Search results for: settlement prediction

Authors: Sol Girouard, Zona Kostic

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A home is often the largest and most expensive purchase a person makes. Whether the decision leads to a successful outcome will be determined by a combination of critical factors. In this paper, we propose a method that efficiently handles all the factors in residential real estate and performs predictions given a feature space with high dimensionality while controlling for overfitting. The proposed method was built on gradient descent and boosting algorithms and uses a mixed optimizing technique to improve the prediction power. Usually, a single model cannot handle all the cases thus our approach builds multiple models based on different subsets of the predictors. The algorithm was tested on 3 million homes across the U.S., and the experimental results demonstrate the efficiency of this approach by outperforming techniques currently used in forecasting prices. With everyday changes on the real estate market, our proposed algorithm capitalizes from new events allowing more efficient predictions.

Keywords: real estate prediction, gradient descent, boosting, ensemble methods, active learning, training

Procedia PDF Downloads 247

Authors: V. G. Rifert, V. V. Gorin, V. V. Sereda, V. V. Treputnev

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The analysis of heat transfer design methods in condensing inside plain tubes under existing influence of shear stress is presented in this paper. The existing discrepancy in more than 30-50% between rating heat transfer coefficients and experimental data has been noted. The analysis of existing theoretical and semi-empirical methods of heat transfer prediction is given. The influence of a precise definition concerning boundaries of phase flow (it is especially important in condensing inside horizontal tubes), shear stress (friction coefficient) and heat flux on design of heat transfer is shown. The substantiation of boundary conditions of the values of parameters, influencing accuracy of rated relationships, is given. More correct relationships for heat transfer prediction, which showed good convergence with experiments made by different authors, are substantiated in this work.

Keywords: film condensation, heat transfer, plain tube, shear stress

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Authors: Ehsan Mehryaar, Seyed Armin Motahari Tabari

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Without a doubt, soil shear strength is the most important property of the soil. The majority of fatal and catastrophic geological accidents are related to shear strength failure of the soil. Therefore, its prediction is a matter of high importance. However, acquiring the shear strength is usually a cumbersome task that might need complicated laboratory testing. Therefore, prediction of it based on common and easy to get soil properties can simplify the projects substantially. In this paper, A hybrid model based on the classification and regression tree algorithm and logistic regression is proposed where each leaf of the tree is an independent regression model. A database of 189 points for clay soil, including Moisture content, liquid limit, plastic limit, clay content, and shear strength, is collected. The performance of the developed model compared to the existing models and equations using root mean squared error and coefficient of correlation.

Keywords: model tree, CART, logistic regression, soil shear strength

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Authors: Said Boukais, Ali Kezmane, Kahil Amar, Mohand Hamizi, Hannachi Neceur Eddine

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This paper presents an analytical study on the behavior of rectangular reinforced concrete walls with an aspect ratio between one and tow. Several experiments on such walls have been selected to be studied. Database from various experiments were collected and nominal wall strengths have been calculated using formulas, such as those of the ACI (American), NZS (New Zealand), Mexican (NTCC), and Wood equation for shear and strain compatibility analysis for flexure. Subsequently, nominal ultimate wall strengths from the formulas were compared with the ultimate wall strengths from the database. These formulas vary substantially in functional form and do not account for all variables that affect the response of walls. There is substantial scatter in the predicted values of ultimate strength. New semi empirical equation are developed using data from tests of 46 walls with the objective of improving the prediction of ultimate strength of walls with the most possible accuracy and for all failure modes.

Keywords: prediction, ultimate strength, reinforced concrete walls, walls, rectangular walls

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Authors: A. Khosravi, S. Ghadirian, J. S. McCartney

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Evaluation of the seismic-induced settlement of an unsaturated soil layer depends on several variables, among which the small strain shear modulus, Gmax, and soil’s state of stress have been demonstrated to be of particular significance. Recent interpretation of trends in Gmax revealed considerable effects of the degree of saturation and hydraulic hysteresis on the shear stiffness of soils in unsaturated states. Accordingly, the soil layer is expected to experience different settlement behaviors depending on the soil saturation and seasonal weathering conditions. In this study, a semi-empirical formulation was adapted to extend an existing Gmax model to infer hysteretic effects along different paths of the SWRC including scanning curves. The suitability of the proposed approach is validated against experimental results from a suction-controlled resonant column test and from data reported in literature. The model was observed to follow the experimental data along different paths of the SWRC, and showed a slight hysteresis in shear modulus along the scanning curves.

