Search results for: data structure

Authors: Mehul Srivastava, Sharma Tushar Ravikant, Mridul Krishn Mishra

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In structural design, the evaluation of safety and probability failure of structure is of significant importance, mainly when the variables are random. On real structures, structural reliability can be evaluated obtaining an implicit limit state function. The structural reliability limit state function is obtained depending upon the statistically independent variables. In the analysis of reliability, we considered the statistically independent random variables to be the load intensity applied and the depth or height of the beam member considered. There are many approaches for structural reliability problems. In this paper Extreme Learning Machine technique and First Order Second Moment Method is used to determine the reliability indices for the same set of variables. The reliability index obtained using ELM is compared with the reliability index obtained using FOSM. Higher the reliability index, more feasible is the method to determine the reliability.

Keywords: reliability, reliability index, statistically independent, extreme learning machine

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Authors: Gui Yang Wu, Bo Wang, Xin Wang

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Nowadays, 5G service-based interface architecture uses text-based payload like JSON to transfer business data between network functions, which has obvious advantages as internet services but causes unnecessarily larger traffic. In this paper, a new 5G application payload size reduction method is presented to provides the mechanism to negotiate about new capability between network functions when network communication starts up and how 5G application data are reduced according to negotiated information with peer network function. Without losing the advantages of 5G text-based payload, this method demonstrates an excellent result on application payload size reduction and does not increase the usage quota of computing resource. Implementation of this method does not impact any standards or specifications and not change any encoding or decoding functionality too. In a real 5G network, this method will contribute to network efficiency and eventually save considerable computing resources.

Keywords: 5G, JSON, payload size, service-based interface

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Authors: Mohamed Ahmed Abdelmawla

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Calling to achieve Millennium Development Goals (MDGs) engaged with agriculture, i.e. poverty alleviation targets, human resources involved in agricultural sectors with special emphasis on irrigation must receive wealth of practical experience and training. Increased food production, including staple food, is needed to overcome the present and future threats to food security. This should happen within a framework of sustainable management of natural resources, elimination of unsustainable methods of production and poverty reduction (i.e. axes of modernization). A didactic tool to confirm the task of wise and maximum utility is the best management and accurate measurement, as major requisites for modernization process. The key component to modernization as a warranted goal is adhering great attention to management and measurement issues via capacity building. As such, this paper stressed the issues of discharge management and measurement by Field Outlet Pipes (FOP) for selected ones within the Gezira Scheme, where randomly nine FOPs were selected as representative locations. These FOPs extended along the Gezira Main Canal at Kilo 57 areas in the South up to Kilo 194 in the North. The following steps were followed during the field data collection and measurements: For each selected FOP, a 90 v- notch thin plate weir was placed in such away that the water was directed to pass only through the notch. An optical survey level was used to measure the water head of the notch and FOP. Both calculated discharge rates as measured by the v – notch, denoted as [Qc], and the adopted discharges given by (MOIWR), denoted as [Qa], are tackled for the average of three replicated readings undertaken at each location. The study revealed that the FOP overestimates and sometimes underestimates the discharges. This is attributed to the fact that the original design specifications were not fulfilled or met at present conditions where water is allowed to flow day and night with high head fluctuation, knowing that the FOP is non modular structure, i.e. the flow depends on both levels upstream and downstream and confirmed by the results of this study. It is convenient and formative to quantify the discharge in FOP with weirs or Parshall flumes. Cropping calendar should be clearly determined and agreed upon before the beginning of the season in accordance and consistency with the Sudan Gezira Board (SGB) and Ministry of Irrigation and Water Resources. As such, the water indenting should be based on actual Crop Water Requirements (CWRs), not on rules of thumb (420 m3/feddan, irrespective of crop or time of season).

Keywords: management, measurement, MDGs, modernization

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Authors: R. Ranjith Kumar

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Computer modeling and simulation of human movement is playing an important role in sports and rehabilitation. Accurate modeling and analysis of human knee join is more complex because of complicated structure whose geometry is not easily to represent by a solid model. As part of this project, from the number of CT scan images of human knee join surface reconstruction is carried out using 3D slicer software, an open source software. From this surface reconstruction model, using mesh lab (another open source software) triangular meshes are created on reconstructed surface. This final triangular mesh model is imported to Solid Works, 3D mechanical CAD modeling software. Finally this CAD model is imported to ABAQUS, finite element analysis software for analyzing the knee joints. The results obtained are encouraging and provides an accurate way of modeling and analysis of biological parts without human intervention.

