Search results for: software cumulative failure prediction

Authors: Hooman Dabirmanesh, Mahmoud Ghazavi, Kazem Barkhordari

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A finite element modeling for determination of the bearing capacity of caissons subjected to inclined loads is presented in this paper. The model investigates the uplift capacity of the caisson with varying cross sectional area. To this aim, the behavior of the soil is assumed to be elasto-plastic, and its failure is controlled by Modified Cam-Clay failure criterion. The simulation takes into account the couple analysis. The approach is verified using available data from other research work especially centrifuge data. Parametric studies are subsequently performed to investigate the effect of contributing parameters such as aspect ratio of the caisson, the loading rate, the loading direction angle, and points where the external load is applied. In addition, the influence of the caisson geometry is taken into account. The results show the bearing capacity of the caisson increases with increasing the taper angle. Hence, the pullout capacity will increase using the same material. In addition, the bearing capacity of caissons strongly depends on the suction that is generated at tip and in sealed surface on top of caisson. Other results concerning the influencing factors will be presented.

Keywords: aspect ratio, finite element method, inclined load, modified Cam clay, taper angle, undrained condition

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Authors: Zabeehullah, Fahim Arif, Yawar Abbas

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Software Defined Network (SDN) is a next genera-tion networking model which simplifies the traditional network complexities and improve the utilization of constrained resources. Currently, most of the SDN based Internet of Things(IoT) environments use traditional network routing strategies which work on the basis of max or min metric value. However, IoT network heterogeneity, dynamic traffic flow and complexity demands intelligent and self-adaptive routing algorithms because traditional routing algorithms lack the self-adaptions, intelligence and efficient utilization of resources. To some extent, SDN, due its flexibility, and centralized control has managed the IoT complexity and heterogeneity but still Software Defined IoT (SDIoT) lacks intelligence. To address this challenge, we proposed a model called Deep Routing Strategy (DRS) which uses Deep Learning algorithm to perform routing in SDIoT intelligently and efficiently. Our model uses real-time traffic for training and learning. Results demonstrate that proposed model has achieved high accuracy and low packet loss rate during path selection. Proposed model has also outperformed benchmark routing algorithm (OSPF). Moreover, proposed model provided encouraging results during high dynamic traffic flow.

Keywords: SDN, IoT, DL, ML, DRS

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Authors: Amina Unis, Samah S. El Basateeny, Noha A. H. Nassef

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Introduction: Acute Renal Failure (ARF) is one of the most common problems encountered in hospitalized critically ill patients. In recent years great effort has been focused on the introduction of herbal medicine as a novel therapeutic agent for prevention of ARF. Hence, the current study was designed to investigate the effect of Green Coffee Bean Extract (GCBE) on gentamicin induced ARF in rats. Methods: The study was conducted on 60 male rats divided into six equal groups. Group 1 served as normal control group and GCBE was administered for 7 days at a dose of 20 mg/kg/day in group 2 and 40 mg/kg/day in group 3 to test the effect of GCBE on normal kidneys. ARF was induced by a daily intraperitoneal injection of gentamicin (80 mg/kg) for 7 days in group 4 (model group), group 5 (GCBE 20 mg/kg/day) and group 6 (GCBE 20 mg/kg/day). All rats were sacrificed after 7 days and blood was withdrawn for kidney function tests. Kidneys were removed for determination of renal oxidative stress markers and histopathological examination. Results: The present study showed that rats that received oral GCBE for 7 days without induction of ARF showed no significant change in all the assessed parameters in comparison to the normal control group, while rats in the groups that received oral GCBE for 7 days with induction of ARF showed a significant improvement in kidney functions tests (decrease in serum urea, serum creatinine, and blood urea nitrogen) when compared to the ARF model group. Moreover, there was significant amelioration in renal oxidative stress markers (renal malondialdehyde, renal superoxide dismutase) and renal histopathological changes in the GCBE treated groups along induction of ARF when compared to ARF model group. The most significant improvement was reported in the group where GCBE was administered for 7 days in a dose 40 mg/kg/day, along with induction of ARF. Conclusion: GCBE has a potential role in ameliorating renal damage involved in ARF mostly through its antioxidant effect.

Keywords: green coffee bean extract, gentamicin, acute renal failure, pharmacology

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Authors: Hesham Abd Elkhalek, Sherif M. Hafez, Yasser M. El Fahham

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Allocating limited budget to maintain bridge networks and selecting effective maintenance strategies for each bridge represent challenging tasks for maintenance managers and decision makers. In Egypt, bridges are continuously deteriorating. In many cases, maintenance works are performed due to user complaints. The objective of this paper is to develop a practical and reliable framework to manage the maintenance, repair, and rehabilitation (MR&R) activities of Bridges network considering performance and budget limits. The model solves an optimization problem that maximizes the average condition of the entire network given the limited available budget using Genetic Algorithm (GA). The framework contains bridge inventory, condition assessment, repair cost calculation, deterioration prediction, and maintenance optimization. The developed model takes into account multiple parameters including serviceability requirements, budget allocation, element importance on structural safety and serviceability, bridge impact on network, and traffic. A questionnaire is conducted to complete the research scope. The proposed model is implemented in software, which provides a friendly user interface. The framework provides a multi-year maintenance plan for the entire network for up to five years. A case study of ten bridges is presented to validate and test the proposed model with data collected from Transportation Authorities in Egypt. Different scenarios are presented. The results are reasonable, feasible and within acceptable domain.