Keywords: hydraulic hysteresis, scanning path, small strain shear modulus, unsaturated soil

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Authors: Sudikshya Bhandari, Jonathan K. London

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The earthquake of April 2015 was one of the biggest disasters in the history of Nepal. The epicenter was located near Barpak, north of the Gorkha district. Before the disaster, this settlement was a compact and homogeneous settlement manifesting its uniqueness through the social and cultural activities, and a distinct vernacular architecture. Narrow alleys with stone paved streets, buildings with slate roofs, and common spaces between the houses made this settlement socially, culturally, and environmentally cohesive. With the presence of micro hydro power plants, local economic activities enabled the local community to exist and thrive. Agriculture and animal rearing are the sources of livelihood for the majority of families, along with the booming homestays (where local people welcome guests to their home, as a business) and local shops. Most of these activities are difficult to find as the houses have been destroyed with the earthquake and the process of reconstruction has been transforming the outlook of the settlement. This study characterized the drastic transformation in Barpak post-earthquake, and analyzed the consequences of the reconstruction process. In addition, it contributes to comprehending a broader representation about unsustainability created by the lack of contextual post-disaster development. Since the research is based in a specific area, a case study approach was used. Sample houses were selected on the basis of ethnicity and house typology. Mixed methods such as key informant and semi structured interviews, focus groups, observations and photographs are used for the collection of data. The research focus is predominantly on the physical change of the house typology from vernacular to externally adopted designs. This transformation of the house entails socio-cultural changes such as social fragmentation with differences among the rich and the poor and decreases in the social connectivity within families and neighborhood. Families have found that new houses require more maintenance and resources that have increased their economic expenses. The study also found that the reconstructed houses are not thermally comfortable in the cold climate of Barpak, leading to the increased use of different sources of heating like electric heaters and more firewood. Lack of storage spaces for crops and livestock have discouraged them to pursue traditional means of livelihood and depend more on buying food from stores, ultimately making it less economical for most of the families. The transformation of space leading to the economic, social and cultural changes demonstrates the unsustainability of Barpak. Conclusions from the study suggest place based and inclusive planning and policy formations that include locals as partners, identifying the possible ways to minimize the impact and implement these recommendations into the future policy and planning scenarios.

Keywords: earthquake, Nepal, reconstruction, settlement, transformation

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Authors: Mona Hejazi, Ali Motie Nasrabadi

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Epilepsy is a persistent neurological disorder that affects more than 50 million people worldwide. Hence, there is a necessity to introduce an efficient prediction model for making a correct diagnosis of the epileptic seizure and accurate prediction of its type. In this study we consider how the Effective Connectivity (EC) patterns obtained from intracranial Electroencephalographic (EEG) recordings reveal information about the dynamics of the epileptic brain and can be used to predict imminent seizures, as this will enable the patients (and caregivers) to take appropriate precautions. We use this definition because we believe that effective connectivity near seizures begin to change, so we can predict seizures according to this feature. Results are reported on the standard Freiburg EEG dataset which contains data from 21 patients suffering from medically intractable focal epilepsy. Six channels of EEG from each patients are considered and effective connectivity using Directed Transfer Function (DTF) and Granger Causality (GC) methods is estimated. We concentrate on effective connectivity standard deviation over time and feature changes in five brain frequency sub-bands (Alpha, Beta, Theta, Delta, and Gamma) are compared. The performance obtained for the proposed scheme in predicting seizures is: average prediction time is 50 minutes before seizure onset, the maximum sensitivity is approximate ~80% and the false positive rate is 0.33 FP/h. DTF method is more acceptable to predict epileptic seizures and generally we can observe that the greater results are in gamma and beta sub-bands. The research of this paper is significantly helpful for clinical applications, especially for the exploitation of online portable devices.

Keywords: effective connectivity, Granger causality, directed transfer function, epilepsy seizure prediction, EEG

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how to achieve knowledge understanding and prediction (Situation Awareness (SA)) for multiple-agents conducting searching activity using Bayesian inferential reasoning and learning. Bayesian Belief Network was used to monitor agents' knowledge about their environment, and cases are recorded for the network training using expectation-maximisation or gradient descent algorithm. The well trained network will be used for decision making and environmental situation prediction. Forest fire searching by multiple UAVs was the use case. UAVs are tasked to explore a forest and find a fire for urgent actions by the fire wardens. The paper focused on two problems: (i) effective agents’ path planning strategy and (ii) knowledge understanding and prediction (SA). The path planning problem by inspiring animal mode of foraging using Lévy distribution augmented with Bayesian reasoning was fully described in this paper. Results proof that the Lévy flight strategy performs better than the previous fixed-pattern (e.g., parallel sweeps) approaches in terms of energy and time utilisation. We also introduced a waypoint assessment strategy called k-previous waypoints assessment. It improves the performance of the ordinary levy flight by saving agent’s resources and mission time through redundant search avoidance. The agents (UAVs) are to report their mission knowledge at the central server for interpretation and prediction purposes. Bayesian reasoning and learning were used for the SA and results proof effectiveness in different environments scenario in terms of prediction and effective knowledge representation. The prediction accuracy was measured using learning error rate, logarithm loss, and Brier score and the result proves that little agents mission that can be used for prediction within the same or different environment. Finally, we described a situation-based knowledge visualization and prediction technique for heterogeneous multi-UAV mission. While this paper proves linkage of Bayesian reasoning and learning with SA and effective searching strategy, future works is focusing on simplifying the architecture.