Keywords: solid works, CATIA, Pro-e, CAD

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Authors: M. Kharrat, G. Moreau, Z. Aboura

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The latest advances in the weaving industry, combined with increasingly sophisticated means of materials processing, have made it possible to produce complex 3D composite structures. Mainly used in aeronautics, composite materials with 3D architecture offer better mechanical properties than 2D reinforced composites. Nevertheless, these materials require a good understanding of their behavior. Because of the complexity of such materials, the damage mechanisms are multiple, and the scenario of their appearance and evolution depends on the nature of the exerted solicitations. The AE technique is a well-established tool for discriminating between the damage mechanisms. Suitable sensors are used during the mechanical test to monitor the structural health of the material. Relevant AE-features are then extracted from the recorded signals, followed by a data analysis using pattern recognition techniques. In order to better understand the damage scenarios of interlock composite materials, a multi-instrumentation was set-up in this work for tracking damage initiation and development, especially in the vicinity of the first significant damage, called macro-damage. The deployed instrumentation includes video-microscopy, Digital Image Correlation, Acoustic Emission (AE) and micro-tomography. In this study, a multi-variable AE data analysis approach was developed for the discrimination between the different signal classes representing the different emission sources during testing. An unsupervised classification technique was adopted to perform AE data clustering without a priori knowledge. The multi-instrumentation and the clustered data served to label the different signal families and to build a learning database. This latter is useful to construct a supervised classifier that can be used for automatic recognition of the AE signals. Several materials with different ingredients were tested under various solicitations in order to feed and enrich the learning database. The methodology presented in this work was useful to refine the damage threshold for the new generation materials. The damage mechanisms around this threshold were highlighted. The obtained signal classes were assigned to the different mechanisms. The isolation of a 'noise' class makes it possible to discriminate between the signals emitted by damages without resorting to spatial filtering or increasing the AE detection threshold. The approach was validated on different material configurations. For the same material and the same type of solicitation, the identified classes are reproducible and little disturbed. The supervised classifier constructed based on the learning database was able to predict the labels of the classified signals.

Keywords: acoustic emission, classifier, damage mechanisms, first damage threshold, interlock composite materials, pattern recognition

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Authors: Vikas Kumar, Kailash Chandra, Kaomud Tyagi

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The insect order Thysanoptera includes small insects commonly called thrips. As insect vectors, only thrips are capable of Tospoviruses transmission (genus Tospovirus, family Bunyaviridae) affecting various crops. Currently, fifteen species of subfamily Thripinae (Thripidae) have been reported as vectors for tospoviruses. Frankliniella schultzei, which is reported as act as a vector for at least five tospovirses, have been suspected to be a species complex with more than one species. It is one of the historical unresolved issues where, two species namely, F. schultzei Trybom and F. sulphurea Schmutz were erected from South Africa and Srilanaka respectively. These two species were considered to be valid until 1968 when sulphurea was treated as colour morph (pale form) and synonymised under schultzei (dark form) However, these two have been considered as valid species by some of the thrips workers. Parallel studies have indicated that brown form of schultzei is a vector for tospoviruses while yellow form is a non-vector. However, recent studies have shown that yellow populations have also been documented as vectors. In view of all these facts, it is highly important to have a clear understanding whether these colour forms represent true species or merely different populations with different vector carrying capacities and whether there is some hidden diversity in 'Frankliniella schultzei species complex'. In this study, we aim to study the 'Frankliniella schultzei species complex' with molecular spectacles with DNA data from India and Australia and Africa. A total of fifty-five specimens was collected from diverse locations in India and Australia. We generated molecular data using partial fragments of mitochondrial cytochrome c oxidase I gene (mtCOI) and 28S rRNA gene. For COI dataset, there were seventy-four sequences, out of which data on fifty-five was generated in the current study and others were retrieved from NCBI. All the four different tree construction methods: neighbor-joining, maximum parsimony, maximum likelihood and Bayesian analysis, yielded the same tree topology and produced five cryptic species with high genetic divergence. For, rDNA, there were forty-five sequences, out of which data on thirty-nine was generated in the current study and others were retrieved from NCBI. The four tree building methods yielded four cryptic species with high bootstrap support value/posterior probability. Here we could not retrieve one cryptic species from South Africa as we could not generate data on rDNA from South Africa and sequence for rDNA from African region were not available in the database. The results of multiple species delimitation methods (barcode index numbers, automatic barcode gap discovery, general mixed Yule-coalescent, and Poisson-tree-processes) also supported the phylogenetic data and produced 5 and 4 Molecular Operational Taxonomic Units (MOTUs) for mtCOI and 28S dataset respectively. These results of our study indicate the likelihood that F. sulphurea may be a valid species, however, more morphological and molecular data is required on specimens from type localities of these two species and comparison with type specimens.

