Search results for: prediction of publications

Authors: Hamid Goharnejad, Milad Sadeghpoor Moalem, Mahmood Zakeri Niri, Leili Sadeghi Khalegh Abadi

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While there are many benefits to using reservoir dams, their break leads to destructive effects. From the viewpoint of International Committee of Large Dams (ICOLD), dam break means the collapse of whole or some parts of a dam; thereby the dam will be unable to hold water. Therefore, studying dam break phenomenon and prediction of its behavior and effects reduces losses and damages of the mentioned phenomenon. One of the most common types of reservoir dams is embankment dam. Overtopping in embankment dams occurs because of flood discharge system inability in release inflows to reservoir. One of the most important issues among managers and engineers to evaluate the performance of the reservoir dam rim when sliding into the storage, creating waves is large and long. In this study, the effects of floods which caused the overtopping of the dam have been investigated. It was assumed that spillway is unable to release the inflow. To determine outflow hydrograph resulting from dam break, numerical model using Flow-3D software and empirical equations was used. Results of numerical models and their comparison with empirical equations show that numerical model and empirical equations can be used to study the flood resulting from dam break.

Keywords: embankment dam break, empirical equations, Taleghan dam, Flow-3D numerical model

Procedia PDF Downloads 321

Authors: Reena Murali, David Peter S.

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The small interfering RNA (siRNA) alters the regulatory role of mRNA during gene expression by translational inhibition. Recent studies shows that up regulation of mRNA cause serious diseases like Cancer. So designing effective siRNA with good knockdown effects play an important role in gene silencing. Various siRNA design tools had been developed earlier. In this work, we are trying to analyze the existing good scoring second generation siRNA predicting tools and to optimize the efficiency of siRNA prediction by designing a computational model using Artificial Neural Network and whole stacking energy (ΔG), which may help in gene silencing and drug design in cancer therapy. Our model is trained and tested against a large data set of siRNA sequences. Validation of our results is done by finding correlation coefficient of experimental versus observed inhibition efficacy of siRNA. We achieved a correlation coefficient of 0.727 in our previous computational model and we could improve the correlation coefficient up to 0.753 when the threshold of whole tacking energy is greater than or equal to -32.5 kcal/mol.

Keywords: artificial neural network, double stranded RNA, RNA interference, short interfering RNA

Procedia PDF Downloads 526

Authors: Jia-Jang Wu

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This paper presents the scaling laws that provide the criteria of geometry and dynamic similitude between the full-size rotor-shaft system and its scale model, and can be used to predict the torsional vibration characteristics of the full-size rotor-shaft system by manipulating the corresponding data of its scale model. The scaling factors, which play fundamental roles in predicting the geometry and dynamic relationships between the full-size rotor-shaft system and its scale model, for torsional free vibration problems between scale and full-size rotor-shaft systems are firstly obtained from the equation of motion of torsional free vibration. Then, the scaling factor of external force (i.e., torque) required for the torsional forced vibration problems is determined based on the Newton’s second law. Numerical results show that the torsional free and forced vibration characteristics of a full-size rotor-shaft system can be accurately predicted from those of its scale models by using the foregoing scaling factors. For this reason, it is believed that the presented approach will be significant for investigating the relevant phenomenon in the scale model tests.

Keywords: torsional vibration, full-size model, scale model, scaling laws

Procedia PDF Downloads 396

Authors: Tawfik Thelaidjia, Salah Chenikher

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Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, feature extraction from faulty bearing vibration signals is performed by a combination of the signal’s Kurtosis and features obtained through the preprocessing of the vibration signal samples using Db2 discrete wavelet transform at the fifth level of decomposition. In this way, a 7-dimensional vector of the vibration signal feature is obtained. After feature extraction from vibration signal, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. To improve the classification accuracy for bearing fault prediction, particle swarm optimization (PSO) is employed to simultaneously optimize the SVM kernel function parameter and the penalty parameter. The results have shown feasibility and effectiveness of the proposed approach

Keywords: condition monitoring, discrete wavelet transform, fault diagnosis, kurtosis, machine learning, particle swarm optimization, roller bearing, rotating machines, support vector machine, vibration measurement

Procedia PDF Downloads 437

Authors: Yosra Mefteh Rekik, Younes Boujelbene

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We introduce in this paper a new conceptual model representing the stock market dynamics. This model is essentially based on cognitive behavior of the intelligence investors. In order to validate our model, we build an artificial stock market simulation based on agent-oriented methodologies. The proposed simulator is composed of market supervisor agent essentially responsible for executing transactions via an order book and various kinds of investor agents depending to their profile. The purpose of this simulation is to understand the influence of psychological character of an investor and its neighborhood on its decision-making and their impact on the market in terms of price fluctuations. Therefore, the difficulty of the prediction is due to several features: the complexity, the non-linearity and the dynamism of the financial market system, as well as the investor psychology. The Artificial Neural Networks learning mechanism take on the role of traders, who from their futures return expectations and place orders based on their expectations. The results of intensive analysis indicate that the existence of agents having heterogeneous beliefs and preferences has provided a better understanding of price dynamics in the financial market.