Keywords: Levy flight, distributed constraint optimization problem, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence

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Authors: Rehab Abdulmajed, Amr Hamada, Ahmed Elsaid, Hisashi Hayakawa, Ayman Mahrous

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Solar emissions have a high impact on the Earth’s magnetic field, and the prediction of solar events is of high interest. Various techniques have been used in the prediction of solar wind using mathematical models, MHD models, and neural network (NN) models. This study investigates the coronal hole (CH) derived high-speed streams (HSSs) and their correlation to the CH area and create a neural network model to predict the HSSs. Two different algorithms were used to compare different models to find a model that best simulates the HSSs. A dataset of CH synoptic maps through Carrington rotations 1601 to 2185 along with Omni-data set solar wind speed averaged over the Carrington rotations is used, which covers Solar cycles (sc) 21, 22, 23, and most of 24.

Keywords: artificial neural network, coronal hole area, feed-forward neural network models, solar high speed streams

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Authors: Neritan Shkodrani

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On November 26, 2019, an earthquake hit the central western part of Albania. It was assessed as Mw 6.4. Its epicenter was located offshore north western Durrës, about 7 km north of the city. In this paper, the consequences of settlements of very soft soils have been discussed for the case of a residential building, mentioned as “K Building”, which was suffering a significant tilting after the earthquake. “KBuilding” is an RC framed building having 12+1 (basement) storiesand a floor area of 21000 m2. The construction of the building was completed in 2012. “KBuilding”, located in Durres city, suffered severe non-structural damage during November 26, 2019, Durrës Earthquake sequences. During the in-site inspections immediately after the earthquake, the general condition of the buildings, the presence of observable settlements on the ground, and the crack situation in the structure were determined, and damage inspection were performed. It was significant to note that the “K Building” presented tilting that might be attributed, as it was believed at the beginning, partially to the failure of the columns of the ground floor and partially to liquefaction phenomena, but it did not collapse. At the first moment was not clear if the foundation had a bearing capacity failure or the foundation failed because of the soil liquefaction. Geotechnical soil investigations by using CPTU test were executed, and their data are usedto evaluatebearing capacity, consolidation settlement of the mat foundation, and soil liquefaction since they were believed to be the main reasons of this building tilting.Geotechnical soil investigation consist in 5 (five) Static Cone Penetration tests with pore pressure measurement (piezocone test). They reached a penetration depth of 20.0 m to 30.0 mand, clearly shown the presence of very soft and organic soils in the soil profile of the site. Geotechnical CPT based analysis of bearing capacity, consolidation, and secondary settlement are applied, and results are reported for each test. These results shown very small values of allowable bearing capacity and very high values of consolidation and secondary settlements. Liquefaction analysis based on the data of CPTU tests and the characteristics of ground shaking of the mentioned earthquake has shown the possibility of liquefaction for some layers of the considered soil profile, but the estimated vertical settlements are at a small range and clearly shown that the main reason of the building tilting was not related to the consequences of liquefaction, but was an existing settlement caused from the applied bearing pressure of this building. All the CPTU tests were carried out on August 2021, almost two years after the November 26, 2019, Durrës Earthquake and when the building itself was demolished. After removing the mat foundation on September 2021, it was possible to carry out CPTU tests even on the footprint of the existing building, which made possible to observe the effects of long time applied of foundation bearing pressure to the consolidation on the considered soil profile.

Keywords: bearing capacity, cone penetration test, consolidation settlement, secondary settlement, soil liquefaction, etc

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Authors: Brahim-Fares Zaidi, Malika Boudraa, Sid-Ahmed Selouani

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Our work aims to improve our Automatic Recognition System for Dysarthria Speech (ARSDS) based on the Hidden Models of Markov (HMM) and the Hidden Markov Model Toolkit (HTK) to help people who are sick. With pronunciation problems, we applied two techniques of speech parameterization based on Mel Frequency Cepstral Coefficients (MFCC's) and Perceptual Linear Prediction (PLP's) and concatenated them with JITTER and SHIMMER coefficients in order to increase the recognition rate of a dysarthria speech. For our tests, we used the NEMOURS database that represents speakers with dysarthria and normal speakers.