Keywords: DNA barcoding, species complex, thrips, species delimitation

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Authors: Hyungoo Kang, Minsung Kim, Jinkoo Kim

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This study investigates the performance of a 2D and 3D steel moment frame subjected to vehicle collision at a first story column using LS-DYNA. The finite element models of vehicles provided by the National Crash Analysis Center (NCAC) are used for numerical analysis. Nonlinear dynamic time history analysis of the 2D and 3D model structures are carried out based on the arbitrary column removal scenario, and the vertical displacement of the damaged structures are compared with that obtained from collision analysis. The analysis results show that the model structure remains stable when the speed of the vehicle is 40km/h. However, at the speed of 80 and 120km/h both the 2D and 3D structures collapse by progressive collapse. The vertical displacement of the damaged joint obtained from collision analysis is significantly larger than the displacement computed based on the arbitrary column removal scenario.

Keywords: vehicle collision, progressive collapse, FEM, LS-DYNA

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Authors: M. Panidou, F. Kilipiris, E. Christou, K. Alexandris

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This study intends to investigate the travel preferences of Generation Z (born between 1996 and 2012), focusing on their inclination towards unique and unconventional travel experiences, prioritization of authentic cultural immersion and local experiences over traditional tourist attractions, and their value for flexibility and spontaneity in travel plans. By examining these aspects, the research aims to provide insights into the preferences and behaviors of Generation Z travelers, contributing to a better understanding of their travel choices and informing the tourism industry in catering to their needs and desires. Secondary data was gathered from academic literature and industry reports to offer a thorough study of the topic. A quantitative method was used, and primary data was collected through an online questionnaire. One hundred Greek people between the ages of eighteen and twenty-seven were the study's sample. SPSS software was used to assist in the analysis of the data. The findings of the research showed that Gen Z is attracted to unusual and distinctive travel experiences, prioritizing genuine cultural immersion over typical tourist attractions, and they highly value flexibility in their travel decision-making. This research contributes to a deeper understanding of how Gen Z travelers are reshaping the travel industry. Travel companies, marketers, and destination management organizations will find the findings useful in adjusting their products to suit this influential demographic's changing demands and preferences. Considering the limitations of the sample size, future studies could expand the sample size to include individuals from different cultural backgrounds for a more comprehensive understanding.

Keywords: cultural immersion, flexibility, generation Z, travel preferences, unique experiences

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Authors: Sultan Noman Qasem

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This paper presents an evolutionary algorithm for solving multi-objective optimization problems-based artificial neural network (ANN). The multi-objective evolutionary algorithm used in this study is genetic algorithm while ANN used is radial basis function network (RBFN). The proposed algorithm named memetic elitist Pareto non-dominated sorting genetic algorithm-based RBFNN (MEPGAN). The proposed algorithm is implemented on medical diseases problems. The experimental results indicate that the proposed algorithm is viable, and provides an effective means to design multi-objective RBFNs with good generalization capability and compact network structure. This study shows that MEPGAN generates RBFNs coming with an appropriate balance between accuracy and simplicity, comparing to the other algorithms found in literature.