Keywords: artificial intelligence methods, artificial stock market, behavioral modeling, multi-agent based simulation

Procedia PDF Downloads 445

Authors: Fares Laouacheria, Said Kechida, Moncef Chabi

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The objective of the study was based on the hydrological routing modelling for the continuous monitoring of the hydrological situation in the Moudjar river catchment, especially during floods with Hydrologic Engineering Center–Hydrologic Modelling Systems (HEC-HMS). The HEC-GeoHMS was used to transform data from geographic information system (GIS) to HEC-HMS for delineating and modelling the catchment river in order to estimate the runoff volume, which is used as inputs to the hydrological routing model. Two hydrological routing models were used, namely Muskingum and Muskingum routing models, for conducting this study. In this study, a comparison between the parameters of the Muskingum and Muskingum-Cunge routing models in HEC-HMS was used for modelling flood routing in the Moudjar river catchment and determining the relationship between these parameters and the physical characteristics of the river. The results indicate that the effects of input parameters such as the weighting factor "X" and travel time "K" on the output results are more significant, where the Muskingum routing model was more sensitive to input parameters than the Muskingum-Cunge routing model. This study can contribute to understand and improve the knowledge of the mechanisms of river floods, especially in ungauged river catchments.

Keywords: HEC-HMS, hydrological modelling, Muskingum routing model, Muskingum-Cunge routing model

Procedia PDF Downloads 278

Authors: S. Jursová, P. Pustějovská, S. Brožová, J. Bilík

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The paper deals with possibilities of interpretation of iron ore reducibility tests. It presents a mathematical model developed at Centre ENET, VŠB–Technical University of Ostrava, Czech Republic for an evaluation of metallurgical material of blast furnace feedstock such as iron ore, sinter or pellets. According to the data from the test, the model predicts its usage in blast furnace technology and its effects on production parameters of shaft aggregate. At the beginning, the paper sums up the general concept and experience in mathematical modelling of iron ore reduction. It presents basic equation for the calculation and the main parts of the developed model. In the experimental part, there is an example of usage of the mathematical model. The paper describes the usage of data for some predictive calculation. There are presented material, method of carried test of iron ore reducibility. Then there are graphically interpreted effects of used material on carbon consumption, rate of direct reduction and the whole reduction process.

Keywords: blast furnace technology, iron ore reduction, mathematical model, prediction of iron ore reduction

Procedia PDF Downloads 674

Authors: Sina Fadaie, Seyed Abolhassan Naeini

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Coastal embankments play an important role in coastal structures by reducing the effect of the wave forces and controlling the movement of sediments. Many coastal areas are underlain by weak and compressible soils. Estimation of during construction settlement of coastal embankments is highly important in design and safety control of embankments and appurtenant structures. Accordingly, selecting and establishing of an appropriate model with a reasonable level of complication is one of the challenges for engineers. Although there are advanced models in the literature regarding design of embankments, there is not enough information on the prediction of their associated settlement, particularly in coastal areas having considerable soft soils. Marine engineering study in Iran is important due to the existence of two important coastal areas located in the northern and southern parts of the country. In the present study, the validity of Terzaghi’s consolidation theory has been investigated. In addition, the settlement of these coastal embankments during construction is predicted by using special methods in PLAXIS software by the help of appropriate boundary conditions and soil layers. The results indicate that, for the existing soil condition at the site, some parameters are important to be considered in analysis. Consequently, a model is introduced to estimate the settlement of the embankments in such geotechnical conditions.

Keywords: consolidation, settlement, coastal embankments, numerical methods, finite elements method

Procedia PDF Downloads 157

Authors: Nasim Babazadeh

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Growing demand for a quieter dwelling environment has turned the attention of construction companies to reducing the propagated noise of their project. In inner-city constructions, close distance between the construction site and surrounding buildings lessens the efficiency of passive noise control methods. Dwellers of the nearby areas may file complaints and lawsuits against the construction companies due to the emitted construction noise, thereby leading to the interruption of processes, compensation costs, or even suspension of the project. Therefore, construction noise should be predicted along with the project schedule. The advantage of managing the noise in the pre-construction phase is two-fold. Firstly, changes in the time plan and construction methods can be applied more flexibly. Thus, the costs related to rescheduling can be avoided. Secondly, noise-related legal problems are expected to be reduced. To implement noise mapping methods for the mentioned prediction, the required detailed information (such as the location of the noisy process, duration of the noisy work) can be exported from the 4D BIM model. The results obtained from the noise maps would be used to help the planners to define different work scenarios. The proposed approach has been applied for the foundation and earthwork of a site located in a residential area, and the obtained results are discussed.