Keywords: hidden Markov model toolkit (HTK), hidden models of Markov (HMM), Mel-frequency cepstral coefficients (MFCC), perceptual linear prediction (PLP’s)

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Authors: Anurag Jha

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Dharavi is the largest slum in Asia, for which many redevelopment projects have been put forth, to improve the housing conditions of the locals. And yet, these projects are met with much-unexpected resistance from the locals. The research analyses the why and the how of the resistances these projects face and analyses these programs and points out the flaws and benefits of such projects, by predicting its impact on the regulars of Dharavi. The research aims to analyze various aspects of Dharavi, which affect its socio-cultural backdrops, such as its history, and eventual growth into a mega slum. Through various surveys, the research aims to analyze the life of a slum dweller, the street life, and the effect of such settlement on the urban fabric. Various development projects such as Dharavi Museum Movement, are analyzed, and a feasibility and efficiency analysis of the proposals for redevelopment of Dharavi Slums has been theorized. Flaws and benefits of such projects, by predicting its impact on the regulars of Dharavi has been the major approach to the research. Also, prediction the implementation of these projects in another prominent slum area, Anand Nagar, Bhopal, with the use of generated hypothetical model has been done. The research provides a basic framework for a comparative analysis of various redevelopment projects and the effect of implementation of such projects on the general populace. Secondly, it proposes a hypothetical model for feasibility of such projects in certain slum areas.

Keywords: Anand Nagar, Bhopal slums, Dharavi, slum redevelopment programmes

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Authors: Kevin T. Inks

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India’s Brahmaputra River Valley has, since independence, experienced consistent low-intensity guerrilla warfare between ethnic and religious groups. These groups are often organized around perceived ethnic territoriality, and target civilians, communities, and especially migrants belonging to other ethnic and religious groups. Intense flooding and erosion have led to widespread displacement, and disaster relief funds are largely tied to legal land tenure. Displaced residents of informal settlements receive little or no resettlement aid, and their subsequent migration strategies and risk from guerrilla violence are poorly understood. Semi-structured interviews and comprehensive surveys focused on perceptions of risk, efficacy of disaster relief, and migration and adaptation strategies were conducted with households identified as being ‘at-risk’ of catastrophic flooding and erosion in Majuli District, Assam. Interviews with policymakers and government workers were conducted to assess disaster relief efforts in informal settlements, and remote sensing methods were used to identify informal settlement and hydrogeomorphic change. The results show that various ethnic and religious groups have differential strategies and preferences for resettlement. However, these varying strategies are likely to lead to differential levels of risk from guerrilla violence. Members of certain ethnic groups residing in informal settlements, in the absence of resettlement assistance, are more likely to seek out unofficial settlement on land far from the protection of the state and experience greater risk of becoming victims of political violence. As climate change and deforestation are likely to increase the severity of the displacement crisis in the Brahmaputra River Valley, more comprehensive disaster relief and surveying efforts are vital for limiting migration and informal settlement in potential sites of guerrilla warfare.

Keywords: climate, displacement, flooding, India, violence

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Authors: Hyun-Woo Cho

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Detection of incipient abnormal events in production processes is important to improve safety and reliability of manufacturing operations and reduce losses caused by failures. The construction of calibration models for predicting faulty conditions is quite essential in making decisions on when to perform preventive maintenance. This paper presents a multivariate calibration monitoring approach based on the statistical analysis of process measurement data. The calibration model is used to predict faulty conditions from historical reference data. This approach utilizes variable selection techniques, and the predictive performance of several prediction methods are evaluated using real data. The results shows that the calibration model based on supervised probabilistic model yielded best performance in this work. By adopting a proper variable selection scheme in calibration models, the prediction performance can be improved by excluding non-informative variables from their model building steps.

Keywords: calibration model, monitoring, quality improvement, feature selection

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Authors: Xiangtuo Chen, Paul-Henry Cournéde