Keywords: radial basis function network, hybrid learning, multi-objective optimization, genetic algorithm

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Authors: Christopher Webb

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This work presents a new model of mentoring in an organizational environment and has important implications for both practice and research, the model frames the organizational environment as organizational curriculum, which includes the elements that affect learning within the organization. This includes the organizational structure and culture, roles within the organization, and accessibility of knowledge. The program curriculum includes the elements of the mentoring program, including materials, training, and scheduled events for the program participants. The term dyadic curriculum is coined in this work. The dyadic curriculum describes the participation, behavior, and identities of the pairs participating in mentorships. This also includes the identity work of the participants and their views of each other. Much of this curriculum is unprescribed and is unique within each dyad. It describes how participants mediate the elements of organizational and program curricula. These three curricula interact and affect each other in predictable ways. A detailed example of a mentoring program framed in this model is provided.

Keywords: curriculum, mentoring, organizational learning and development, social learning

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Authors: Shaikh Farhad Hossain, Liakot Ali

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Technology advances accelerate the quality and type of services provided for health care and especially for monitoring health conditions. Sensors have turned out to be more effective to detect diverse physiological signs and can be worn on the human body utilizing remote correspondence modules. An assortment of programming devices have been created to help in preparing a difference rundown of essential signs by examining and envisioning information produced by different sensors. In this proposition, we presented a Health signs and Activity acknowledgment monitoring system. Utilizing off-the-rack sensors, we executed a movement location system for identifying five sorts of action: falling, lying down, sitting, standing, and walking. The framework collects and analyzes sensory data in real-time, and provides different feedback to the users. In addition, it can generate alerts based on the detected events and store the data collected to a medical server.

Keywords: ADL, SVM, TRIL , MEMS

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Authors: Catherine Maware, Olufemi Adetunji

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The paper is aimed at developing a model to measure the impact of Lean manufacturing deployment on organizational performance. The model will help industry practitioners to assess the impact of implementing Lean constructs on organizational performance. It will also harmonize the measurement models of Lean performance with the house of Lean that seems to have become the industry standard. The sheer number of measurement models for impact assessment of Lean implementation makes it difficult for new adopters to select an appropriate assessment model or deployment methodology. A literature review is conducted to classify the Lean performance model. Pareto analysis is used to select the Lean constructs for the development of the model. The model is further formalized through the use of Structural Equation Modeling (SEM) in defining the underlying latent structure of a Lean system. An impact assessment measurement model developed can be used to measure Lean performance and can be adopted by different industries.

Keywords: impact measurement model, lean bundles, lean manufacturing, organizational performance

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Authors: Mohammed Affanuddin H. Siddique, Jayesh S. Shukla, Chetan B. Meshram

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The neural networks are one of the power tools of machine learning. After the invention of perceptron in early 1980's, the neural networks and its application have grown rapidly. Neural networks are a technique originally developed for pattern investigation. The structure of a neural network consists of neurons connected through synapse. Here, we have investigated the different algorithms and cost function reduction techniques for optimization of vertical axis wind turbine (VAWT) rotor blades. The aerodynamic force coefficients corresponding to the airfoils are stored in a database along with the airfoil coordinates. A forward propagation neural network is created with the input as aerodynamic coefficients and output as the airfoil co-ordinates. In the proposed algorithm, the hidden layer is incorporated into cost function having linear and non-linear error terms. In this article, it is observed that the ANNs (Artificial Neural Network) can be used for the VAWT’s optimization.

Keywords: VAWT, ANN, optimization, inverse design

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Authors: Manikandan S. P., Sharmila N.

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Pain in the neck, intermediate and anterior, and even low back may occur in one or more locations. Numerous factors can lead to back discomfort, which can manifest into sensations in the other parts of your body. Up to 80% of people will have low back problems at a certain stage of their lives, making spine-related pain a highly prevalent ailment. Roughly twice as commonly as neck pain, low back discomfort also happens about as often as knee pain. According to current studies, using digital devices for extended periods of time and poor sitting posture are the main causes of neck and low back pain. There are numerous monitoring techniques provided to enhance the sitting posture for the aforementioned problems. A sophisticated technique to monitor the extended sitting position is suggested in this research based on this problem. The system is made up of an inertial measurement unit, a T-shirt, an Arduino board, a buzzer, and a mobile app with cloud services. Based on the anatomical position of the spinal cord, the inertial measurement unit was positioned on the inner back side of the T-shirt. The IMU (inertial measurement unit) sensor will evaluate the hip position, imbalanced shoulder, and bending angle. Based on the output provided by the IMU, the data will be analyzed by Arduino, supplied through the cloud, and shared with a mobile app for continuous monitoring. The buzzer will sound if the measured data is mismatched with the human body's natural position. The implementation and data prediction with design to identify balanced and unbalanced posture using a posture monitoring t-shirt will be further discussed in this research article.