Keywords: bridge management systems (BMS), cost optimization condition assessment, fund allocation, Markov chain

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Authors: C. U. Om Kumar, S. Kishore, A. Geetha

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An Operating system (OS) is software that manages computer hardware and software resources by providing services to computer programs. One of the important user expectations of the operating system is to provide the practice of defending information from unauthorized access, disclosure, modification, inspection, recording or destruction. Operating system is always vulnerable to the attacks of malwares such as computer virus, worm, Trojan horse, backdoors, ransomware, spyware, adware, scareware and more. And so the anti-virus software were created for ensuring security against the prominent computer viruses by applying a dictionary based approach. The anti-virus programs are not always guaranteed to provide security against the new viruses proliferating every day. To clarify this issue and to secure the computer system, our proposed expert system concentrates on authorizing the processes as wanted and unwanted by the administrator for execution. The Expert system maintains a database which consists of hash code of the processes which are to be allowed. These hash codes are generated using MD5 message-digest algorithm which is a widely used cryptographic hash function. The administrator approves the wanted processes that are to be executed in the client in a Local Area Network by implementing Client-Server architecture and only the processes that match with the processes in the database table will be executed by which many malicious processes are restricted from infecting the operating system. The add-on advantage of this proposed Expert system is that it limits CPU usage and minimizes resource utilization. Thus data and information security is ensured by our system along with increased performance of the operating system.

Keywords: virus, worm, Trojan horse, back doors, Ransomware, Spyware, Adware, Scareware, sticky software, process table, MD5, CPU usage and resource utilization

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Authors: Phillip Garrad, Saritha Unnikrishnan

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The recent popularity of connected and autonomous vehicles (CAV) corresponds with an increase in the risk of cyber-attacks. These cyber-attacks have been instigated by both researchers or white-coat hackers and cyber-criminals. As Connected Vehicles move towards full autonomy, the impact of these cyber-attacks also grows. The current research details challenges faced in cybersecurity testing of CAV, including access and cost of the representative test setup. Other challenges faced are lack of experts in the field. Possible solutions to how these challenges can be overcome are reviewed and discussed. From these findings, a software simulated CAV network is established as a cost-effective representative testbed. Penetration tests are then performed on this simulation, demonstrating a cyber-attack in CAV. Studies have shown Artificial Intelligence (AI) to improve runtime, increase efficiency and comprehensively cover all the typical test aspects in penetration testing in other industries. There is an attempt to introduce similar AI models to the software simulation. The expectation from this implementation is to see similar improvements in runtime and efficiency for the CAV model. If proven to be an effective means of penetration test for CAV, this methodology may be used on a full CAV test network.

Keywords: cybersecurity, connected vehicles, software simulation, artificial intelligence, penetration testing

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Authors: Paschal A. Ochang, Emmanuel C. Oji

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The Duffing’s Equation is a second order system that is very important because they are fundamental to the behaviour of higher order systems and they have applications in almost all fields of science and engineering. In the biological area, it is useful in plant stem dependence and natural frequency and model of the Brain Crash Analysis (BCA). In Engineering, it is useful in the study of Damping indoor construction and Traffic lights and to the meteorologist it is used in the prediction of weather conditions. However, most Problems in real life that occur are non-linear in nature and may not have analytical solutions except approximations or simulations, so trying to find an exact explicit solution may in general be complicated and sometimes impossible. Therefore we aim to find out if it is possible to obtain one analytical fixed point to the non-linear ordinary equation using fixed point analytical method. We started by exposing the scope of the Duffing’s equation and other related works on it. With a major focus on the fixed point and fixed point iterative scheme, we tried different iterative schemes on the Duffing’s Equation. We were able to identify that one can only see the fixed points to a Damped Duffing’s Equation and not to the Undamped Duffing’s Equation. This is because the cubic nonlinearity term is the determining factor to the Duffing’s Equation. We finally came to the results where we identified the stability of an equation that is damped, forced and second order in nature. Generally, in this research, we approximate the solution of Duffing’s Equation by converting it to a system of First and Second Order Ordinary Differential Equation and using Fixed Point Iterative approach. This approach shows that for different versions of Duffing’s Equations (damped), we find fixed points, therefore the order of computations and running time of applied software in all fields using the Duffing’s equation will be reduced.

