north-eastern part of Turkey. Past landslide occurrences were identified and mapped from an interpretation of high-resolution satellite images, and earlier reports as well as by carrying out field surveys. In total, 42 landslide incidence polygons were mapped using ArcGIS 10.4.1 software and randomly split into a construction dataset 70 % (30 landslide incidences) for building the EWM, EBF, and ICM models and the remaining 30 % (12 landslides incidences) were used for verification purposes. Twelve layers of landslide-predisposing parameters were prepared, including total surface radiation, maximum relief, soil groups, standard curvature, distance to stream/river sites, distance to the road network, surface roughness, land use pattern, engineering geological rock group, topographical elevation, the orientation of slope, and terrain slope gradient. The relationships between the landslide-predisposing parameters and the landslide inventory map were determined using different statistical models (EWM, EBF, and ICM). The model results were validated with landslide incidences, which were not used during the model construction. In addition, receiver operating characteristic curves were applied, and the area under the curve (AUC) was determined for the different susceptibility maps using the success (construction data) and prediction (verification data) rate curves. The results revealed that the AUC for success rates are 0.7055, 0.7221, and 0.7368, while the prediction rates are 0.6811, 0.6997, and 0.7105 for EWM, EBF, and ICM models, respectively. Consequently, landslide susceptibility maps were classified into five susceptibility classes, including very low, low, moderate, high, and very high. Additionally, the portion of construction and verification landslides incidences in high and very high landslide susceptibility classes in each map was determined. The results showed that the EWM, EBF, and ICM models produced satisfactory accuracy. The obtained landslide susceptibility maps may be useful for future natural hazard mitigation studies and planning purposes for environmental protection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=entropy%20weight%20method" title="entropy weight method">entropy weight method</a>, <a href="https://publications.waset.org/abstracts/search?q=evidence%20belief%20function" title=" evidence belief function"> evidence belief function</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20content%20model" title=" information content model"> information content model</a>, <a href="https://publications.waset.org/abstracts/search?q=landslide%20susceptibility%20mapping" title=" landslide susceptibility mapping"> landslide susceptibility mapping</a> </p> <a href="https://publications.waset.org/abstracts/125567/different-data-driven-bivariate-statistical-approaches-to-landslide-susceptibility-mapping-uzundere-erzurum-turkey" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/125567.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">132</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5073</span> Measurement of Susceptibility Users Using Email Phishing Attack</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cindy%20Sahera">Cindy Sahera</a>, <a href="https://publications.waset.org/abstracts/search?q=Sarwono%20Sutikno"> Sarwono Sutikno</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rapid technological developments also have negative impacts, namely the increasing criminal cases based on technology or cybercrime. One technique that can be used to conduct cybercrime attacks are phishing email. The issue is whether the user is aware that email can be misused by others so that it can harm the user's own? This research was conducted to measure the susceptibility of selected targets against email abuse. The objectives of this research are measurement of targets’ susceptibility and find vulnerability in email recipient. There are three steps being taken in this research, (1) the information gathering phase, (2) the design phase, and (3) the execution phase. The first step includes the collection of the information necessary to carry out an attack on a target. The next step is to make the design of an attack against a target. The last step is to send phishing emails to the target. The levels of susceptibility are three: level 1, level 2 and level 3. Level 1 indicates a low level of targets’ susceptibility, level 2 indicates the intermediate level of targets’ susceptibility, and level 3 indicates a high level of targets’ susceptibility. The results showed that users who are on level 1 and level 2 more that level 3, which means the user is not too careless. However, it does not mean the user to be safe. There are still vulnerabilities that may occur, such as automatic location detection when opening emails and automatic downloaded malware as user clicks a link in the email. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cybercrime" title="cybercrime">cybercrime</a>, <a href="https://publications.waset.org/abstracts/search?q=email%20phishing" title=" email phishing"> email phishing</a>, <a href="https://publications.waset.org/abstracts/search?q=susceptibility" title=" susceptibility"> susceptibility</a>, <a href="https://publications.waset.org/abstracts/search?q=vulnerability" title=" vulnerability"> vulnerability</a> </p> <a href="https://publications.waset.org/abstracts/66869/measurement-of-susceptibility-users-using-email-phishing-attack" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66869.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">289</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5072</span> An Alternative Way to Mapping Cone</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yousuf%20Alkhezi">Yousuf Alkhezi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Since most of the literature on algebra does not make much deal with the special case of mapping cone. This paper is an alternative way to examine the special tensor product and mapping cone. Also, we show that the isomorphism that implies the mapping cone commutes with the tensor product for the ordinary tensor product no longer holds for the pinched tensor product. However, we show there is a morphism. We will introduce an alternative way of mapping cone. We are looking for more properties which is our future project. Also, we want to apply these new properties in some application. Many results and examples with classical algorithms will be provided. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=complex" title="complex">complex</a>, <a href="https://publications.waset.org/abstracts/search?q=tensor%20product" title=" tensor product"> tensor product</a>, <a href="https://publications.waset.org/abstracts/search?q=pinched%20tensore%20product" title=" pinched tensore product"> pinched tensore product</a>, <a href="https://publications.waset.org/abstracts/search?q=mapping%20cone" title=" mapping cone"> mapping cone</a> </p> <a href="https://publications.waset.org/abstracts/153677/an-alternative-way-to-mapping-cone" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153677.