Keywords: IMU, posture, IOT, textile

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Authors: Mayank Raj, V. G. Narendra

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Ever since the DARPA challenge on autonomous vehicles in 2005, there has been a lot of buzz about ‘Autonomous Vehicles’ amongst the major tech giants such as Google, Uber, and Tesla. Numerous approaches have been adopted to solve this problem, which can have a long-lasting impact on mankind. In this paper, we have used Deep Learning techniques and TensorFlow framework with the goal of building a neural network model to predict (speed, acceleration, steering angle, and brake) features needed for navigation of autonomous vehicles. The Deep Neural Network has been trained on images and sensor data obtained from the comma.ai dataset. A heatmap was used to check for correlation among the features, and finally, four important features were selected. This was a multivariate regression problem. The final model had five convolutional layers, followed by five dense layers. Finally, the calculated values were tested against the labeled data, where the mean squared error was used as a performance metric.

Keywords: autonomous vehicles, deep learning, computer vision, artificial intelligence

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Authors: Jinming Ma, Tianbing Xia, Janusz Getta

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This paper presents the application of fuzzy set theory to implement of mobile advertisement anti-fraud systems. Mobile anti-fraud is a method aiming to identify mobile advertisement fraudsters. One of the main problems of mobile anti-fraud is the lack of evidence to prove a user to be a fraudster. In this paper, we implement an application by using fuzzy set theory to demonstrate how to detect cheaters. The advantage of our method is that the hardship in detecting fraudsters in small data samples has been avoided. We achieved this by giving each user a suspicious degree showing how likely the user is cheating and decide whether a group of users (like all users of a certain APP) together to be fraudsters according to the average suspicious degree. This makes the process more accurate as the data of a single user is too small to be predictable.

Keywords: mobile internet, advertisement, anti-fraud, fuzzy set theory

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Authors: Mingming Guo, Xiaozan Wang

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Introduction: The purpose of this study is to examine the correlation between the physical fitness of Chinese college students and their daily running habits (RH). Methods: A total of 718 college students from East China Normal University participated in this study (385 boys and 333 girls). Each participant participated in the Chinese Students’ Physical Fitness Test during the 2018-2019 school year. In addition, each student is also required to use the app to record all their running results during each run during the 2018-2019 school year. Researchers can query and export all running records through the app's management platform. Results: (1) The total number of kilometers run by the students showed a significant negative correlation with their vital capacity (VC), sitting body flexion (SBF), and long jump (LJ) (rᵥ

Keywords: college students, physical fitness, running habits, data mining

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Authors: John Gibbs, Benjamin Flanders, Dylan Pozorski, Weixuan Liu

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Weight Painting–adjusting the influence a skeletal joint has on a given vertex in a character mesh–is an arduous and time con- suming part of the 3D animation pipeline. This process generally requires a trained technical animator and many hours of work to complete. Our skiNNer plug-in, which works within Autodesk’s Maya 3D animation software, uses Machine Learning and data pro- cessing techniques to create a deep neural network model that can accomplish the weight painting task in seconds rather than hours for bipedal quasi-humanoid character meshes. In order to create a properly trained network, a number of challenges were overcome, including curating an appropriately large data library, managing an arbitrary 3D mesh size, handling arbitrary skeletal architectures, accounting for extreme numeric values (most data points are near 0 or 1 for weight maps), and constructing an appropriate neural network model that can properly capture the high frequency alter- ation between high weight values (near 1.0) and low weight values (near 0.0). The arrived at neural network model is a cross between a traditional CNN, deep residual network, and fully dense network. The resultant network captures the unusually hard-edged features of a weight map matrix, and produces excellent results on many bipedal models.