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Crop yield prediction is a paramount issue in agriculture. The main idea of this paper is to find out efficient way to predict the yield of corn based meteorological records. The prediction models used in this paper can be classified into model-driven approaches and data-driven approaches, according to the different modeling methodologies. The model-driven approaches are based on crop mechanistic modeling. They describe crop growth in interaction with their environment as dynamical systems. But the calibration process of the dynamic system comes up with much difficulty, because it turns out to be a multidimensional non-convex optimization problem. An original contribution of this paper is to propose a statistical methodology, Multi-Scenarios Parameters Estimation (MSPE), for the parametrization of potentially complex mechanistic models from a new type of datasets (climatic data, final yield in many situations). It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction is free of the complex biophysical process. But it has some strict requirements about the dataset. A second contribution of the paper is the comparison of these model-driven methods with classical data-driven methods. For this purpose, we consider two classes of regression methods, methods derived from linear regression (Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression) and machine learning methods (Random Forest, k-Nearest Neighbor, Artificial Neural Network and SVM regression). The dataset consists of 720 records of corn yield at county scale provided by the United States Department of Agriculture (USDA) and the associated climatic data. A 5-folds cross-validation process and two accuracy metrics: root mean square error of prediction(RMSEP), mean absolute error of prediction(MAEP) were used to evaluate the crop prediction capacity. The results show that among the data-driven approaches, Random Forest is the most robust and generally achieves the best prediction error (MAEP 4.27%). It also outperforms our model-driven approach (MAEP 6.11%). However, the method to calibrate the mechanistic model from dataset easy to access offers several side-perspectives. The mechanistic model can potentially help to underline the stresses suffered by the crop or to identify the biological parameters of interest for breeding purposes. For this reason, an interesting perspective is to combine these two types of approaches.

Keywords: crop yield prediction, crop model, sensitivity analysis, paramater estimation, particle swarm optimization, random forest

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Authors: Yang Zhou, Heli Sun, Jianbin Huang, Jizhong Zhao, Shaojie Qiao

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Traffic prediction is a fundamental problem in urban environment, facilitating the smart management of various businesses, such as taxi dispatching, bike relocation, and stampede alert. Most earlier methods rely on identifying the intrinsic spatial-temporal correlation to forecast. However, the complex nature of this problem entails a more sophisticated solution that can simultaneously capture the mutual influence of both adjacent and far-flung areas, with the information of time-dimension also incorporated seamlessly. To tackle this difficulty, we propose a new multi-phase architecture, DSTDS (Dual Spatial-Temporal Data Scheme for traffic prediction), that aims to reveal the underlying relationship that determines future traffic trend. First, a graph-based neural network with an attention mechanism is devised to obtain the static features of the road network. Then, a multi-granularity recurrent neural network is built in conjunction with the knowledge from a grid-based model. Subsequently, the preceding output is fed into a spatial-temporal super-resolution module. With this 3-phase structure, we carry out extensive experiments on several real-world datasets to demonstrate the effectiveness of our approach, which surpasses several state-of-the-art methods.

Keywords: traffic prediction, spatial-temporal, recurrent neural network, dual data scheme

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Authors: Edson Ephraim Sanga

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This paper stems to explore possible places used for recreation in unplanned settlements in order to avail knowledge on how to create and shape urban spaces essential for recreation and leisure. The context of unplanned settlements is spatially characterized compactness and congestions of buildings developed by residents without professional inputs. These characteristics surpass greenery landscapes such as parks and squares essential for health, happiness and wellbeing. The lack of recreational greenery landscape arises a question on how possible can recreation take places in the settlements? This study used qualitative methods mainly observation and in-depth interview to explore the recreational situation in Manzese informal settlements as an instrumental case and found that ordinary spaces are re-configured into recreational spaces and used as ‘parks’ and ‘squares’ in the settlements. The spaces are diverse and complex as they possess different spatial characteristics based on their physical attributes and the way they are used and interpreted by respective users. This paper argues that the re-configuration processes of ordinary spaces should not be taken for granted because they portray the appropriation of spaces from quotidian dimensions in a particular context.

Keywords: ordinary spaces, recreation, unplanned settlement, urban spaces

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Authors: S. Adjiri-Bailiche, S. M. Boudia, H. Daaou, S. Hadouche, A. Benzaoui

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In this work the study of changes in the wind speed depending on the altitude is calculated and described by the model of the neural networks, the use of measured data, the speed and direction of wind, temperature and the humidity at 10 m are used as input data and as data targets at 50m above sea level. Comparing predict wind speeds and extrapolated at 50 m above sea level is performed. The results show that the prediction by the method of artificial neural networks is very accurate.

Keywords: MATLAB, neural network, power low, vertical extrapolation, wind energy, wind speed

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Authors: Sijing Xiong, Ran Su, Lit-Hsin Loo, Daniele Zink