Keywords: damping, Duffing's equation, fixed point analysis, second order differential, stability analysis

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Authors: N. Massoum, B. Bouazza

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In this paper, we have developed a numerical simulation model to describe the electrical properties of GaInP MESFET with submicron gate (Lg = 0.5 µm). This model takes into account the three-dimensional (3D) distribution of the load in the short channel and the law effect of mobility as a function of electric field. Simulation software based on a stochastic method such as Monte Carlo has been established. The results are discussed and compared with those of the experiment. The result suggests experimentally that, in a very small gate length in our devices (smaller than 40 nm), short-channel tunneling explains the degradation of transistor performance, which was previously enhanced by velocity overshoot.

Keywords: Monte Carlo simulation, transient electron transport, MESFET device, simulation software

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Authors: M. R. Martinez-Torres, S. L. Toral, M. Olmedilla

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Typically, virtual communities exhibit the well-known phenomenon of participation inequality, which means that only a small percentage of users is responsible of the majority of contributions. However, the sustainability of the community requires that the group of active users must be continuously nurtured with new users that gain expertise through a participation process. This paper analyzes the time evolution of Open Source Software (OSS) communities, considering users that join/abandon the community over time and several topological properties of the network when modeled as a social network. More specifically, the paper analyzes the role of those users rejoining the community and their influence in the global characteristics of the network.

Keywords: open source communities, social network Analysis, time series, virtual communities

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: Mia Françoise

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This report aims to develop a strategy for assisting short-term investors to benefit from the current economic climate in South Africa by utilizing technical analysis techniques and predictive analytics. As part of this research, value investing and technical analysis principles will be combined to maximize returns for South African investors while optimizing volatility. As an emerging market, South Africa offers many opportunities for high growth in sectors where other developed countries cannot grow at the same rate. Investing in South African companies with significant growth potential can be extremely rewarding. Although the risk involved is more significant in countries with less developed markets and infrastructure, there is more room for growth in these countries. According to recent research, the offshore market is expected to outperform the local market over the long term; however, short-term investments in the local market will likely be more profitable, as the Johannesburg Stock Exchange is predicted to outperform the S&P500 over the short term. The instabilities in the economy contribute to increased market volatility, which can benefit investors if appropriately utilized. Price prediction and portfolio optimization comprise the two primary components of this methodology. As part of this process, statistics and other predictive modeling techniques will be used to predict the future performance of stocks listed on the Johannesburg Stock Exchange. Following predictive data analysis, Modern Portfolio Theory, based on Markowitz's Mean-Variance Theorem, will be applied to optimize the allocation of assets within an investment portfolio. By combining different assets within an investment portfolio, this optimization method produces a portfolio with an optimal ratio of expected risk to expected return. This methodology aims to provide a short-term investment with a stock portfolio that offers the best risk-to-return profile for stocks listed on the JSE by combining price prediction and portfolio optimization.

Keywords: financial stocks, optimized asset allocation, prediction modelling, South Africa

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Authors: A. Maruf Aytekin

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We present our approach on using continuous delivery pattern for release management. One of the key practices of agile and lean teams is the continuous delivery of new features to stakeholders. The main benefits of this approach lie in the ability to release new applications rapidly which has real strategic impact on the competitive advantage of an organization. Organizations that successfully implement Continuous Delivery have the ability to evolve rapidly to support innovation, provide stable and reliable software in more efficient ways, decrease the amount of resources need for maintenance, and lower the software delivery time and costs. One of the objectives of this paper is to elaborate a case study where IT division of Central Securities Depository Institution (MKK) of Turkey apply Continuous Delivery pattern to improve release management process.

Keywords: automation, continuous delivery, deployment, release management

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Authors: Armin Solemanifar, Arthur Wilkinson, Kinjalkumar Patel

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Carbon fibre (CF) - polymer laminate composites have very low densities (approximately 40% lower than aluminium), high strength and high stiffness but in terms of toughness properties they often require modifications. For example, adding rubbers or thermoplastics toughening agents are common ways of improving the interlaminar fracture toughness of initially brittle thermoset composite matrices. The main aim of this project was to toughen CF-epoxy resin laminate composites using hybrid CF-fabrics incorporating Innegra™ a commercial highly-oriented polypropylene (PP) fibre, in which more than 90% of its crystal orientation is parallel to the fibre axis. In this study, the damage tolerance of hybrid (carbon-Innegra, CI) composites was investigated. Laminate composites were produced by resin-infusion using: pure CF fabric; fabrics with different ratios of commingled CI, and two different types of pure Innegra fabrics (Innegra 1 and Innegra 2). Dynamic mechanical thermal analysis (DMTA) was used to measure the glass transition temperature (Tg) of the composite matrix and values of flexural storage modulus versus temperature. Mechanical testing included drop-weight impact, compression-after-impact (CAI), and interlaminar (short-beam) shear strength (ILSS). Ultrasonic C-Scan imaging was used to determine the impact damage area and scanning electron microscopy (SEM) to observe the fracture mechanisms that occur during failure of the composites. For all composites, 8 layers of fabrics were used with a quasi-isotropic sequence of [-45°, 0°, +45°, 90°]s. DMTA showed the Tg of all composites to be approximately same (123 ±3°C) and that flexural storage modulus (before the onset of Tg) was the highest for the pure CF composite while the lowest were for the Innegra 1 and 2 composites. Short-beam shear strength of the commingled composites was higher than other composites, while for Innegra 1 and 2 composites only inelastic deformation failure was observed during the short-beam test. During impact, the Innegra 1 composite withstood up to 40 J without any perforation while for the CF perforation occurred at 10 J. The rate of reduction in compression strength upon increasing the impact energy was lowest for the Innegra 1 and 2 composites, while CF showed the highest rate. On the other hand, the compressive strength of the CF composite was highest of all the composites at all impacted energy levels. The predominant failure modes for Innegra composites observed in cross-sections of fractured specimens were fibre pull-out, micro-buckling, and fibre plastic deformation; while fibre breakage and matrix delamination were a major failure observed in the commingled composites due to the more brittle behaviour of CF. Thus, Innegra fibres toughened the CF composites but only at the expense of reducing compressive strength.