Keywords: 3d animation, animation, character, rigging, skinning, weight painting, machine learning, artificial intelligence, neural network, deep neural network

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Authors: Mohammad Saeed Bahramy

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Transition metal dichalcogenides have experienced a resurgence of interest in the past few years owing to their rich properties, ranging from metals and superconductors to strongly spin-orbit-coupled semiconductors and charge-density-wave systems. In all these cases, the transition metal d-electrons mainly determine the ground state properties. This presentation focuses on the chalcogen-derived states. Combining density-functional theory calculations with spin- and angle-resolved photoemission, it is shown that these states generically host a coexistence of type I and type II three-dimensional bulk Dirac fermions as well as ladders of topological surface states and surface resonances. It will be discussed how these naturally arise within a single p-orbital manifold as a general consequence of a trigonal crystal field, and as such can be expected across many compounds. Our finding opens a new route to design topological materials with advanced functionalities.

Keywords: topology, electronic structure, Dirac semimetals, transition metal dichalcogenides

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Authors: Laicheng Cao, Xiangqian Su, Youxiao Wu

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Feature selection is one of the important problems in network security, pattern recognition, data mining and other fields. In order to remove redundant features, effectively improve the detection speed of intrusion detection system, proposes a new feature selection method, which is based on the invasive weed optimization (IWO) algorithm and tabu search algorithm(TS). Use IWO as a global search, tabu search algorithm for local search, to improve the results of IWO algorithm. The experimental results show that the feature selection method can effectively remove the redundant features of network data information in feature selection, reduction time, and to guarantee accurate detection rate, effectively improve the speed of detection system.

Keywords: intrusion detection, feature selection, iwo, tabu search

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Authors: Carlos Guillermo Lloga Sanz, Maria del Carmen Tamayo Asef

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As an object of study in Cuban universities, cinema is still in its infancy. This is relevant considering the significance of cinema within the local political culture and its impact on countries of the region. Discussions about the medium have been carried out mainly in the field of film criticism. The objective of this article is to reflect on the divergences between film studies and film criticism taking into account formal and theoretical features and to explore the transcendence of this debate for the intellectual ambiance of the Island. Methodologically, the study relies on theoretical elaborations based on literature review and non-structure interviews with Cuban film critics and scholars. The study finds that the gradation proposed by the Anglo-Saxon tradition, where film studies are considered a “higher stage," compared to criticism and cinephilia, does not apply to the Cuban space. Instead, to assess the state of reflection on cinema in Cuba, it is essential to consider it a starry node traversed by epistemic, institutional, and geopolitical matrices.

Keywords: film studies, film criticism, Cuban cinema, Cuban film studies

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Authors: Bahram Fathi, Seyedhossein Sajadifar, Naser Khiabani

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Data envelopment analysis (DEA) has recently gained popularity in energy efficiency analysis. A common feature of the previously proposed DEA models for measuring energy efficiency performance is that they treat energy consumption as an input within a production framework without considering undesirable outputs. However, energy use results in the generation of undesirable outputs as byproducts of producing desirable outputs. Within a joint production framework of both desirable and undesirable outputs, this paper presents several DEA-type linear programming models for measuring energy efficiency performance. In addition to considering undesirable outputs, our models treat different energy sources as different inputs so that changes in energy mix could be accounted for in evaluating energy efficiency. The proposed models are applied to measure the energy efficiency performances of 12 OPEC countries and the results obtained are presented.

Keywords: energy efficiency, undesirable outputs, data envelopment analysis

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Authors: Maariyah Irfan

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Intro: Current cardiovascular (CV) care faces challenges such as low budgets and high hospital admission rates. This review aims to evaluate Big Data in CV healthcare management through the use of wearable devices in atrial fibrillation (AF) detection. AF may present intermittently, thus it is difficult for a healthcare professional to capture and diagnose a symptomatic rhythm. Methods: The iRhythm ZioPatch, AliveCor portable electrocardiogram (ECG), and Apple Watch were chosen for review due to their involvement in controlled clinical trials, and their integration with smartphones. The cost-effectiveness and AF detection of these devices were compared against the 12-lead ambulatory ECG (Holter monitor) that the NHS currently employs for the detection of AF. Results: The Zio patch was found to detect more arrhythmic events than the Holter monitor over a 2-week period. When patients presented to the emergency department with palpitations, AliveCor portable ECGs detected 6-fold more symptomatic events compared to the standard care group over 3-months. Based off preliminary results from the Apple Heart Study, only 0.5% of participants received irregular pulse notifications from the Apple Watch. Discussion: The Zio Patch and AliveCor devices have promising potential to be implemented into the standard duty of care offered by the NHS as they compare well to current routine measures. Nonetheless, companies must address the discrepancy between their target population and current consumers as those that could benefit the most from the innovation may be left out due to cost and access.