Keywords: building information modeling, construction noise management, noise mapping, 4D BIM

Procedia PDF Downloads 185

Authors: Hatem Mrad, Alban Notin, Mohamed Bouazara

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Sheet metal forming process has a multiple successive steps starting from sheets fixation to sheets evacuation. Often after forming operation, the sheet has defects requiring additional corrections steps. For example, in the drawing process, the formed sheet may have several defects such as springback, localized thinning and bends. All these defects are directly dependent on process, geometric and material parameters. The prediction and elimination of these defects requires the control of most sensitive parameters. The present study is concerned with a reliable parametric study of deep forming process in order to control the localized thinning. The proposed approach will be based on stochastic finite element method. Especially, the polynomial Chaos development will be used to establish a reliable relationship between input (process, geometric and material parameters) and output variables (sheet thickness). The commercial software Abaqus is used to conduct numerical finite elements simulations. The automatized parametrical modification is provided by coupling a FORTRAN routine, a PYTHON script and input Abaqus files.

Keywords: sheet metal forming, reliability, localized thinning, parametric simulation

Procedia PDF Downloads 423

Authors: Imdat Taymaz, Erman Aslan, Kemal Cakir

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The Lattice Boltzmann Method (LBM) is performed to computationally investigate the laminar flow and heat transfer of an incompressible fluid with constant material properties in a 2D channel with a built-in triangular prism. Both momentum and energy transport is modelled by the LBM. A uniform lattice structure with a single time relaxation rule is used. Interpolation methods are applied for obtaining a higher flexibility on the computational grid, where the information is transferred from the lattice structure to the computational grid by Lagrange interpolation. The flow is researched on for different Reynolds number, while Prandtl number is keeping constant as a 0.7. The results show how the presence of a triangular prism effects the flow and heat transfer patterns for the steady-state and unsteady-periodic flow regimes. As an evaluation of the accuracy of the developed LBM code, the results are compared with those obtained by a commercial CFD code. It is observed that the present LBM code produces results that have similar accuracy with the well-established CFD code, as an additionally, LBM needs much smaller CPU time for the prediction of the unsteady phonema.

Keywords: laminar forced convection, lbm, triangular prism

Procedia PDF Downloads 373

Authors: Prasanna Ramachandran, Kareem Graham, Helena Kiefel, Sunit Jain, Todd DeSantis

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Microbes that reside inside the mammalian GI tract, commonly referred to as the gut microbiome, have been shown to have therapeutic effects in animal models of disease. We hypothesize that specific proteins produced by these microbes are responsible for this activity and may be used directly as therapeutics. To speed up the discovery of these key proteins from the big-data metagenomics, we have applied machine learning techniques. Using amino acid sequences of known epitopes and their corresponding binding partners, protein interaction descriptors (PID) were calculated, making a positive interaction set. A negative interaction dataset was calculated using sequences of proteins known not to interact with these same binding partners. Using Random Forest and positive and negative PID, a machine learning model was trained and used to predict interacting versus non-interacting proteins. Furthermore, the continuous variable, cosine similarity in the interaction descriptors was used to rank bacterial therapeutic candidates. Laboratory binding assays were conducted to test the candidates for their potential as therapeutics. Results from binding assays reveal the accuracy of the machine learning prediction and are subsequently used to further improve the model.

Keywords: protein-interactions, machine-learning, metagenomics, microbiome

Procedia PDF Downloads 376

Authors: Abolfazl Hosseinkhani, Sepehr Sanaye

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Different approaches have been used to predict the performance of the vertical axis wind turbines (VAWT), such as experimental, computational fluid dynamics (CFD), and analytical methods. Analytical methods, such as momentum models that use streamtubes, have low computational cost and sufficient accuracy. The double multiple streamtube (DMST) is one of the most commonly used of momentum models, which divide the rotor plane of VAWT into upwind and downwind. In fact, results from the DMST method have shown some discrepancy compared with experiment results; that is because the Darrieus turbine is a complex and aerodynamically unsteady configuration. In this study, analytical-experimental-based corrections, including dynamic stall, streamtube expansion, and finite blade length correction are used to improve the DMST method. Results indicated that using these corrections for a SANDIA 17-m VAWT will lead to improving the results of DMST.