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The kidney is a major target for toxic effects of drugs, industrial and environmental chemicals and other compounds. Typically, nephrotoxicity is detected late during drug development, and regulatory animal models could not solve this problem. Validated or accepted in silico or in vitro methods for the prediction of nephrotoxicity are not available. We have established the first and currently only pre-validated in vitro models for the accurate prediction of nephrotoxicity in humans and the first predictive platforms based on renal cells derived from human pluripotent stem cells. In order to further improve the efficiency of our predictive models, we recently developed a high content screening (HCS) platform. This platform employed automated imaging in combination with automated quantitative phenotypic profiling and machine learning methods. 129 image-based phenotypic features were analyzed with respect to their predictive performance in combination with 44 compounds with different chemical structures that included drugs, environmental and industrial chemicals and herbal and fungal compounds. The nephrotoxicity of these compounds in humans is well characterized. A combination of chromatin and cytoskeletal features resulted in high predictivity with respect to nephrotoxicity in humans. Test balanced accuracies of 82% or 89% were obtained with human primary or immortalized renal proximal tubular cells, respectively. Furthermore, our results revealed that a DNA damage response is commonly induced by different PTC-toxicants with diverse chemical structures and injury mechanisms. Together, the results show that the automated HCS platform allows efficient and accurate nephrotoxicity prediction for compounds with diverse chemical structures.

Keywords: high content screening, in vitro models, nephrotoxicity, toxicity prediction

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Authors: Yingkun Huang, Li Guo, Zekang Lan, Kai Tian

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Hard disk drives (HDD) failure of the exascale supercomputing system may lead to service interruption and invalidate previous calculations, and it will cause permanent data loss. Therefore, initiating corrective actions before hard drive failures materialize is critical to the continued operation of jobs. In this paper, a highly accurate analysis model based on CNN-LSTM and oversampling technique was proposed, which can correctly predict the necessity of a disk replacement even ten days in advance. Generally, the learning-based method performs poorly on a training dataset with long-tail distribution, especially fault prediction is a very classic situation as the scarcity of failure data. To overcome the puzzle, a new oversampling was employed to augment the data, and then, an improved CNN-LSTM with the shortcut was built to learn more effective features. The shortcut transmits the results of the previous layer of CNN and is used as the input of the LSTM model after weighted fusion with the output of the next layer. Finally, a detailed, empirical comparison of 6 prediction methods is presented and discussed on a public dataset for evaluation. The experiments indicate that the proposed method predicts disk failure with 0.91 Precision, 0.91 Recall, 0.91 F-measure, and 0.90 MCC for 10 days prediction horizon. Thus, the proposed algorithm is an efficient algorithm for predicting HDD failure in supercomputing.

Keywords: HDD replacement, failure, CNN-LSTM, oversampling, prediction

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Authors: Ichihan Tai, Bill Olson, Paul Blessner

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Traditional early warning systems that alarm against crisis are generally based on structured or numerical data; therefore, a system that can make predictions based on unstructured textual data, an uncorrelated data source, is a great complement to the traditional early warning systems. The Chicago Board Options Exchange (CBOE) Volatility Index (VIX), commonly referred to as the fear index, measures the cost of insurance against market crash, and spikes in the event of crisis. In this study, news data is consumed for prediction of whether there will be a market-wide crisis by predicting the movement of the fear index, and the historical references to similar events are presented in an unsupervised manner. Topic modeling-based prediction and representation are made based on daily news data between 1990 and 2015 from The Wall Street Journal against VIX index data from CBOE.

Keywords: early warning system, knowledge management, market prediction, topic modeling.

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Authors: Rodrigo S. Fonseca, Antônio C. P. Veiga

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Aiming at helping people with some movement limitation that makes typing and communication difficult, there is a need to customize an assistive tool with a learning environment that helps the user in order to optimize text input, identifying the error and providing the correction and possibilities of choice in the Portuguese language. The work presents an Orthographic and Grammatical System that can be incorporated into writing environments, improving and facilitating the use of an alphanumeric keyboard, using a prototype built using a genetic algorithm in addition to carrying out the prediction, which can occur based on the quantity and position of the inserted letters and even placement in the sentence, ensuring the sequence of ideas using a Long Short Term Memory (LSTM) neural network. The prototype optimizes data entry, being a component of assistive technology for the textual formulation, detecting errors, seeking solutions and informing the user of accurate predictions quickly and effectively through machine learning.

Keywords: genetic algorithm, neural networks, word prediction, machine learning

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Authors: Sayantani Neogi, Sean Taylor, Julia Budka

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This paper presents the results of the investigations of soil/sediment sequences associated with the New Kingdom town at Sai Island, Sudan. During the course of this study, geoarchaeological surveys have been undertaken in the vicinity of this Pharaonic town within the island and the soil block samples for soil micromorphological analysis were accordingly collected. The intention was to better understand the archaeological site in its environmental context and the nature of the land surface prior to the establishment of the settlement. Soil micromorphology, a very powerful geoarchaeological methodology, is concerned with the description, measurement and interpretation of soil components and pedological features at a microscopic scale. Since soil profiles themselves are archives of their own history, soil micromorphology investigates the environmental and cultural signatures preserved within buried soils and sediments. A study of the thin sections from these soils/sediments has been able to provide robust data for providing interesting insights into the various nuances of this site, for example, the nature of the topography and existent environmental condition during the time of Pharaonic site establishment. These geoarchaeological evaluations have indicated that there is a varied hidden landscape context for this pharaonic settlement, which indicates a symbiotic relationship with the Nilotic environmental system.