Keywords: hybrid composite, thermoplastic fibre, compression strength, damage tolerance

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Authors: Rabaa Tlili Yaakoubi, Hind Nakouri, Olivier Blanpain

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The current tools for real time management of sewer systems are based on two software tools: the software of weather forecast and the software of hydraulic simulation. The use of the first ones is an important cause of imprecision and uncertainty, the use of the second requires temporal important steps of decision because of their need in times of calculation. This way of proceeding fact that the obtained results are generally different from those waited. The major idea of the CARDIO project is to change the basic paradigm by approaching the problem by the "automatic" face rather than by that "hydrology". The objective is to make possible the realization of a large number of simulations at very short times (a few seconds) allowing to take place weather forecasts by using directly the real time meditative pluviometric data. The aim is to reach a system where the decision-making is realized from reliable data and where the correction of the error is permanent. A first model of control laws was realized and tested with different return-period rainfalls. The gains obtained in rejecting volume vary from 40 to 100%. The development of a new algorithm was then used to optimize calculation time and thus to overcome the subsequent combinatorial problem in our first approach. Finally, this new algorithm was tested with 16- year-rainfall series. The obtained gains are 60% of total volume rejected to the natural environment and of 80 % in the number of discharges.

Keywords: RTC, paradigm, optimization, automation

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Authors: Ji-Hoon Hong, Dong-Hee Kim, Nam-Uk Kim, Tai-Myoung Chung

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Smartphone users are increasing rapidly. Accordingly, many companies are running BYOD (Bring Your Own Device: Policies to bring private-smartphones to the company) policy to increase work efficiency. However, smartphones are always under the threat of malware, thus the company network that is connected smartphone is exposed to serious risks. Most smartphone malware detection techniques are to perform an independent detection (perform the detection of a single target application). In this paper, we analyzed a variety of intrusion detection techniques. Based on the results of analysis propose an agent using the network IDS.

Keywords: android malware detection, software-defined network, interaction environment, android malware detection, software-defined network, interaction environment

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Authors: Pierre van Tonder, Christoff Kruger

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Modular concrete elements are used for retaining walls to provide lateral support. Depending on the retaining wall layout, these precast panels may be interlocking and may be tied into the soil backfill via geosynthetic strips. This study investigates the ultimate pull-out load increase, which is possible by adding varied diameter supplementary reinforcement through embedded anchor loops within concrete retaining wall panels. Full-scale panels used in practice have four embedded anchor points. However, only one anchor loop was embedded in the center of the experimental panels. The experimental panels had the same thickness but a smaller footprint (600mm x 600mm x 140mm) area than the full-sized panels to accommodate the space limitations of the laboratory and experimental setup. The experimental panels were also cast without any bending reinforcement as would typically be obtained in the full-scale panels. The exclusion of these reinforcements was purposefully neglected to evaluate the impact of a single bar reinforcement through the center of the anchor loops. The reinforcement bars had of 8 mm, 10 mm, 12 mm, and 12 mm. 30 samples of concrete panels with embedded anchor loops were tested. The panels were supported on the edges and the anchor loops were subjected to an increasing tensile force using an Instron piston. Failures that occurred were loop failures and panel failures and a mixture thereof. There was an increase in ultimate load vs. increasing diameter as expected, but this relationship persisted until the reinforcement diameter exceeded 10 mm. For diameters larger than 10 mm, the ultimate failure load starts to decrease due to the dependency of the reinforcement bond strength to the concrete matrix. Overall, the reinforced panels showed a 14 to 23% increase in the factor of safety. Using anchor loops of 66kN ultimate load together with Y10 steel reinforcement with bent ends had shown the most promising results in reducing concrete panel pull-out failure. The Y10 reinforcement had shown, on average, a 24% increase in ultimate load achieved. Previous research has investigated supplementary reinforcement around the anchor loops. This paper extends this investigation by evaluating supplementary reinforcement placed through the panel anchor loops.