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">130</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5071</span> Use of Landsat OLI Images in the Mapping of Landslides: Case of the Taounate Province in Northern Morocco</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Benchelha">S. Benchelha</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Chennaoui"> H. Chennaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Hakdaoui"> M. Hakdaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Baidder"> L. Baidder</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Mansouri"> H. Mansouri</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Ejjaaouani"> H. Ejjaaouani</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Benchelha"> T. Benchelha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Northern Morocco is characterized by relatively young mountains experiencing a very important dynamic compared to other areas of Morocco. The dynamics associated with the formation of the Rif chain (Alpine tectonics), is accompanied by instabilities essentially related to tectonic movements. The realization of important infrastructures (Roads, Highways,...) represents a triggering factor and favoring landslides. This paper is part of the establishment of landslides susceptibility map and concerns the mapping of unstable areas in the province of Taounate. The landslide was identified using the components of the false color (FCC) of images Landsat OLI: i) the first independent component (IC1), ii) The main component (PC), iii) Normalized difference index (NDI). This mapping for landslides class is validated by in-situ surveys. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=landslides" title="landslides">landslides</a>, <a href="https://publications.waset.org/abstracts/search?q=False%20Color%20Composite%20%28FCC%29" title=" False Color Composite (FCC)"> False Color Composite (FCC)</a>, <a href="https://publications.waset.org/abstracts/search?q=Independent%20Component%20Analysis%20%28ICA%29" title=" Independent Component Analysis (ICA)"> Independent Component Analysis (ICA)</a>, <a href="https://publications.waset.org/abstracts/search?q=Principal%20Component%20Analysis%20%28PCA%29" title=" Principal Component Analysis (PCA)"> Principal Component Analysis (PCA)</a>, <a href="https://publications.waset.org/abstracts/search?q=Normalized%20Difference%20Index%20%28NDI%29" title=" Normalized Difference Index (NDI)"> Normalized Difference Index (NDI)</a>, <a href="https://publications.waset.org/abstracts/search?q=Normalized%20Difference%20Mid%20Red%20Index%20%28NDMIDR%29" title=" Normalized Difference Mid Red Index (NDMIDR)"> Normalized Difference Mid Red Index (NDMIDR)</a> </p> <a href="https://publications.waset.org/abstracts/73841/use-of-landsat-oli-images-in-the-mapping-of-landslides-case-of-the-taounate-province-in-northern-morocco" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73841.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">291</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5070</span> Application and Verification of Regression Model to Landslide Susceptibility Mapping</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Masood%20Beheshtirad">Masood Beheshtirad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Identification of regions having potential for landslide occurrence is one of the basic measures in natural resources management. Different landslide hazard mapping models are proposed based on the environmental condition and goals. In this research landslide hazard map using multiple regression model were provided and applicability of this model is investigated in Baghdasht watershed. Dependent variable is landslide inventory map and independent variables consist of information layers as Geology, slope, aspect, distance from river, distance from road, fault and land use. For doing this, existing landslides have been identified and an inventory map made. The landslide hazard map is based on the multiple regression provided. The level of similarity potential hazard classes and figures of this model were compared with the landslide inventory map in the SPSS environments. Results of research showed that there is a significant correlation between the potential hazard classes and figures with area of the landslides. The multiple regression model is suitable for application in the Baghdasht Watershed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=landslide" title="landslide">landslide</a>, <a href="https://publications.waset.org/abstracts/search?q=mapping" title=" mapping"> mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20model" title=" multiple model"> multiple model</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a> </p> <a href="https://publications.waset.org/abstracts/25864/application-and-verification-of-regression-model-to-landslide-susceptibility-mapping" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25864.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">325</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5069</span> Heuristic for Accelerating Run-Time Task Mapping in NoC-Based Heterogeneous MPSoCs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20K.%20Benhaoua">M. K. Benhaoua</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20K.%20Singh"> A. K. Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20E.%20H.%20Benyamina"> A. E. H. Benyamina</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Kumar"> A. Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Boulet"> P. Boulet</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a new packing strategy to find free resources for run-time mapping of application tasks on NoC-based Heterogeneous MPSoCs. The proposed strategy minimizes the task mapping time in addition to placing the communicating tasks close to each other. To evaluate our approach, a comparative study is carried out. Experiments show that our strategy provides better results when compared to latest dynamic mapping strategies reported in the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20MPSoCs" title="heterogeneous MPSoCs">heterogeneous MPSoCs</a>, <a href="https://publications.waset.org/abstracts/search?q=NoC" title=" NoC"> NoC</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20mapping" title=" dynamic mapping"> dynamic mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=routing" title=" routing"> routing</a> </p> <a href="https://publications.waset.org/abstracts/7972/heuristic-for-accelerating-run-time-task-mapping-in-noc-based-heterogeneous-mpsocs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7972.