Keywords: atrial fibrillation, big data, cardiovascular healthcare management, wearable devices

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Authors: B. Zerouali, M. Kara, B. Hamaidi, H. Mahdjoub, S. Rouabhia

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In this paper, a safety analysis for crude oil tanks to prevent undesirable events that may cause catastrophic accidents. The estimation of the probability of damage to industrial systems is carried out through a series of steps, and in accordance with a specific methodology. In this context, this work involves developing an assessment tool and risk analysis at the level of crude oil tanks system, based primarily on identification of various potential causes of crude oil tanks fire and explosion by the use of Fault Tree Analysis (FTA), then improved risk modelling by Bayesian Networks (BNs). Bayesian approach in the evaluation of failure and quantification of risks is a dynamic analysis approach. For this reason, have been selected as an analytical tool in this study. Research concludes that the Bayesian networks have a distinct and effective method in the safety analysis because of the flexibility of its structure; it is suitable for a wide variety of accident scenarios.

Keywords: bayesian networks, crude oil tank, fault tree, prediction, safety

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Authors: Sultan A. Al Shaqaq

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Corrosion is a serious challenge for a piping system in our Gas and Oil Separation Plant (GOSP) that causes piping failures. Two GOSPs (Plant-A and Plant-B) observed chronic corrosion issue with an on-plot piping system that leads to having more piping replacement during the past years. Since it is almost impossible to avoid corrosion, it is becoming more obvious that managing the corrosion level may be the most economical resolution. Corrosion engineers are thus increasingly involved in approximating the cost of their answers to corrosion prevention, and assessing the useful life of the equipment. This case study covers the background of corrosion encountered in piping internally and externally in these two GOSPs. The collected piping replacement data from year of 2011 to 2014 was covered. These data showed the replicate corrosion levels in an on-plot piping system. Also, it is included the total piping replacement with drain lines system and other service lines in plants (Plant-A and Plant-B) at Saudi Aramco facility.

Keywords: gas and oil separation plant, on-plot piping, drain lines, Saudi Aramco

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Authors: Seyed Saeid Tabaee, Mohammad Afshari, Bahador Ziaeemehr, Omid Bahar

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Nowadays, using modern structural systems with specific capabilities, like Diagrid, is emerging around the world. In this paper, a new resisting system, a combination of both Diagrid axial behavior and proper seismic performance of regular moment frames in tall buildings, named 'Hybrid Diagrid' is presented. The scaled specimen of the suggested hybrid system was built and tested using IIEES shaking table. The natural frequency and structural response of the analytical model were updated with the real experimental results. In order to compare its performance with the traditional Diagrid and moment frame systems, time history analysis was carried out. Extensive analysis shows the efficient seismic responses and economical behavior of Hybrid Diagrid structure with respect to the other two systems.

Keywords: hybrid diagrid system, moment frame, shaking table, tall buildings, time history analysis

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Authors: Rasaq Bello, Grace Akintola Sunday, Yemi Sikiru Onifade