Keywords: supplementary reinforcement, anchor loops, retaining panels, reinforced concrete, pull-out failure

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Authors: Evgeniia Surkova, Manal Assaad, Hleb Makeyeu, Juho Makio

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The main objective of STIMEY (Science, Technology, Innovation, Mathematics, Engineering for the Young) project is the delivery of a hybrid learning environment that combines multi-level components such as social media concepts, robotic artefacts, and radio, among others. It is based on a well-researched pedagogical framework to attract European youths to STEM (science, technology, engineering, and mathematics) education and careers. To develop and integrate these various components, STIMEY is executed in iterative research cycles leading to progressive improvements. Scrum was the development methodology of choice in the project, as studies indicated its benefits as an agile methodology in global software development, especially of e-learning and integrated learning projects. This paper describes the project partners’ experience with the Scrum framework, discussing the challenges faced in its implementation and the mitigation practices employed. The authors conclude with exploring user experience tools and principles for future research, as a novel direction in supporting the Scrum development team.

Keywords: e-learning, global software development, scrum, STEM education

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Authors: Tobias Strübing, Fatemeh Ziaeetabar

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Humans usually manipulate objects with their hands. To represent these actions in a simple and understandable way, we need to use a semantic framework. For this purpose, the Semantic Event Chain (SEC) method has already been presented which is done by consideration of touching and non-touching relations between manipulated objects in a scene. This method was improved by a computational model, the so-called enriched Semantic Event Chain (eSEC), which incorporates the information of static (e.g. top, bottom) and dynamic spatial relations (e.g. moving apart, getting closer) between objects in an action scene. This leads to a better action prediction as well as the ability to distinguish between more actions. Each eSEC manipulation descriptor is a huge matrix with thirty rows and a massive set of the spatial relations between each pair of manipulated objects. The current eSEC framework has so far only been used in the category of manipulation actions, which eventually involve two hands. Here, we would like to extend this approach to a whole body action descriptor and make a conjoint activity representation structure. For this purpose, we need to do a statistical analysis to modify the current eSEC by summarizing while preserving its features, and introduce a new version called Enhanced eSEC or (e2SEC). This summarization can be done from two points of the view: 1) reducing the number of rows in an eSEC matrix, 2) shrinking the set of possible semantic spatial relations. To achieve these, we computed the importance of each matrix row in an statistical way, to see if it is possible to remove a particular one while all manipulations are still distinguishable from each other. On the other hand, we examined which semantic spatial relations can be merged without compromising the unity of the predefined manipulation actions. Therefore by performing the above analyses, we made the new e2SEC framework which has 20% fewer rows, 16.7% less static spatial and 11.1% less dynamic spatial relations. This simplification, while preserving the salient features of a semantic structure in representing actions, has a tremendous impact on the recognition and prediction of complex actions, as well as the interactions between humans and robots. It also creates a comprehensive platform to integrate with the body limbs descriptors and dramatically increases system performance, especially in complex real time applications such as human-robot interaction prediction.

Keywords: enriched semantic event chain, semantic action representation, spatial relations, statistical analysis

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Authors: Mohammad Sheikhalishahi, Vahid Ebrahimipour, Amir Hossein Radman-Kian

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This paper proposes an Ontology framework for knowledge modeling and representation of the equipment integrity process in a typical oil and gas production plant. Our aim is to construct a knowledge modeling that facilitates translation, interpretation, and conversion of human-readable integrity interpretation into computer-readable representation. The framework provides a function structure related to fault propagation using ISO 14224 and ISO 15926 OWL-Lite/ Resource Description Framework (RDF) to obtain a generic system-level model of asset integrity that can be utilized in the integrity engineering process during the equipment life cycle. It employs standard terminology developed by ISO 15926 and ISO 14224 to map textual descriptions of equipment failure and then convert it to a causality-driven logic by semantic interpretation and computer-based representation using Lite/RDF. The framework applied for an offshore gas riser. The result shows that the approach can cross-link the failure-related integrity words and domain-specific logic to obtain a representation structure of equipment integrity with causality inference based on semantic extraction of inspection report context.