Keywords: geoarchaeology, New Kingdom, Nilotic environment, soil micromorphology

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Authors: Kassaye Balkew Workagegn, Gunnar Klemetsdal, Hans Magnus Gjøen

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In this study, the first objective was to investigate whether non-lethal or non-invasive methods, utilizing body measurements, could be used to efficiently predict fillet weight and fillet yield for a complete diallel cross of three Nile tilapia (Oreochromis niloticus) strains collected from three Ethiopian Rift Valley lakes, Lakes Ziway, Koka and Chamo. The second objective was to estimate heritability of body weight, actual and predicted fillet traits, as well as genetic correlations between these traits. A third goal was to estimate additive, reciprocal, and heterosis effects for body weight and the various fillet traits. As in females, early sexual maturation was widespread, only 958 male fish from 81 full-sib families were used, both for the prediction of fillet traits and in genetic analysis. The prediction equations from body measurements were established by forward regression analysis, choosing models with the least predicted residual error sums of squares (PRESS). The results revealed that body measurements on live Nile tilapia is well suited to predict fillet weight but not fillet yield (R²= 0.945 and 0.209, respectively), but both models were seemingly unbiased. The genetic analyses were carried out with bivariate, multibreed models. Body weight, fillet weight, and predicted fillet weight were all estimated with a heritability ranged from 0.23 to 0.28, and with genetic correlations close to one. Contrary, fillet yield was only to a minor degree heritable (0.05), while predicted fillet yield obtained a heritability of 0.19, being a resultant of two body weight variables known to have high heritability. The latter trait was estimated with genetic correlations to body weight and fillet weight traits larger than 0.82. No significant differences among strains were found for their additive genetic, reciprocal, or heterosis effects, while total heterosis effects were estimated as positive and significant (P < 0.05). As a conclusion, prediction of prediction of fillet weight based on body measurements is possible, but not for fillet yield.

Keywords: additive, fillet traits, genetic correlation, heritability, heterosis, prediction, reciprocal

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Authors: Ali H. Mahfouz, Hossam E. M. Sallam, Abdulwali Wazir, Hamod H. Kharezi

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The soil profile at site of the bridge project includes soft fine grained soil layer located between 5.0 m to 11.0 m in depth, it has high water content, low SPT no., and low bearing capacity. The clay layer induces high settlement due to surcharge application of earth embankment at ramp T1, ramp T2, and ramp T3 especially at heights from 9m right 3m. Calculated settlement for embankment heights less than 3m may be accepted regarding Saudi Code for soil and foundation. The soil and groundwater at the project site comprise high contents of sulfates and chlorides of high aggressively on concrete and steel bars, respectively. Regarding results of the study, it has been recommended to use stone column piles or new technology named PCC piles as soil improvement to improve the bearing capacity of the weak layer. The new technology is cast in-situ thin wall concrete pipe piles (PCC piles), it has economically advantageous and high workability. The technology can save time of implementation and cost of application is almost 30% of other types of piles.

Keywords: soft foundation soil, bearing capacity, bridge ramps, soil improvement, geogrid, PCC piles

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Authors: Salim H. Al Salmani, Kevin Tansey, Mohammed S. Ozigis

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The study of the growth of built-up areas and settlement expansion is a major exercise that city managers seek to undertake to establish previous and current developmental trends. This is to ensure that there is an equal match of settlement expansion needs to the appropriate levels of services and infrastructure required. This research aims at demonstrating the potential of satellite image processing technique, harnessing the utility of single feature probability-object based image analysis technique in assessing the urban growth dynamics of the Muscat Governorate in Oman for the period 1990, 2002 and 2013. This need is fueled by the continuous expansion of the Muscat Governorate beyond predicted levels of infrastructural provision. Landsat Images of the years 1990, 2002 and 2013 were downloaded and preprocessed to forestall appropriate radiometric and geometric standards. A novel approach of probability filtering of the target feature segment was implemented to derive the spatial extent of the final Built-Up Area of the Muscat governorate for the three years period. This however proved to be a useful technique as high accuracy assessment results of 55%, 70%, and 71% were recorded for the Urban Landcover of 1990, 2002 and 2013 respectively. Furthermore, the Normalized Differential Built – Up Index for the various images were derived and used to consolidate the results of the SFP-OBIA through a linear regression model and visual comparison. The result obtained showed various hotspots where urbanization have sporadically taken place. Specifically, settlement in the districts (Wilayat) of AL-Amarat, Muscat, and Qurayyat experienced tremendous change between 1990 and 2002, while the districts (Wilayat) of AL-Seeb, Bawshar, and Muttrah experienced more sporadic changes between 2002 and 2013.