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">527</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5068</span> Correlation Results Based on Magnetic Susceptibility Measurements by in-situ and Ex-Situ Measurements as Indicators of Environmental Changes Due to the Fertilizer Industry</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nurin%20Amalina%20Widityani">Nurin Amalina Widityani</a>, <a href="https://publications.waset.org/abstracts/search?q=Adinda%20Syifa%20%20Azhari"> Adinda Syifa Azhari</a>, <a href="https://publications.waset.org/abstracts/search?q=Twin%20Aji%20Kusumagiani"> Twin Aji Kusumagiani</a>, <a href="https://publications.waset.org/abstracts/search?q=Eleonora%20%20Agustine"> Eleonora Agustine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fertilizer industry activities contribute to environmental changes. Changes to the environment became one of a few problems in this era of globalization. Parameters that can be seen as criteria to identify changes in the environment can be seen from the aspects of physics, chemistry, and biology. One aspect that can be assessed quickly and efficiently to describe environmental change is the aspect of physics, one of which is the value of magnetic susceptibility (χ). The rock magnetism method can be used as a proxy indicator of environmental changes, seen from the value of magnetic susceptibility. The rock magnetism method is based on magnetic susceptibility studies to measure and classify the degree of pollutant elements that cause changes in the environment. This research was conducted in the area around the fertilizer plant, with five coring points on each track, each coring point a depth of 15 cm. Magnetic susceptibility measurements were performed by in-situ and ex-situ. In-situ measurements were carried out directly by using the SM30 tool by putting the tools on the soil surface at each measurement point and by that obtaining the value of the magnetic susceptibility. Meanwhile, ex-situ measurements are performed in the laboratory by using the Bartington MS2B tool’s susceptibility, which is done on a coring sample which is taken every 5 cm. In-situ measurement shows results that the value of magnetic susceptibility at the surface varies, with the lowest score on the second and fifth points with the -0.81 value and the highest value at the third point, with the score of 0,345. Ex-situ measurements can find out the variations of magnetic susceptibility values at each depth point of coring. At a depth of 0-5 cm, the value of the highest XLF = 494.8 (x10-8m³/kg) is at the third point, while the value of the lowest XLF = 187.1 (x10-8m³/kg) at first. At a depth of 6-10 cm, the highest value of the XLF was at the second point, which was 832.7 (x10-8m³/kg) while the lowest XLF is at the first point, at 211 (x10-8m³/kg). At a depth of 11-15 cm, the XLF’s highest value = 857.7 (x10-8m³/kg) is at the second point, whereas the value of the lowest XLF = 83.3 (x10-8m³/kg) is at the fifth point. Based on the in situ and exsit measurements, it can be seen that the highest magnetic susceptibility values from the surface samples are at the third point. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=magnetic%20susceptibility" title="magnetic susceptibility">magnetic susceptibility</a>, <a href="https://publications.waset.org/abstracts/search?q=fertilizer%20plant" title=" fertilizer plant"> fertilizer plant</a>, <a href="https://publications.waset.org/abstracts/search?q=Bartington%20MS2B" title=" Bartington MS2B"> Bartington MS2B</a>, <a href="https://publications.waset.org/abstracts/search?q=SM30" title=" SM30"> SM30</a> </p> <a href="https://publications.waset.org/abstracts/65609/correlation-results-based-on-magnetic-susceptibility-measurements-by-in-situ-and-ex-situ-measurements-as-indicators-of-environmental-changes-due-to-the-fertilizer-industry" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65609.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">342</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5067</span> Magnetic Susceptibility Measurements of Urban Areas in Denizli City and Showing the Distributions of Heavy Metal Pollution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Aydin">Ali Aydin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Three hundred and fifty soil samples were collected around the urban and residential area, for the purpose of a magnetic susceptibility study on pollution in Denizli City, Turkiye. Measurements of volume-specific magnetic susceptibility (к) and mass-specific magnetic susceptibility (χ) show a significant variation range from place to place collected soil samples. In this study, we did a primary magnetic study near the high heavy traffic pollution in a part of Denizli city, Turkiye which was said the most polluted city in Aegean Region of Turkey. The magnetic susceptibility measurements increased from the garden area to residential area and reached the high levels near the industrial areas of the city. Magnetic particle concentration and grain size sourced exhaust gasses, and other pollution sources increase with the increasing distance from a residential area, indicating the high traffic road area. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=magnetic%20susceptibility" title="magnetic susceptibility">magnetic susceptibility</a>, <a href="https://publications.waset.org/abstracts/search?q=pollution" title=" pollution"> pollution</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20particle" title=" magnetic particle"> magnetic particle</a>, <a href="https://publications.waset.org/abstracts/search?q=Denizli" title=" Denizli"> Denizli</a> </p> <a href="https://publications.waset.org/abstracts/57092/magnetic-susceptibility-measurements-of-urban-areas-in-denizli-city-and-showing-the-distributions-of-heavy-metal-pollution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57092.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">294</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5066</span> Instability Index Method and Logistic Regression to Assess Landslide Susceptibility in County Route 89, Taiwan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Y.%20H.%20Wu">Y. H. Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Ji-Yuan%20Lin"> Ji-Yuan Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu-Ming%20Liou"> Yu-Ming Liou </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study aims to set up the landslide susceptibility map of County Route 89 at Ren-Ai Township in Nantou County using the Instability Index Method and Logistic regression. Seven susceptibility factors including Slope Angle, Aspect, Elevation, Distance to fold, Distance to River, Distance to Road and Accumulated Rainfall were obtained by GIS based on the Typhoon Toraji landslide area identified by Industrial Technology Research Institute in 2001. To calculate the landslide percentage of each factor and acquire the weight and grade the grid by means of Instability Index Method. In this study, landslide susceptibility can be classified into four grades: high, medium high, medium low and low, in order to determine the advantages and disadvantages of the two models. The precision of this model is verified by classification error matrix and SRC curve. These results suggest that the logistic regression model is a preferred method than instability index in the assessment of landslide susceptibility. It is suitable for the landslide prediction and precaution in this area in the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=instability%20index%20method" title="instability index method">instability index method</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=landslide%20susceptibility" title=" landslide susceptibility"> landslide susceptibility</a>, <a href="https://publications.waset.org/abstracts/search?q=SRC%20curve" title=" SRC curve"> SRC curve</a> </p> <a href="https://publications.waset.org/abstracts/46070/instability-index-method-and-logistic-regression-to-assess-landslide-susceptibility-in-county-route-89-taiwan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46070.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">292</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5065</span> Dynamic Communications Mapping in NoC-Based Heterogeneous MPSoCs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20K.%20Benhaoua">M. K. Benhaoua</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20K.%20Singh"> A. K. Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20E.%20H.%20Benyamina"> A. E. H. Benyamina</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose heuristic for dynamic communications mapping that considers the placement of communications in order to optimize the overall performance. The mapping technique uses a newly proposed Algorithm to place communications between the tasks. The placement we propose of the communications leads to a better optimization of several performance metrics (time and energy consumption). Experimental results show that the proposed mapping approach provides significant performance improvements when compared to those using static routing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Multi-Processor%20Systems-on-Chip%20%28MPSoCs%29" title="Multi-Processor Systems-on-Chip (MPSoCs)">Multi-Processor Systems-on-Chip (MPSoCs)</a>, <a href="https://publications.waset.org/abstracts/search?q=Network-on-Chip%20%28NoC%29" title=" Network-on-Chip (NoC)"> Network-on-Chip (NoC)</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20architectures" title=" heterogeneous architectures"> heterogeneous architectures</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20mapping%20heuristics" title=" dynamic mapping heuristics"> dynamic mapping heuristics</a> </p> <a href="https://publications.waset.org/abstracts/22269/dynamic-communications-mapping-in-noc-based-heterogeneous-mpsocs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22269.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">533</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5064</span> Flood Susceptibility Assessment of Mandaluyong City Using Analytic Hierarchy Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Keigh%20D.%20Guinto">Keigh D. Guinto</a>, <a href="https://publications.waset.org/abstracts/search?q=Ma.%20Romina%20M.%20Santos"> Ma. Romina M. Santos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the most catastrophic natural disasters in the Philippines is floods. Twelve (12) million people reside in Metro Manila, National Capital Region (NCR), prone to flooding. A flood can cause widespread devastation resulting in damaged properties and infrastructures and loss of life. By using the analytical hierarchy process, six (6) parameters were selected, namely elevation, slope, lithology, distance from the river, river network density, and flow accumulation. Ranking of these parameters demonstrates that distance from the river with 25.31% and river density with 17.30% ranked the highest causative factor to flooding. This is followed by flow accumulation with 16.72%, elevation with 15.33%, slope with 13.53%, and the least flood causative factor is lithology with 11.8%. The generated flood susceptibility map of Mandaluyong has three (3) classes: high susceptibility, moderate susceptibility, and low susceptibility. The flood susceptibility map generated in this study can be used as an aid for planning flood mitigation, land use planning, and general public awareness. This study can also be used for emergency management and can be applied in the disaster risk management of Mandaluyong. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analytical%20hierarchy%20process" title="analytical hierarchy process">analytical hierarchy process</a>, <a href="https://publications.waset.org/abstracts/search?q=assessment" title=" assessment"> assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=flood" title=" flood"> flood</a>, <a href="https://publications.waset.org/abstracts/search?q=geographic%20information%20system" title=" geographic information system"> geographic information system</a> </p> <a href="https://publications.waset.org/abstracts/147575/flood-susceptibility-assessment-of-mandaluyong-city-using-analytic-hierarchy-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147575.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">200</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5063</span> Quantitative Trait Loci Analysis in Multiple Sorghum Mapping Populations Facilitates the Dissection of Genetic Control of Drought Tolerance Related Traits in Sorghum [Sorghum bicolor (Moench)]</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Techale%20B.">Techale B.</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongxu%20Dong"> Hongxu Dong</a>, <a href="https://publications.waset.org/abstracts/search?q=Mihrete%20Getinet"> Mihrete Getinet</a>, <a href="https://publications.waset.org/abstracts/search?q=Aregash%20Gabizew"> Aregash Gabizew</a>, <a href="https://publications.waset.org/abstracts/search?q=Andrew%20H.%20Paterson"> Andrew H. Paterson</a>, <a href="https://publications.waset.org/abstracts/search?q=Kassahun%20Bantte"> Kassahun Bantte</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The genetic architecture of drought tolerance is expected to involve multiple loci that are unlikely to all segregate for alternative alleles in a single bi-parental population. Therefore, the identification of quantitative trait loci (QTL) that are expressed in diverse genetic backgrounds of multiple bi-parental populations provides evidence about both background-specific and common genetic variants. The purpose of this study was to map QTL related to drought tolerance using three connected mapping populations of different genetic backgrounds to gain insight into the genomic landscape of this important trait in elite Ethiopian germplasm. The three bi-parental populations, each with 207 F₂:₃ lines, were evaluated using an alpha lattice design with two replications under two moisture stress environments. Drought tolerance related traits were analyzed separately for each population using composite interval mapping, finding a total of 105 QTLs. All the QTLs identified from individual populations were projected on a combined consensus map, comprising a total of 25 meta QTLs for seven traits. The consensus map allowed us to deduce locations of a larger number of markers than possible in any individual map, providing a reference for genetic studies in different genetic backgrounds. The mQTL identified in this study could be used for marker-assisted breeding programs in sorghum after validation. Only one trait, reduced leaf senescence, showed a striking bias of allele distribution, indicating substantial standing variation among present varieties that might be employed in improving drought tolerance of Ethiopian and other sorghums. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Drought%20tolerance" title="Drought tolerance ">Drought tolerance </a>, <a href="https://publications.waset.org/abstracts/search?q=Mapping%20populations" title=" Mapping populations"> Mapping populations</a>, <a href="https://publications.waset.org/abstracts/search?q=Meta%20QTL" title=" Meta QTL"> Meta QTL</a>, <a href="https://publications.waset.org/abstracts/search?q=QTL%20mapping" title=" QTL mapping"> QTL mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=Sorghum" title=" Sorghum"> Sorghum</a> </p> <a href="https://publications.waset.org/abstracts/130505/quantitative-trait-loci-analysis-in-multiple-sorghum-mapping-populations-facilitates-the-dissection-of-genetic-control-of-drought-tolerance-related-traits-in-sorghum-sorghum-bicolor-moench" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130505.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">180</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5062</span> Antifungal Susceptibility of Yeasts Isolated from Clinical Samples from a Tertiary Hospital from State of Puebla</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ricardo%20Munguia-Perez">Ricardo Munguia-Perez</a>, <a href="https://publications.waset.org/abstracts/search?q=Nayeli%20Remigio-Alvarado"> Nayeli Remigio-Alvarado</a>, <a href="https://publications.waset.org/abstracts/search?q=M.Miriam%20Hernandez-Arroyo"> M.Miriam Hernandez-Arroyo</a>, <a href="https://publications.waset.org/abstracts/search?q=Elsa%20Casta%C3%B1eda-Roldan"> Elsa Castañeda-Roldan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fungi have emerged as important pathogens causing morbidity and mortality mainly in immunosuppressed, malnourished and elderly patients. It has detected an increase in resistance to azoles primarily to fluconazol. The fungal infections have become a problem of public health for the resistance to antifungal agents, they have developed new antifungals with broad-spectrum. The aim of this study was determine the antifungal susceptibility of yeasts isolated from clinical samples (respiratory secretions, exudates, wounds, blood cultures, urine cultures) obtained from inpatients and outpatients of a tertiary hospital from State of Puebla. The antifungal susceptibility of the yeast from several clinical samples were determined by the CLS M44-A disk diffusion methods. 149 samples of yeast were analyzed. All species were 100% susceptible to nystatin and amphotericin B. Candida albicans showed resistance of 95.5 % to fluconazole, 50.7 % to 5-flurocytosine and 55.2 % intermediate susceptibility to ketoconazole. Candida glabrata 81.3 % was susceptibility to ketoconazole and 75 % to fluconazole, for the case of 5-flurocytosine the 56.3 % was susceptible. Candida krusei 100 % was susceptible to ketoconazole, 50 % to fluconazole and 37.5 % to 5-flurocytosine. The internal medicine have greater diversity of yeast, the samples have susceptibility of 64.7% to ketoconazole, 47.1 % to fluconazole and 27.5 % to 5-flurocytosine. Hospitalized patients are more resistant to fluconazole and nystatin, but in the case of outpatients presents resistance to ketoconazole. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=antifungal" title="antifungal">antifungal</a>, <a href="https://publications.waset.org/abstracts/search?q=susceptibility" title=" susceptibility"> susceptibility</a>, <a href="https://publications.waset.org/abstracts/search?q=yeast" title=" yeast"> yeast</a>, <a href="https://publications.waset.org/abstracts/search?q=clinical%20samples" title=" clinical samples"> clinical samples</a> </p> <a href="https://publications.waset.org/abstracts/59752/antifungal-susceptibility-of-yeasts-isolated-from-clinical-samples-from-a-tertiary-hospital-from-state-of-puebla" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59752.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">338</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5061</span> Measurements of Environmental Pollution in Chemical Fertilizer Industrial Area Using Magnetic Susceptibility Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramadhani%20Yasyfi%20Cysela">Ramadhani Yasyfi Cysela</a>, <a href="https://publications.waset.org/abstracts/search?q=Adinda%20Syifa%20Azhari"> Adinda Syifa Azhari</a>, <a href="https://publications.waset.org/abstracts/search?q=Eleonora%20Agustine"> Eleonora Agustine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The World Health Organization (WHO) estimates that about a quarter of the diseases facing mankind today occur due to environmental pollution. The soil is a part of environment that have a widespread problem. The contaminated soil should no longer be used to grow food because the chemicals can leech into the food and harm people who eat it. The chemical fertilizer industry gives specific effect due to soil pollution. To determine ammonia and urea emissions from fertilizer industry, we can use physical characteristic of soil, which is magnetic susceptibility. Rock magnetism is used as a proxy indicator to determine changes in physical properties. Magnetic susceptibilities of samples in low and high frequency have been measured by Bartington MS2B magnetic susceptibility measurement device. The sample was taken from different area which located closer by pollution source and far from the pollution source. The susceptibility values of polluted samples in topsoil were quite low, with range from 187.1- 494.8 [x 10-8 m3 kg-1] when free polluted area’s sample has high values (1188.7- 2237.8 [x 10-8 m3 kg-1 ]). From this studies shows that susceptibility values in areas of the fertilizer industry are lower than the free polluted area. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=environmental" title="environmental">environmental</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20susceptibility" title=" magnetic susceptibility"> magnetic susceptibility</a>, <a href="https://publications.waset.org/abstracts/search?q=rock%20magnetism" title=" rock magnetism"> rock magnetism</a>, <a href="https://publications.waset.org/abstracts/search?q=soil%20pollution" title=" soil pollution"> soil pollution</a> </p> <a href="https://publications.waset.org/abstracts/65586/measurements-of-environmental-pollution-in-chemical-fertilizer-industrial-area-using-magnetic-susceptibility-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65586.