Keywords: asset integrity modeling, interoperability, OWL, RDF/XML

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Authors: Siti Khadijah Che Osmi, Mohammed Ahmad Syed

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Underground structures are crucial components which required detailed analysis and design. Tunnels, for instance, are massively constructed as transportation infrastructures and utilities network especially in urban environments. Considering their prime importance to the economy and public safety that cannot be compromised, thus any instability to these tunnels will be highly detrimental to their performance. Recent experience suggests that tunnels become vulnerable during earthquakes and blast scenarios. However, a very limited amount of studies has been carried out to study and understanding the dynamic response and performance of underground tunnels under those unpredictable extreme hazards. In view of the importance of enhancing the resilience of these structures, the overall aims of the study are to evaluate probabilistic future performance of shallow tunnels subjected to seismic and blast loads by developing detailed fragility analysis. Critical non-linear time history numerical analyses using sophisticated finite element software Midas GTS NX have been presented about the current methods of analysis, taking into consideration of structural typology, ground motion and explosive characteristics, effect of soil conditions and other associated uncertainties on the tunnel integrity which may ultimately lead to the catastrophic failure of the structures. The proposed fragility curves for both extreme loadings are discussed and compared which provide significant information the performance of the tunnel under extreme hazards which may beneficial for future risk assessment and loss estimation.

Keywords: fragility analysis, seismic loads, shallow tunnels, blast loads

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Authors: Kristina Ancutiene

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The goal of this research is to perform comparative analysis between the virtual fit of the woman's dress and the fit on a real person. The dress fitting was done using mechanical and structural parameters of the 100 % linen fabric and using Modaris_3D_Fit software (CAD Lectra). The dress was also sawn after which garment fit differences of real and virtual dress was researched. Four respondents whose figures were similar were used to evaluate the ease and strain deformations of the real and virtual dress. The scores that were given by the respondents wearing the real dress were compared to the ease and strain results that were given by the software. The main result was that respondents feel similar to the virtual stretch deformations but their ease feeling is not always matching the virtual ones. The results may be influenced by psychological factors and different understanding about purpose of garment.

Keywords: virtual garment, 3D CAD, garment fit, mechanical properties

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Authors: Chen Li-Ching

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The multinomial logistic regression model is used popularly for inferring the relationship of risk factors and disease with multiple categories. This study based on the discrepancy between the nonparametric maximum likelihood estimator and semiparametric maximum likelihood estimator of the cumulative distribution function to propose a Kolmogorov-Smirnov type test statistic to assess adequacy of the multinomial logistic regression model for case-control data. A bootstrap procedure is presented to calculate the critical value of the proposed test statistic. Empirical type I error rates and powers of the test are performed by simulation studies. Some examples will be illustrated the implementation of the test.

Keywords: case-control studies, goodness-of-fit test, Kolmogorov-Smirnov test, multinomial logistic regression

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Authors: Xianjia Meng, Chuanyong Qu

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Compact bone produces fatigue damage under the inevitable physiological load. The accumulation of fatigue damage can change the bone’s micro-structure at different scales and cause the catastrophic failure eventually. However, most tests were limited to the macroscopic modulus of bone and there is a need to assess the microscopic modulus during fatigue progress. In this paper, nano-identation was used to investigate the bone specimen subjected to four point bending. The microscopic modulus of the same area were measured at different degrees of damage including fracture. So microscopic damage can be divided into three stages: first, the modulus decreased rapidly and then They fell slowly, before fracture the decline became fast again. After fracture, the average modulus decreased by 20%. The results of inner and outer planes explained the influence of compressive and tensile loads on modulus. Both the compressive and tensile moduli decreased with the accumulation of damage. They reached the minimum at ending and increased after fracture. The modulus evolution under different strains were revealed by the side. They all fell slowly and then fast with the accumulation of damage. The fractured results indicated that the elastic modulus decreased obviously at the high strain while decreased less at the low strain. During the fatigue progress, there was a significant difference in modulus at low degree of damage. However, the dispersed modulus tended to be similar at high degree of damage, but they became different again after the failure.

Keywords: fatigue damage, fracture, microscopic modulus, bone, nano-identation

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Authors: L. Ngowi, N. H. Mvungi

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This paper critically examines the evolution of socio-technical systems theory, its practices, and challenges in system design and development. It examines concepts put forward by researchers focusing on the application of the theory in software engineering. There are various methods developed that use socio-technical concepts based on systems engineering without remarkable success. The main constraint is the large amount of data and inefficient techniques used in the application of the concepts in system engineering for developing time-bound systems and within a limited/controlled budget. This paper critically examines each of the methods, highlight bottlenecks and suggest the way forward. Since socio-technical systems theory only explains what to do, but not how doing it, hence engineers are not using the concept to save time, costs and reduce risks associated with new frameworks. Hence, a new framework, which can be considered as a practical approach is proposed that borrows concepts from soft systems method, agile systems development and object-oriented analysis and design to bridge the gap between theory and practice. The approach will enable the development of systems using socio-technical systems theory to attract/enable the system engineers/software developers to use socio-technical systems theory in building worthwhile information systems to avoid fragilities and hostilities in the work environment.