Keywords: urban growth, single feature probability, object based image analysis, landcover change

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Authors: Dhiaadin Bahaadin Noory

Abstract:

This research involves the effect of both sizes of reinforced zone and the amount of polypropylene fiber reinforcement on the structural behavior of model-reinforced sand loaded by square footing. The ratio of the side of the square reinforced zone to the footing width (W/B) and the ratio of the square reinforced zone depth to footing width (H/B) has been varied from one to six and from one to three, respectively. The tests were carried out on a small-scale laboratory model in which uniform-graded sand was used as a fill material. It was placed in a highly dense state by hitting a thin wooden board placed on the sand surface with a hammer. The sand was reinforced with randomly oriented discrete fibrillated polypropylene fibers. The test results indicated a significant increase in the bearing capacity and stiffness of the subgrade and a modification of load–the settlement behavior of sand with the size of the reinforced zone and amount of fiber reinforcement. On the basis of the present test results, the optimal side width and depth of the reinforced zone were 4B and 2B, respectively, while the optimal percentage of fibers was 0.4%.

Keywords: square footing, polypropylene fibers, bearing capacity, stiffness, load settlement behavior, relative density

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Authors: Mary Cruz

Abstract:

Founded from 1496 to 1502, Santo Domingo is the oldest European settlement in the Americas, inhabited without any discontinuity and was the first seat of Spanish power in the new world. Capital of the country since 1932.In this text, we discover Santo Domingo as an international tourist center, Urban Structure, Eco-tourism, Contamination and other issues related to tourism and development of this city. Founded from 1496 to 1502, Santo Domingo is the oldest European settlement in the Americas, inhabited without any discontinuity and was the first seat of Spanish power in the new world. Capital of the country since 1932. Encouraged by the United Nations and the World Bank, many Caribbean governments have encouraged tourism from the 1950s to boost their Third World economies. In this text, we discover Santo Domingo as an international tourist center, Urban Structure, Eco-tourism, Contamination and challenges of the first tourist destination in the Caribbean.

Keywords: eco-tourism, urban structure, contamination, development

Procedia PDF Downloads 287

Authors: Sateesh Kumar Pisini, Swetha Priya Pisini

Abstract:

Granular piles are becoming popular as a technique of deep ground improvement not only in soft cohesive soils but also in loose cohesionless deposits. The present experimental study has been carried out on granular piles in sand (loose sand and medium dense sand i.e. relative density at 15% and 30%) with geogrid reinforcement. In this experimental study, a group of five piles installed in sand (at different spacing i.e s = 2d, 3d and 4d) the length and diameter of the pile (L = 0.4 m and d= 50 mm) kept as same for all series of experiments. Geogrid reinforcement is provided on granular piles with a limited number of laboratory tests. It has been conducted in laboratory to study the behavior of a granular pile with reinforced geogrid layers supporting a square footing at different s/d ratios. The influence of geogrid layers providing on granular piles investigated through model tests. In this paper the experimental study carried out results in significant increase in load carrying capacity and decrease in settlement reduction of the weak cohesionless soil. Also, the behavior of load carrying capacity and settlement with changing the s/d ratio has been carried out through a parametric study.

Keywords: granular piles, cohesionless soil, geogrid reinforcement, load carrying capacity

Procedia PDF Downloads 232

Authors: Nataša Kalajdžija, Strahinja Kovačević, Davor Lončar, Sanja Podunavac Kuzmanović, Lidija Jevrić

Abstract:

In order to investigate the relationship between retention and structure, a quantitative Structure Retention Relationships (QSRRs) study was applied for the prediction of retention times of a set of 23 secoestrane derivatives in a reversed-phase thin-layer chromatography. After the calculation of molecular descriptors, a suitable set of molecular descriptors was selected by using step-wise multiple linear regressions. Artificial Neural Network (ANN) method was employed to model the nonlinear structure-activity relationships. The ANN technique resulted in 5-6-1 ANN model with the correlation coefficient of 0.98. We found that the following descriptors: Critical pressure, total energy, protease inhibition, distribution coefficient (LogD) and parameter of lipophilicity (miLogP) have a significant effect on the retention times. The prediction results are in very good agreement with the experimental ones. This approach provided a new and effective method for predicting the chromatographic retention index for the secoestrane derivatives investigated.

Keywords: lipophilicity, QSRR, RP TLC retention, secoestranes

Procedia PDF Downloads 425