Keywords: vertical axis wind turbine, analytical, double multiple streamtube, streamtube expansion model, dynamic stall model, finite blade length correction

Procedia PDF Downloads 135

Authors: Seyed Abbas Tabatabaei, Fereydoon Moghadas Nejad, Mohammad Saed

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The bleeding prediction of the asphalt is one of the most complex subjects in the pavement engineering. In this paper, an Adaptive Neuro Fuzzy Inference System (ANFIS) is used for modeling the effect of important parameters on bleeding is trained and tested with the experimental results. bleeding index based on the asphalt film thickness differential as target parameter,asphalt content, temperature depth of two centemeter, heavy traffic, dust to effective binder, Marshall strength, passing 3/4 sieves, passing 3/8 sieves,passing 3/16 sieves, passing NO8, passing NO50, passing NO100, passing NO200 as input parameters. Then, we randomly divided empirical data into train and test sections in order to accomplish modeling. We instructed ANFIS network by 72 percent of empirical data. 28 percent of primary data which had been considered for testing the approprativity of the modeling were entered into ANFIS model. Results were compared by two statistical criterions (R2, RMSE) with empirical ones. Considering the results, it is obvious that our proposed modeling by ANFIS is efficient and valid and it can also be promoted to more general states.

Keywords: bleeding, asphalt film thickness differential, Anfis Modeling

Procedia PDF Downloads 269

Authors: Tao Song, Hao Cheng, Guangfeng Tian, Chuang Xu

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An autonomous emergency braking system is an advanced driving assistance system that enables vehicle collision avoidance and pedestrian collision avoidance to improve vehicle safety. At present, because the pedestrian target is small, and the mobility is large, the pedestrian AEB system is faced with more technical difficulties and higher functional requirements. In this paper, a method of pedestrian target selection based on a variable width funnel is proposed. Based on the current position and predicted position of pedestrians, the relative position of vehicle and pedestrian at the time of collision is calculated, and different braking strategies are adopted according to the hazard level of pedestrian collisions. In the CNCAP standard operating conditions, comparing the method of considering only the current position of pedestrians and the method of considering pedestrian prediction position, as well as the method based on fixed width funnel and variable width funnel, the results show that, based on variable width funnel, the choice of pedestrian target will be more accurate and the opportunity of the intervention of AEB system will be more reasonable by considering the predicted position of the pedestrian target and vehicle's lateral motion.

Keywords: automatic emergency braking system, pedestrian target selection, TTC, variable width funnel

Procedia PDF Downloads 157

Authors: Azlina Binti Abu Bakar, Fahmi Zaidi Bin Abdul Razak, Wan Salihin Wong Abdullah

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Psychographic is a psychological study of values, attitudes, interests and it is used mostly in prediction, opinion research and social research. This study predicts the influence of performance expectancy, effort expectancy, social influence and facilitating condition on e-government acceptance among Malaysian citizens. The survey responses of 543 e-government users have been validated and analyzed by means of covariance-based Structural Equation Modeling. The findings indicate that e-government acceptance among Malaysian citizens are mainly influenced by performance expectancy (β = 0.66, t = 11.53, p < 0.01) and social influence (β = 0.20, t = 4.23, p < 0.01). Surprisingly, there is no significant effect of facilitating condition and effort expectancy on e-government continuance intention (β = 0.01, t = 0.27, p > 0.05; β = -0.01, t = -0.40, p > 0.05). This study offers government and vendors a frame of reference to analyze citizen’s situation before initiating new innovations. In case of Malaysian e-government technology, adoption strategies should be built around fostering level of citizens’ technological expectation and social influence on e-government usage.

Keywords: continuance intention, Malaysian citizen, media psychology, structural equation modeling

Procedia PDF Downloads 327

Authors: A. Shebani, C. Pislaru

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Wear of materials is an everyday experience and has been observed and studied for long time. The prediction of wear is a fundamental problem in the industrial field, mainly correlated to the planning of maintenance interventions and economy. Pin-on-disc test is the most common test which is used to study the wear behaviour. In this paper, the pin-on-disc (AEROTECH UNIDEX 11) is used for the investigation of the effects of normal load and hardness of material on the wear under dry and sliding conditions. In the pin-on-disc rig, two specimens were used; one, a pin which is made of steel with a tip, is positioned perpendicular to the disc, where the disc is made of aluminium. The pin wear and disc wear were measured by using the following instruments: The Talysurf instrument, a digital microscope, and the alicona instrument; where the Talysurf profilometer was used to measure the pin/disc wear scar depth, and the alicona was used to measure the volume loss for pin and disc. After that, the Archard model, American Society for Testing and Materials model (ASTM), and neural network model were used for pin/disc wear modelling and the simulation results are implemented by using the Matlab program. This paper focuses on how the alicona can be considered as a powerful tool for wear measurements and how the neural network is an effective algorithm for wear estimation.