Keywords: socio-technical systems, human centered design, software engineering, cognitive engineering, soft systems, systems engineering

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Authors: O. J. Akinsola, Yinan Zheng, Rose Anorlu, F. T. Ogunsola, Lifang Hou, Robert Leo-Murphy

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Cervical Cancer (CC) is the 2nd most common cancer among women living in low and middle-income countries, with no associated symptoms during formative periods. With the advancement and innovative medical research, there are numerous preventive measures being utilized, but the incidence of cervical cancer cannot be truncated with the application of only screening tests. The mortality associated with this invasive cervical cancer can be nipped in the bud through the important role of early-stage detection. This study research selected an array of different top features selection techniques which was aimed at developing a model that could validly diagnose the risk factors of cervical cancer. A retrospective clinic-based cohort study was conducted on 178 HIV-associated cervical cancer patients in Lagos University teaching Hospital, Nigeria (U54 data repository) in April 2022. The outcome measure was the automated prediction of the HIV-associated cervical cancer cases, while the predictor variables include: demographic information, reproductive history, birth control, sexual history, cervical cancer screening history for invasive cervical cancer. The proposed technique was assessed with R and Python programming software to produce the model by utilizing the classification algorithms for the detection and diagnosis of cervical cancer disease. Four machine learning classification algorithms used are: the machine learning model was split into training and testing dataset into ratio 80:20. The numerical features were also standardized while hyperparameter tuning was carried out on the machine learning to train and test the data. Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN). Some fitting features were selected for the detection and diagnosis of cervical cancer diseases from selected characteristics in the dataset using the contribution of various selection methods for the classification cervical cancer into healthy or diseased status. The mean age of patients was 49.7±12.1 years, mean age at pregnancy was 23.3±5.5 years, mean age at first sexual experience was 19.4±3.2 years, while the mean BMI was 27.1±5.6 kg/m2. A larger percentage of the patients are Married (62.9%), while most of them have at least two sexual partners (72.5%). Age of patients (OR=1.065, p<0.001**), marital status (OR=0.375, p=0.011**), number of pregnancy live-births (OR=1.317, p=0.007**), and use of birth control pills (OR=0.291, p=0.015**) were found to be significantly associated with HIV-associated cervical cancer. On top ten 10 features (variables) considered in the analysis, RF claims the overall model performance, which include: accuracy of (72.0%), the precision of (84.6%), a recall of (84.6%) and F1-score of (74.0%) while LR has: an accuracy of (74.0%), precision of (70.0%), recall of (70.0%) and F1-score of (70.0%). The RF model identified 10 features predictive of developing cervical cancer. The age of patients was considered as the most important risk factor, followed by the number of pregnancy livebirths, marital status, and use of birth control pills, The study shows that data mining techniques could be used to identify women living with HIV at high risk of developing cervical cancer in Nigeria and other sub-Saharan African countries.

Keywords: associated cervical cancer, data mining, random forest, logistic regression

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Authors: Omar Imine, Mohammed Aounallah, Mustapha Belkadi

Abstract:

Steady three-dimensional and two free surface waves generated by moving bodies are presented, the flow problem to be simulated is rich in complexity and poses many modeling challenges because of the existence of breaking waves around the ship hull, and because of the interaction of the two-phase flow with the turbulent boundary layer. The results of several simulations are reported. The first study was performed for NACA0012 of hydrofoil with different meshes, this section is analyzed at h/c= 1, 0345 for 2D. In the second simulation, a mathematically defined Wigley hull form is used to investigate the application of a commercial CFD code in prediction of the total resistance and its components from tangential and normal forces on the hull wetted surface. The computed resistance and wave profiles are used to estimate the coefficient of the total resistance for Wigley hull advancing in calm water under steady conditions. The commercial CFD software FLUENT version 12 is used for the computations in the present study. The calculated grid is established using the code computer GAMBIT 2.3.26. The shear stress k-ωSST model is used for turbulence modeling and the volume of the fluid technique is employed to simulate the free-surface motion. The second order upwind scheme is used for discretizing the convection terms in the momentum transport equations, the Modified HRICscheme for VOF discretization. The results obtained compare well with the experimental data.

Keywords: free surface flows, breaking waves, boundary layer, Wigley hull, volume of fluid

Procedia PDF Downloads 360

Authors: Misganaw Abebe Baye, Ji-Woo Park, Beom-Soo Kang

Abstract:

The product quality of multi-point dieless forming (MDF) is identified to be dependent on the process parameters. Moreover, a certain variation of friction and material properties may have a substantially worse influence on the final product quality. This study proposed on how to compensate the MDF product defects by minimizing the sensitivity of noise parameter variations. This can be attained by reliability-based robust optimization (RRO) technique to obtain the optimal process setting of the controllable parameters. Initially two MDF Finite Element (FE) simulations of AA3003-H14 saddle shape showed a substantial amount of dimpling, wrinkling, and shape error. FE analyses are consequently applied on ABAQUS commercial software to obtain the correlation between the control process setting and noise variation with regard to the product defects. The best prediction models are chosen from the family of metamodels to swap the computational expensive FE simulation. Genetic algorithm (GA) is applied to determine the optimal process settings of the control parameters. Monte Carlo Analysis (MCA) is executed to determine how the noise parameter variation affects the final product quality. Finally, the RRO FE simulation and the experimental result show that the amendment of the control parameters in the final forming process leads to a considerably better-quality product.