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Groundwater is required for the continuity of life and sustainability of the ecosystem. Hence, this research was purposed to assess groundwater quality for domestic use in Kaltungo Local Government Area, Gombe State. The work was also aimed at determining the thickness and resistivity of the topsoil, areas suitable for borehole construction, quality and potentials of groundwater in the study area. The study area extends from latitude N10015’38” - E11008’01” and longitude N10019’29” - E11013’05”. The data was acquired using the Vertical Electrical Sounding (VES) method and processed using IP12win software. Twenty (20) Vertical Electrical Soundings were carried out with a maximum current electrode separation (AB) of 150m. The VES curves generated from the data reveal that all the VES points have five to six subsurface layers. The first layer has a resistivity value of 7.5 to 364.1 Ωm and a thickness ranging from 0.8 to 7.4m, and the second layer has a resistivity value of 1.8 to 600.3 Ωm thickness ranging from 2.6 to 31.4m, the third layer has resistivity value of 23.3 to 564.4 Ωm thickness ranging from 10.3 to 77.8m, the fourth layer has resistivity value of 19.7 to 640.2 Ωm thickness ranging from 8.2m to 120.0m, the fifth layer has resistivity value of 27 to 234 Ωm thickness ranging from 8.2 to 53.7m and the six-layer is the layer that extended beyond the probing depth. The VES curves generated from the data revealed KQHA curve type for VES 1, HKQQ curve for VES 4, HKQ curve for VES 5, KHA curve for VES 11, QQHK curve for VES 12, HAA curve for VES 6 and VES 19, HAKH curve for VES 7, VES 8, VES 10 and VES 18, HKH curve for VES 2, VES 3, VES 9, VES 13, VES 14, VES 15, VES 16, VES 17 and VES 20. Values of the Coefficient of Anisotropy, Reflection Coefficient, and Resistivity Contrast obtained from the Dar-Zarrouk parameters indicated good water prospects for all the VES points in this study, with VES points 4, 9 and 18 having the highest prospects for groundwater exploration.

Keywords: formation parameters, groundwater, resistivity, resistivity contrast, vertical electrical sounding

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Authors: Joanne F. Hayek

Abstract:

This study explores the use of gamification to promote citizen e-participation in urban planning. The research departs from a case study: the ‘Shape Your City’ web app designed and programmed by the author and presented as part of the 2021 Dubai Design Week to engage citizens in the co-creation of the future of their city through a gamified experience. The paper documents the design and development methodology of the web app and concludes with the findings of its pilot release. The case study explores the use of mobile interactive mapping, real-time data visualization, augmented reality, and machine learning as tools to enable co-planning. The paper also details the user interface design strategies employed to integrate complex cross-sector e-planning systems and make them accessible to citizens.

Keywords: gamification, co-planning, citizen e-participation, mobile interactive mapping, real-time data visualization

Procedia PDF Downloads 130

Authors: S. H. Arnaud Kanga, O. Hili, S. Dabo-Niang

Abstract:

Spatial prediction is an issue appealing and attracting several fields such as agriculture, environmental sciences, ecology, econometrics, and many others. Although multiple non-parametric prediction methods exist for spatial data, those are based on the conditional expectation. This paper took a different approach by examining a non-parametric spatial predictor of the conditional quantile. The study especially observes the stationary multidimensional spatial process over a rectangular domain. Indeed, the proposed quantile is obtained by inverting the conditional distribution function. Furthermore, the proposed estimator of the conditional distribution function depends on three kernels, where one of them controls the distance between spatial locations, while the other two control the distance between observations. In addition, the almost complete convergence and the convergence in mean order q of the kernel predictor are obtained when the sample considered is alpha-mixing. Such approach of the prediction method gives the advantage of accuracy as it overcomes sensitivity to extreme and outliers values.

Keywords: conditional quantile, kernel, nonparametric, stationary

Procedia PDF Downloads 140

Authors: Milad Alizadeh Galdiani, Jabbar Ali Zakeri

Abstract:

Lateral movement of CWR ballasted track occurs in sharp curves because of the lack of adequate lateral resistance. Several strategies have been proposed and used for increase the lateral resistance of ballasted tracks, but still there are some problems in tracks with small radius curves. In this paper, a new method has been presented for increase the lateral resistance. This method is using the lateral supports as numerical and field studies. In this paper, the field and laboratory tests have been conducted by using the single tie pressure test (STPT) and track panel loading test (LTPT). Then, their results were compared with the numerical results. The results of numerical and field tests showed that the lateral stiffness of ballasted tracks significantly increased when there were lateral supports in ballasted tracks. Also, the track structure had a bilinear behavior.

Keywords: ballasted railway, Lateral resistance, railway buckling, field and numerical studies

Procedia PDF Downloads 313