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">357</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5060</span> Analysis of Pollution Caused by the Animal Feed Industry and the Fertilizer Industry Using Rock Magnetic Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kharina%20Budiman">Kharina Budiman</a>, <a href="https://publications.waset.org/abstracts/search?q=Adinda%20Syifa%20Azhari"> Adinda Syifa Azhari</a>, <a href="https://publications.waset.org/abstracts/search?q=Eleonora%20Agustine"> Eleonora Agustine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Industrial activities get increase in this globalization era, one of the major impacts of industrial activities is a problem to the environment. This can happen because at the industrial production term will bring out pollutant in the shape of solid, liquid or gas. Normally this pollutant came from some dangerous materials for environment. However not every industry produces the same amount of pollutant, every industry produces different kind of pollution. To compare the pollution impact of industrial activities, soil sample has been taken around the animal feed industry and the fertilizer industry. This study applied the rock magnetic method and used Bartington MS2B to measured magnetic susceptibility (χ) as the physical parameter. This study tested soil samples using the value of susceptibility low frequency (χ lf) and Frequency Dependent (χ FD). Samples only taken in the soil surface with 0-5 cm depth and sampling interval was 20 cm. The animal feed factory has susceptibility low frequency (χ lf) = 111,9 – 325,7 and Frequency Dependent (χ FD) = 0,8 – 3,57 %. And the fertilizer factory has susceptibility low frequency (χ lf) = 187,1 – 494,8 and Frequency Dependent (χ FD) = 1,37 – 2,46 %. Based on the results, the highest value of susceptibility low frequency (χ lf) is the fertilizer factory, but the highest value of Frequency Dependent (FD) is the animal feed factory. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=industrial" title="industrial">industrial</a>, <a href="https://publications.waset.org/abstracts/search?q=pollution" title=" pollution"> pollution</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20susceptibility" title=" magnetic susceptibility"> magnetic susceptibility</a>, <a href="https://publications.waset.org/abstracts/search?q=%CF%87lf" title=" χlf"> χlf</a>, <a href="https://publications.waset.org/abstracts/search?q=%CF%87fd" title=" χfd"> χfd</a>, <a href="https://publications.waset.org/abstracts/search?q=animal%20feed%20industry%20and%20fertilizer%20industry" title=" animal feed industry and fertilizer industry"> animal feed industry and fertilizer industry</a> </p> <a href="https://publications.waset.org/abstracts/65665/analysis-of-pollution-caused-by-the-animal-feed-industry-and-the-fertilizer-industry-using-rock-magnetic-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65665.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">403</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5059</span> Determination of Klebsiella Pneumoniae Susceptibility to Antibiotics Using Infrared Spectroscopy and Machine Learning Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manal%20Suleiman">Manal Suleiman</a>, <a href="https://publications.waset.org/abstracts/search?q=George%20Abu-Aqil"> George Abu-Aqil</a>, <a href="https://publications.waset.org/abstracts/search?q=Uraib%20Sharaha"> Uraib Sharaha</a>, <a href="https://publications.waset.org/abstracts/search?q=Klaris%20Riesenberg"> Klaris Riesenberg</a>, <a href="https://publications.waset.org/abstracts/search?q=Itshak%20Lapidot"> Itshak Lapidot</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Salman"> Ahmad Salman</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20Huleihel"> Mahmoud Huleihel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Klebsiella pneumoniae is one of the most aggressive multidrug-resistant bacteria associated with human infections resulting in high mortality and morbidity. Thus, for an effective treatment, it is important to diagnose both the species of infecting bacteria and their susceptibility to antibiotics. Current used methods for diagnosing the bacterial susceptibility to antibiotics are time-consuming (about 24h following the first culture). Thus, there is a clear need for rapid methods to determine the bacterial susceptibility to antibiotics. Infrared spectroscopy is a well-known method that is known as sensitive and simple which is able to detect minor biomolecular changes in biological samples associated with developing abnormalities. The main goal of this study is to evaluate the potential of infrared spectroscopy in tandem with Random Forest and XGBoost machine learning algorithms to diagnose the susceptibility of Klebsiella pneumoniae to antibiotics within approximately 20 minutes following the first culture. In this study, 1190 Klebsiella pneumoniae isolates were obtained from different patients with urinary tract infections. The isolates were measured by the infrared spectrometer, and the spectra were analyzed by machine learning algorithms Random Forest and XGBoost to determine their susceptibility regarding nine specific antibiotics. Our results confirm that it was possible to classify the isolates into sensitive and resistant to specific antibiotics with a success rate range of 80%-85% for the different tested antibiotics. These results prove the promising potential of infrared spectroscopy as a powerful diagnostic method for determining the Klebsiella pneumoniae susceptibility to antibiotics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=urinary%20tract%20infection%20%28UTI%29" title="urinary tract infection (UTI)">urinary tract infection (UTI)</a>, <a href="https://publications.waset.org/abstracts/search?q=Klebsiella%20pneumoniae" title=" Klebsiella pneumoniae"> Klebsiella pneumoniae</a>, <a href="https://publications.waset.org/abstracts/search?q=bacterial%20susceptibility" title=" bacterial susceptibility"> bacterial susceptibility</a>, <a href="https://publications.waset.org/abstracts/search?q=infrared%20spectroscopy" title=" infrared spectroscopy"> infrared spectroscopy</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/145001/determination-of-klebsiella-pneumoniae-susceptibility-to-antibiotics-using-infrared-spectroscopy-and-machine-learning-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145001.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">168</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5058</span> Analysis of Magnetic Anomaly Data for Identification Structure in Subsurface of Geothermal Manifestation at Candi Umbul Area, Magelang, Central Java Province, Indonesia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20A.