Keywords: dimpling, multi-point dieless forming, reliability-based robust optimization, shape error, variation, wrinkling

Procedia PDF Downloads 238

Authors: Tanzeel Ur Rehman Khan, Franz Josef Behr, Phillip Davis

Abstract:

Open source geospatial certifications are needed in geospatial technology education and industry sector. In parallel with proprietary software, free and open source software solutions become important in geospatial technology research and play an important role for the growth of the geospatial industry. ESRI, GISCI (GIS Certification Institute), ASPRS (American Society of Photogrammetry and remote sensing), and Meta spatial are offering certifications on proprietary and open source software. These are portfolio and competency based certifications depending on GIS Body of Knowledge (Bok). The analysis of these certification approaches might lead to the discovery of some gaps in them and will open a new way to develop certifications related to the geospatial open source (OS). This new certification will investigate the different geospatial competencies according to open source tools that help to identify geospatial professionals and strengthen the geospatial academic content. The goal of this research is to introduce a geospatial certification model based on geospatial technology competency model (GTCM).The developed certification will not only incorporate the importance of geospatial education and production of the geospatial competency-based workforce in universities and companies (private or public) as well as describe open source solutions with tools and technology. Job analysis, market analysis, survey analysis of this certification opens a new horizon for business as well.

Keywords: geospatial certification, open source, geospatial technology competency model, geoscience

Procedia PDF Downloads 537

Authors: Kihyon Kwon, Hyun T. Park, Gil Y. Chung, Sang-Hoon Lee

Abstract:

This paper focuses on assessing sloshing-induced overflow of the seismically-isolated nuclear tanks based on Fluid-Structure Interaction (FSI) analysis. Typically, fluid motion in the seismically-isolated nuclear tank systems may be rather amplified and even overflowed under earthquake. Sloshing-induced overflow in those structures has to be reliably assessed and predicted since it can often cause critical damages to humans and environments. FSI analysis is herein performed to compute the total cumulative overflowed water volume more accurately, by coupling ANSYS with CFX for structural and fluid analyses, respectively. The approach is illustrated on a nuclear liquid storage tank, Spent Fuel Pool (SFP), forgiven conditions under consideration: different liquid levels, Peak Ground Accelerations (PGAs), and post earthquakes.

Keywords: FSI analysis, seismically-isolated nuclear tank system, sloshing-induced overflow

Procedia PDF Downloads 459

Authors: Deog-Gyu Seo, Jin-Soo Ahn

Abstract:

Objectives: Recently, the development of adhesive primers on stable bonding between zirconia and resin cement has been on the increase. The bond strength of zirconia-resin cement can be effectively increased with the treatment of primer composed of the adhesive monomer that can chemically bond with the oxide layer, which forms on the surface of zirconia. 10-methacryloyloxydecyl dihydrogen phosphate (10-MDP) that contains phosphate ester and acidic monomer 4-methacryloxyethyl trimellitic anhydride(4-META) have been suggested as monomers that can form chemical bond with the surface oxide layer of zirconia. Also, these suggested monomers have proved to be effective zirconia surface treatment for bonding to resin cement. The purpose of this study is to evaluate the effects of primer treatment on the bond strength of Zirconia-resin cement by using three different kinds of primers on the market. Methods: Zirconia blocks were prepared into 60 disk-shaped specimens by using a diamond saw. Specimens were divided into four different groups: first three groups were treated with zirconiaLiner(Sun Medical Co., Ltd., Furutaka-cho, Moriyama, Shiga, Japan), Alloy primer (Kuraray Noritake Dental Inc., Sakaju, Kurashiki, Okayama, Japan), and Universal primer (Tokuyama dental Corp., Taitou, Taitou-ku, Tokyo, Japan) respectively. The last group was the control with no surface treatment. Dual cured resin cement (Biscem, Bisco Inc., Schaumburg, IL, USA) was luted to each group of specimens. And then, shear bond strengths were measured by universal tesing machine. The significance of the result was statistically analyzed by one-way ANOVA and Tukey test. The failure sites in each group were inspected under a magnifier. Results: Mean shear bond strength were 0.60, 1.39, 1.03, 1.38 MPa for control, Zirconia Liner (ZL), Alloy primer (AP), Universal primer (UP), respectively. Groups with application of each of the three primers showed significantly higher shear bond strength compared to the control group (p < 0.05). Among the three groups with the treatment, ZL and UP showed significantly higher shear bond strength than AP (p < 0.05), and there were no significant differences in mean shear bond strength between ZL and UP (p < 0.05). While the most specimens of control groups showed adhesive failure (80%), the most specimens of three primer-treated groups showed cohesive or mixed failure (80%).

Keywords: primer, resin cement, shear bond strength, zirconia

Procedia PDF Downloads 188