Keywords: wear modelling, Archard Model, ASTM Model, Neural Networks Model, Pin-on-disc Test, Talysurf, digital microscope, Alicona

Procedia PDF Downloads 456

Authors: Mohammad Abdallah

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Due to the constant increase in terrorist attacks, the research and engineering communities have given significant attention to building performance under explosions. This paper presents a methodology for studying and simulating the dynamic responses of steel structures during external detonations, particularly for accurately investigating the impact of incrementing charge weight on the members total behavior, resistance and failure. Prediction damage method was introduced to evaluate the damage level of the steel members based on five scenarios of explosions. Johnson–Cook strength and failure model have been used as well as ABAQUS finite element code to simulate the explicit dynamic analysis, and antecedent field tests were used to verify the acceptance and accuracy of the proposed material strength and failure model. Based on the structural response, evaluation criteria such as deflection, vertical displacement, drift index, and damage level; the obtained results show the vulnerability of steel columns and un-braced steel frames which are designed and optimized to carry dead and live load to resist and endure blast loading.

Keywords: steel structure, blast load, terrorist attacks, charge weight, damage level

Procedia PDF Downloads 364

Authors: Samar Alarif

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Smart sustainable educational campuses are the latest paradigm of innovation in the education domain. Campuses become a hub for sustainable environmental innovations. University has a vital role in paving the road for digital transformations in the infrastructure domain by preparing skilled engineers and specialists. The open digital platform enables smart campuses to simulate real education experience by managing their infrastructure within the curriculums. Moreover, it allows the engagement between governments, businesses, and citizens to push for innovation and sustainable services. Urban and building information modeling platforms have recently attained widespread attention in smart campuses due to their applications and benefits for creating the campus's digital twin in the form of an open digital platform. Qualitative and quantitative strategies were used in directing this research to develop and validate the UIM/BIM platform benefits for smart campuses FM and its impact on the institution's sustainable vision. The research findings are based on literature reviews and case studies of the TU berlin El-Gouna campus. Textual data will be collected using semi-structured interviews with actors, secondary data like BIM course student projects, documents, and publications related to the campus actors. The study results indicated that UIM/BIM has several benefits for the smart campus. Universities can achieve better capacity-building by integrating all the actors in the UIM/BIM process. Universities would achieve their community outreach vision by launching an online outreach of UIM/BIM course for the academic and professional community. The UIM/BIM training courses would integrate students from different disciplines and alumni graduated as well as engineers and planners and technicians. Open platforms enable universities to build a partnership with the industry; companies should be involved in the development of BIM technology courses. The collaboration between academia and the industry would fix the gap, promote the academic courses to reply to the professional requirements, and transfer the industry's academic innovations. In addition to that, the collaboration between academia, industry, government vocational and training centers, and civil society should be promoted by co-creation workshops, a series of seminars, and conferences. These co-creation activities target the capacity buildings and build governmental strategies and policies to support expanding the sustainable innovations and to agree on the expected role of all the stakeholders to support the transformation.

Keywords: smart city, smart educational campus, UIM, urban platforms, sustainable campus

Procedia PDF Downloads 123

Authors: Wu Xiaojun, Liu Ruiping, Yu Xingzhan, Wu Qian

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In precision grinding, utilizing the elastic matrix ball has higher processing efficiency and better superficial quality than traditional grinding. The diversity of characteristics which elastic abrasive tool contact with bend surface results in irregular wear abrasion，and abrasive tool machining status get complicated. There is no theoretical interpretation that parameters affect the grinding accuracy.Aiming at corrosion resistance, wear resistance and other characteristics of M 300 material, it is often used as a material on aerospace precision components. The paper carried out grinding and polishing experiments by using material of M 300,to theoretically show the relationship between stress magnitude and grinding efficiency，and predict the optimal combination of grinding parameter for effective grinding, just for the high abrasion resistance features of M 300, analyzing the micro-contact of elastic ball abrasive tool (Whetstone), using mathematical methods deduce the functional relationship between residual peak removal rate and the main parameters which impact the grinding accuracy on the plane case.Thus laying the foundation for the study of elastic abrasive prediction and compensation.

Keywords: flexible abrasive tool, polishing parameters, Hertz theory, removal rate

Procedia PDF Downloads 545

Authors: Tamanna Goyal, Divya Bansal, Sanjeev Sofat

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In world-threatening terrorist attacks, where early detection, distinction, and prediction are effective diagnosis techniques and for functionally accurate and precise analysis of terrorism data, there are so many data mining & statistical approaches to assure accuracy. The computational extraction of derived patterns is a non-trivial task which comprises specific domain discovery by means of sophisticated algorithm design and analysis. This paper proposes an approach for similarity extraction by obtaining the useful attributes from the available datasets of terrorist attacks and then applying feature selection technique based on the statistical impurity measures followed by clustering techniques on the basis of similarity measures. On the basis of degree of participation of attributes in the rules, the associative dependencies between the attacks are analyzed. Consequently, to compute the similarity among the discovered rules, we applied a weighted similarity measure. Finally, the rules are grouped by applying using hierarchical clustering. We have applied it to an open source dataset to determine the usability and efficiency of our technique, and a literature search is also accomplished to support the efficiency and accuracy of our results.