%20Kharisa">N. A. Kharisa</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20Wulandari"> I. Wulandari</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Narendratama"> R. Narendratama</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20I.%20Faisal"> M. I. Faisal</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Kirana"> K. Kirana</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Zipora"> R. Zipora</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20Arfiansah"> I. Arfiansah</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20Suyanto"> I. Suyanto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Acquisition of geophysical survey with magnetic method has been done in manifestation of geothermalat Candi Umbul, Grabag, Magelang, Central Java Province on 10-12 May 2013. This objective research is interpretation to interpret structural geology that control geothermal system in CandiUmbul area. The research has been finished with area size 1,5 km x 2 km and measurement space of 150 m. And each point of line space survey is 150 m using PPM Geometrics model G-856. Data processing was started with IGRF and diurnal variation correction to get total magnetic field anomaly. Then, advance processing was done until reduction to pole, upward continuation, and residual anomaly. That results become next interpretation in qualitative step. It is known that the biggest object position causes low anomaly located in central of area survey that comes from hot spring manifestation and demagnetization zone that indicates the existence of heat source activity. Then, modeling the anomaly map was used for quantitative interpretation step. The result of modeling is rock layers and geological structure model that can inform about the geothermal system. And further information from quantitative interpretations can be interpreted about lithology susceptibility. And lithology susceptibilities are andesiteas heat source has susceptibility value of (k= 0.00014 emu), basaltic as alteration rock (k= 0.0016 emu), volcanic breccia as reservoir rock (k= 0.0026 emu), andesite porfirtic as cap rock (k= 0.004 emu), lava andesite (k= 0.003 emu), and alluvium (k= 0.0007 emu). The hot spring manifestation is controlled by the normal fault which becomes a weak zone, easily passed by hot water which comes from the geothermal reservoir. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=geological%20structure" title="geological structure">geological structure</a>, <a href="https://publications.waset.org/abstracts/search?q=geothermal%20system" title=" geothermal system"> geothermal system</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic" title=" magnetic"> magnetic</a>, <a href="https://publications.waset.org/abstracts/search?q=susceptibility" title=" susceptibility"> susceptibility</a> </p> <a href="https://publications.waset.org/abstracts/19430/analysis-of-magnetic-anomaly-data-for-identification-structure-in-subsurface-of-geothermal-manifestation-at-candi-umbul-area-magelang-central-java-province-indonesia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19430.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">384</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5057</span> Effect of Pressure and Dissolved Oxygen on Stress Corrosion Cracking Susceptibility of Inconel 617 in Steam and Supercritical Water</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hasan%20Izhar%20Khan">Hasan Izhar Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Naiqiang%20Zhang"> Naiqiang Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hong%20Xu"> Hong Xu</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhongliang%20Zhu"> Zhongliang Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Dongfang%20Jiang"> Dongfang Jiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Inconel 617, a nickel-based alloy designed for high-temperature applications, got an excellent amalgamation of strength and oxidation resistance at high temperatures. For a better understanding of its suitability to be used in superheater and reheater tubes in ultra-supercritical power plants, stress corrosion cracking (SCC) susceptibility must be evaluated. In the present study, the effect of medium environment on SCC behavior of Inconel 617, in the form of a round bar tensile specimen, was tested via slow strain rate tensile tests in steam and supercritical water (SCW) at 650 °C. The results showed that SCC susceptibility has a linear relationship with exposed pressure and increases monotonically with an increase in pressure. A severe SCC susceptibility was observed in SCW followed by that in a steam environment. Fracture and gage surface showed apparent characteristics of brittle fracture. Intergranular cracks initiated from the edge region and propagated into the matrix through cross section until ductile rupture. When dissolved oxygen contents were decreased in SCW environment, it showed no noticeable effect on mechanical properties but SCC susceptibility slightly decreased. The research revealed the influence of environment on SCC susceptibility of Inconel 617 in steam and SCW. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Inconel%20617" title="Inconel 617">Inconel 617</a>, <a href="https://publications.waset.org/abstracts/search?q=steam" title=" steam"> steam</a>, <a href="https://publications.waset.org/abstracts/search?q=supercritical%20water" title=" supercritical water"> supercritical water</a>, <a href="https://publications.waset.org/abstracts/search?q=stress%20corrosion%20cracking" title=" stress corrosion cracking"> stress corrosion cracking</a> </p> <a href="https://publications.waset.org/abstracts/95171/effect-of-pressure-and-dissolved-oxygen-on-stress-corrosion-cracking-susceptibility-of-inconel-617-in-steam-and-supercritical-water" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95171.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">156</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5056</span> Mapping Method to Solve a Nonlinear Schrodinger Type Equation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Edamana%20Vasudevan%20Krishnan">Edamana Vasudevan Krishnan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper studies solitons in optical materials with the help of Mapping Method. Two types of nonlinear media have been investigated, namely, the cubic nonlinearity and the quintic nonlinearity. The soliton solutions, shock wave solutions and singular solutions have been derives with certain constraint conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=solitons" title="solitons">solitons</a>, <a href="https://publications.waset.org/abstracts/search?q=integrability" title=" integrability"> integrability</a>, <a href="https://publications.waset.org/abstracts/search?q=metamaterials" title=" metamaterials"> metamaterials</a>, <a href="https://publications.waset.org/abstracts/search?q=mapping%20method" title=" mapping method"> mapping method</a> </p> <a href="https://publications.waset.org/abstracts/32851/mapping-method-to-solve-a-nonlinear-schrodinger-type-equation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32851.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">494</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5055</span> Analysis of the Aquifer Vulnerability of a Miopliocene Arid Area Using Drastic and SI Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Majour">H. Majour</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Djabri"> L. Djabri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many methods in the groundwater vulnerability have been developed in the world (methods like PRAST, DRIST, APRON/ARAA, PRASTCHIM, GOD). In this study, our choice dealt with two recent complementary methods using category mapping of index with weighting criteria (Point County Systems Model MSCP) namely the standard DRASTIC method and SI (Susceptibility Index). At present, these two methods are the most used for the mapping of the intrinsic vulnerability of groundwater. Two classes of groundwater vulnerability in the Biskra sandy aquifer were identified by the DRASTIC method (average and high) and the SI method (very high and high). Integrated analysis has revealed that the high class is predominant for the DRASTIC method whereas for that of SI the preponderance is for the very high class. Furthermore, we notice that the method SI estimates better the vulnerability for the pollution in nitrates, with a rate of 85 % between the concentrations in nitrates of groundwater and the various established classes of vulnerability, against 75 % for the DRASTIC method. By including the land use parameter, the SI method produced more realistic results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DRASTIC" title="DRASTIC">DRASTIC</a>, <a href="https://publications.waset.org/abstracts/search?q=SI" title=" SI"> SI</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS" title=" GIS"> GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=Biskra%20sandy%20aquifer" title=" Biskra sandy aquifer"> Biskra sandy aquifer</a>, <a href="https://publications.waset.org/abstracts/search?q=Algeria" title=" Algeria"> Algeria</a> </p> <a href="https://publications.waset.org/abstracts/24830/analysis-of-the-aquifer-vulnerability-of-a-miopliocene-arid-area-using-drastic-and-si-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24830.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">488</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5054</span> Presenting a Knowledge Mapping Model According to a Comparative Study on Applied Models and Approaches to Map Organizational Knowledge</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Aslizadeh">Ahmad Aslizadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Farid%20Ghaderi"> Farid Ghaderi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mapping organizational knowledge is an innovative concept and useful instrument of representation, capturing and visualization of implicit and explicit knowledge. There are a diversity of methods, instruments and techniques presented by different researchers following mapping organizational knowledge to reach determined goals. Implicating of these methods, it is necessary to know their exigencies and conditions in which those can be used. Integrating identified methods of knowledge mapping and comparing them would help knowledge managers to select the appropriate methods. This research conducted to presenting a model and framework to map organizational knowledge. At first, knowledge maps, their applications and necessity are introduced because of extracting comparative framework and detection of their structure. At the next step techniques of researchers such as Eppler, Kim, Egbu, Tandukar and Ebner as knowledge mapping models are presented and surveyed. Finally, they compare and a superior model would be introduced. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=knowledge%20mapping" title="knowledge mapping">knowledge mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management" title=" knowledge management"> knowledge management</a>, <a href="https://publications.waset.org/abstracts/search?q=comparative%20study" title=" comparative study"> comparative study</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20and%20management" title=" business and management"> business and management</a> </p> <a href="https://publications.waset.org/abstracts/29967/presenting-a-knowledge-mapping-model-according-to-a-comparative-study-on-applied-models-and-approaches-to-map-organizational-knowledge" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29967.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">403</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5053</span> Buzan Mind Mapping: An Efficient Technique for Note-Taking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=T.%20K.%20Tee">T. K. Tee</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20N.%20A.%20Azman"> M. N. A. Azman</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Mohamed"> S. Mohamed</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Muhammad"> M. Muhammad</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20M.%20Mohamad"> M. M. Mohamad</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Md%20Yunos"> J. Md Yunos</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20H.%20Yee"> M. H. Yee</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20Othman"> W. Othman </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Buzan mind mapping is an efficient system of note-taking that makes revision a fun thing to do for students. Tony Buzan has been teaching children all over the world for the past thirty years and has proved that mind maps are the magic formula in the classroom for everyone. The purpose of this paper is to discuss the importance of Buzan mind mapping as a note-taking technique for the secondary school students. This paper also examines the mind mapping technique, advantages and disadvantages of hand-drawn mind maps. Samples of students’ mind maps were presented and discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Buzan%20mind%20mapping" title="Buzan mind mapping">Buzan mind mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=note-taking%20technique" title=" note-taking technique"> note-taking technique</a>, <a href="https://publications.waset.org/abstracts/search?q=hand-drawn" title=" hand-drawn"> hand-drawn</a>, <a href="https://publications.waset.org/abstracts/search?q=mind%20maps" title=" mind maps "> mind maps </a> </p> <a href="https://publications.waset.org/abstracts/4240/buzan-mind-mapping-an-efficient-technique-for-note-taking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4240.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">538</span> 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