Keywords: association rules, clustering, similarity measure, statistical approaches

Procedia PDF Downloads 320

Authors: M. Sadoughi, R. Ehsani

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The aim of the present study is examining the relationship between conceptual organizational culture and the level of tolerance in employees of Islamic Azad University of Shahre Ghods. This research is a correlational and analytic-descriptive one. The samples included 144 individuals. A 24-item standard questionnaire of organizational culture by Cameron and Queen was used in this study. This questionnaire has six criteria and each criterion includes four items that each item indicates one cultural dimension. Reliability coefficient of this questionnaire was normed using Cronbach's alpha of 0.91. Also, the 25-item questionnaire of tolerance by Conor and Davidson was used. This questionnaire is in a five-degree Likert scale form. It has seven criteria and is designed to measure the power of coping with pressure and threat. It has the needed content reliability and its reliability coefficient is normed using Cronbach's alpha of 0.87. Data were analyzed using Pearson correlation coefficient and multivariable regression. The results showed among various dimensions of organizational culture, there is a positive significant relationship between three dimensions (family, adhocracy, bureaucracy) and tolerance, there is a negative significant relationship between dimension of market and tolerance and components of organizational culture have the power of prediction and explaining the tolerance. In this explanation, the component of family is the most effective and the best predictor of tolerance.

Keywords: adhocracy, bureaucracy, organizational culture, tolerance

Procedia PDF Downloads 449

Authors: Hamidoddin Yousife

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Drilling optimization requires a prediction of drilling rate of penetration (ROP) since it provides a significant reduction in drilling costs. There are several factors that can have an impact on the ROP, both controllable and uncontrollable. Numerous drilling penetration rate models have been considered based on drilling parameters. This papers considered the effect of proper drilling parameter selection such as bit, Mud Type, applied weight on bit (WOB), Revolution per minutes (RPM), and flow rate on drilling optimization and drilling cost reduction. A predicted analysis is used in real-time drilling performance to determine the optimal drilling operation. As a result of these modeling studies, the real data collected from three directional wells at Azadegan oil fields, Iran, was verified and adjusted to determine the drillability of a specific formation. Simulation results and actual drilling results show significant improvements in inaccuracy. Once simulations had been validated, optimum drilling parameters and equipment specifications were determined by varying weight on bit (WOB), rotary speed (RPM), hydraulics (hydraulic pressure), and bit specification for each well until the highest drilling rate was achieved. To evaluate the potential operational and economic benefits of optimizing results, a qualitative and quantitative analysis of the data was performed.

Keywords: drlling, cost, optimization, parameters

Procedia PDF Downloads 168

Authors: Sirilak Kongkaew, Pathumwadee Yodmanee, Nopporn Kaiyawet, Arthitaya Meeprasert, Thanyada Rungrotmongkol, Toshikatsu Kaburaki, Hiroshi Noguchi, Fujio Takeuch, Nawee Kungwan, Supot Hannongbua

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Behçet’s disease (BD) is a genetic autoimmune expressed by multisystemic inflammatory disorder mostly occurred at the skin, joints, gastrointestinal tract, and genitalia, including ocular, oral, genital, and central nervous systems. Most BD patients in Japan and Korea were strongly indicated by the genetic factor namely HLA-B*51 (especially, HLA-B*51:01) marker in HMC class I, while HLA-A*26:01 allele has been detected from the BD patients in Greek, Japan, and Taiwan. To understand the selective binding of the MICA-TM peptide towards the HLAs associated with BD, the molecular dynamics simulations were applied on the four HLA alleles (B*51:01, B*35:01, A*26:01, and A*11:01) in complex with such peptide. As a result, the key residues in the binding groove of HLA protein which play an important role in the MICA-TM peptide binding and stabilization were revealed. The Van der Waals force was found to be the main protein-protein interaction. Based on the binding free energy prediction by MM/PBSA method, the MICA-TM peptide interacted stronger to the HLA alleles associated to BD in the identical class by 7-12 kcal/mol. The obtained results from the present study could help to differentiate the HLA alleles and explain a source of Behçet’s disease.

Keywords: Behçet’s disease, MD simulations, HMC class I, autoimmune

Procedia PDF Downloads 399

Authors: Yuntao Liu, Lei Wang, Haoran Xia

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Machine learning has shown extensive applications in the development of classification models for autism spectrum disorder (ASD) using neural image data. This paper proposes a fusion multi-modal classification network based on a graph neural network. First, the brain is segmented into 116 regions of interest using a medical segmentation template (AAL, Anatomical Automatic Labeling). The image features of sMRI and the signal features of fMRI are extracted, which build the node and edge embedding representations of the brain map. Then, we construct a dynamically updated brain map neural network and propose a method based on a dynamic brain map adjacency matrix update mechanism and learnable graph to further improve the accuracy of autism diagnosis and recognition results. Based on the Autism Brain Imaging Data Exchange I dataset(ABIDE I), we reached a prediction accuracy of 74% between ASD and TD subjects. Besides, to study the biomarkers that can help doctors analyze diseases and interpretability, we used the features by extracting the top five maximum and minimum ROI weights. This work provides a meaningful way for brain disorder identification.

Keywords: autism spectrum disorder, brain map, supervised machine learning, graph network, multimodal data, model interpretability

Procedia PDF Downloads 66

Authors: Rocío González-Sánchez, Sara Alonso-Muñoz

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Food waste implies an inefficient management of the final stages in the food supply chain. Referring to Sustainable Development Goals (SDGs) by United Nations, the SDG 12.3 proposes to halve per capita food waste at the retail and consumer level and to reduce food losses. In the linear system, food waste is disposed and, to a lesser extent, recovery or reused after consumption. With the negative effect on stocks, the current food consumption system is based on ‘produce, take and dispose’ which put huge pressure on raw materials and energy resources. Therefore, greater focus on the circular management of food waste will mitigate the environmental, economic, and social impact, following a Triple Bottom Line (TBL) approach and consequently the SDGs fulfilment. A mixed methodology is used. A total sample of 311 publications from Web of Science database were retrieved. Firstly, it is performed a bibliometric analysis by SciMat and VOSviewer software to visualise scientific maps about co-occurrence analysis of keywords and co-citation analysis of journals. This allows for the understanding of the knowledge structure about this field, and to detect research issues. Secondly, a systematic literature review is conducted regarding the most influential articles in years 2020 and 2021, coinciding with the most representative period under study. Thirdly, to support the development of this field it is proposed an agenda according to the research gaps identified about circular economy and food waste management. Results reveal that the main topics are related to waste valorisation, the application of waste-to-energy circular model and the anaerobic digestion process towards fossil fuels replacement. It is underlined that the use of food as a source of clean energy is receiving greater attention in the literature. There is a lack of studies about stakeholders’ awareness and training. In addition, available data would facilitate the implementation of circular principles for food waste recovery, management, and valorisation. The research agenda suggests that circularity networks with suppliers and customers need to be deepened. Technological tools for the implementation of sustainable business models, and greater emphasis on social aspects through educational campaigns are also required. This paper contributes on the application of circularity to food waste management by abandoning inefficient linear models. Shedding light about trending topics in the field guiding to scholars for future research opportunities.

Keywords: bibliometric analysis, circular economy, food waste management, future research lines

Procedia PDF Downloads 112

Authors: Saeed Ghali, Hoda El-Faramawy, Mamdouh Eissa, Michael Mishreky

Abstract:

Scale-resistant austenitic stainless steel, X45CrNiW 18-9, has been developed, and modified steels produced through partial and total nickel replacement by nitrogen. These modified steels were produced in a 10 kg induction furnace under different nitrogen pressures and were cast into ingots. The produced modified stainless steels were forged, followed by air cooling. The phases of modified stainless steels have been investigated using the Schaeffler diagram, dilatometer, and microstructure observations. Both partial and total replacement of nickel using 0.33-0.50% nitrogen are effective in producing fully austenitic stainless steels. The nitrogen contents were determined and compared with those calculated using the Institute of Metal Science (IMS) equation. The results showed great deviations between the actual nitrogen contents and predicted values through IMS equation. So, an equation has been derived based on chemical composition, pressure, and temperature at 1600oC. [N%] = 0.0078 + 0.0406*X, where X is a function of chemical composition and nitrogen pressure. The derived equation has been used to calculate the nitrogen content of different steels using published data. The results reveal the difficulty of deriving a general equation for the prediction of nitrogen content covering different steel compositions. So, it is necessary to use a narrow composition range.

Keywords: solubility, nitrogen, stainless steel, Schaeffler

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Authors: Ayu Nabila Kusuma Pradana, Aprinaldi Jasa Mantau, Tomohiko Akahoshi

Abstract:

The incidence of Disseminated intravascular coagulation (DIC) following gastrointestinal surgery has a poor prognosis. Therefore, it is important to determine the factors that can predict the prognosis of DIC. This study will investigate the factors that may influence the outcome of DIC in patients after gastrointestinal surgery. Eighty-one patients were admitted to the intensive care unit after gastrointestinal surgery in Kyushu University Hospital from 2003 to 2021. Acute DIC scores were estimated using the new Japanese Association for Acute Medicine (JAAM) score from before and after surgery from day 1, day 3, and day 7. Acute DIC scores will be compared with The Sequential Organ Failure Assessment (SOFA) score, platelet count, lactate level, and a variety of biochemical parameters. This study applied machine learning algorithms to predict the prognosis of DIC after gastrointestinal surgery. The results of this study are expected to be used as an indicator for evaluating patient prognosis so that it can increase life expectancy and reduce mortality from cases of DIC patients after gastrointestinal surgery.

Keywords: the survival rate, gastrointestinal surgery, JAAM score, neural network, machine learning, disseminated intravascular coagulation (DIC)

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Authors: Tomi Lois Olatunji, Frances Siebert, Ademola Emmanuel Adetunji, Brian Harvey, Johane Gericke, Josias Hamman, Frank Van Der Kooy

Abstract:

Ethnopharmacological relevance: Sceletium tortuosum (L.) N.E.Br, the most sought after and widely researched species in the genus Sceletium is a succulent forb endemic to South Africa. Traditionally, this medicinal plant is mainly masticated or smoked and used for the relief of toothache, abdominal pain, and as a mood-elevator, analgesic, hypnotic, anxiolytic, thirst and hunger suppressant, and for its intoxicating/euphoric effects. Sceletium tortuosum is currently of widespread scientific interest due to its clinical potential in treating anxiety and depression, relieving stress in healthy individuals, and enhancing cognitive functions. These pharmacological actions are attributed to its phytochemical constituents referred to as mesembrine-type alkaloids. Aim of the review: The aim of this review was to comprehensively summarize and critically evaluate recent research advances on the phytochemistry, pharmacokinetics, biological and clinical activities of the medicinal plant S. tortuosum. Additionally, current ongoing research and future perspectives are also discussed. Methods: All relevant scientific articles, books, MSc and Ph.D. dissertations on botany, behavioral pharmacology, traditional uses, and phytochemistry of S. tortuosum were retrieved from different databases (including Science Direct, PubMed, Google Scholar, Scopus and Web of Science). For pharmacokinetics and pharmacological effects of S. tortuosum, the focus fell on relevant publications published between 2009 and 2021. Results: Twenty-five alkaloids belonging to four structural classes viz: mesembrine, Sceletium A4, joubertiamine, and tortuosamine, have been identified from S. tortuosum, of which the mesembrine class is predominant. The crude extracts and commercially available standardized extracts of S. tortuosum have displayed a wide spectrum of biological activities (e.g. antimalarial, anti-oxidant, immunomodulatory, anti-HIV, neuroprotection, enhancement of cognitive function) in in vitro or in vivo studies. This plant has not yet been studied in a clinical population, but has potential for enhancing cognitive function, and managing anxiety and depression. Conclusion: As an important South African medicinal plant, S. tortuosum has garnered many research advances on its phytochemistry and biological activities over the last decade. These scientific studies have shown that S. tortuosum has various bioactivities. The findings have further established the link between the phytochemistry and pharmacological application, and support the traditional use of S. tortuosum in the indigenous medicine of South Africa.

Keywords: Aizoaceae, Mesembrine, Serotonin, Sceletium tortuosum, Zembrin®, psychoactive, antidepressant

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Authors: Stella O. Olubodun, George E. Eriyamremu

Abstract:

The study assessed the effect of crude oil applied at rates, 0, 2, 5, and 10% on the fractional chemical forms and availability of some metals in soils from Usen, Edo State, with no known crude oil contamination and soil from a crude oil spill site in Ubeji, Delta State, Nigeria. Three methods were used to determine the bioavailability of metals in the soils: maize (Zea mays) plant, EDTA and BCR sequential extraction. The sequential extract acid soluble fraction of the BCR extraction (most labile fraction of the soils, normally associated with bioavailability) were compared with total metal concentration in maize seedlings as a means to compare the chemical and biological measures of bioavailability. Total Fe was higher in comparison to other metals for the crude oil contaminated soils. The metal concentrations were below the limits of 4.7% Fe, 190mg/kg Cu and 720mg/kg Zn intervention values and 36mg/kg Cu and 140mg/kg Zn target values for soils provided by the Department of Petroleum Resources (DPR) guidelines. The concentration of the metals in maize seedlings increased with increasing rates of crude oil contamination. Comparison of the metal concentrations in maize seedlings with EDTA extractable concentrations showed that EDTA extracted more metals than maize plant.

Keywords: availability, crude oil contamination, EDTA, maize, metals

Procedia PDF Downloads 228