Search results for: software cumulative failure prediction

Authors: Gelacio JuáRez-Luna, Daniel Enrique GonzáLez-RamíRez, Enrique Tenorio-Montero

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Frame elements coupled with springs elements are used for modelling the development of hinges in segmented tunnels, the spring elements modelled the rotational, transversal and axial failure. These spring elements are equipped with constitutive models to include independently the moment, shear force and axial force, respectively. These constitutive models are formulated based on damage mechanics and experimental test reported in the literature review. The mesh of the segmented tunnels was discretized in the software GID, and the nonlinear analyses were carried out in the finite element software ANSYS. These analyses provide the capacity curve of the primary and secondary lining of a segmented tunnel. Two numerical examples of segmented tunnels show the capability of the spring elements to release energy by the development of hinges. The first example is a segmental concrete lining discretized with frame elements loaded until hinges occurred in the lining. The second example is a tunnel with primary and secondary lining, discretized with a double ring frame model. The outer ring simulates the segmental concrete lining and the inner ring simulates the secondary cast-in-place concrete lining. Spring elements also modelled the joints between the segments in the circumferential direction and the ring joints, which connect parallel adjacent rings. The computed load vs displacement curves are congruent with numerical and experimental results reported in the literature review. It is shown that the modelling of a tunnel with primary and secondary lining with frame elements and springs provides reasonable results and save computational cost, comparing with 2D or 3D models equipped with smeared crack models.

Keywords: damage, hinges, lining, tunnel

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Authors: Caihong Sun, Xizi Zhang

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Rating prediction is an important problem for recommender systems. The task is to predict the rating for an item that a user would give. Most of the existing algorithms for the task ignore the effect of negative ratings rated by users on items, but the negative ratings have a significant impact on users’ purchasing decisions in practice. In this paper, we present a rating prediction algorithm based on factorization machines that consider the effect of negative ratings inspired by Loss Aversion theory. The aim of this paper is to develop a concave and a convex negative disgust function to evaluate the negative ratings respectively. Experiments are conducted on MovieLens dataset. The experimental results demonstrate the effectiveness of the proposed methods by comparing with other four the state-of-the-art approaches. The negative ratings showed much importance in the accuracy of ratings predictions.

Keywords: factorization machines, feature engineering, negative ratings, recommendation systems

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Authors: Cau Tran, Tu Nguyen, Tien Pham

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Model-based design (MBD) is a mathematical and visual technique for addressing design issues in the fields of communications, signal processing, and complicated control systems. It is utilized in several automotive, aerospace, industrial, and motion control applications. Virtual models are at the center of the software development process with model based design. A method used in the creation of embedded software is model-based design. In this study, the LAT motor is modeled in a simulation environment, and the LAT motor control is designed with a cascade structure, a speed and current control loop, and a controller that is used in the next part. A PID structure serves as this controller. Based on techniques and motor parameters that match the design goals, the PID controller is created for the model using traditional design principles. The MBD approach will be used to build embedded software for motor control. The paper will be divided into three distinct sections. The first section will introduce the design process and the benefits and drawbacks of the MBD technique. The design of control software for LAT motors will be the main topic of the next section. The experiment's results are the subject of the last section.

Keywords: model based design, limited angle torque, intellectual property core, hardware description language, controller area network, user datagram protocol

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Authors: Mariana Lima, Rodrigo Silva, Victor Stange, Teodiano Bastos

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Due to the high reliability reached by DNA tests, since the 1980s this kind of test has allowed the identification of a growing number of criminal cases, including old cases that were unsolved, now having a chance to be solved with this technology. Currently, the use of genetic profiling databases is a typical method to increase the scope of genetic comparison. Forensic laboratories must process, analyze, and generate genetic profiles of a growing number of samples, which require time and great storage capacity. Therefore, it is essential to develop methodologies capable to organize and minimize the spent time for both biological sample processing and analysis of genetic profiles, using software tools. Thus, the present work aims the development of a software system solution for laboratories of forensics genetics, which allows sample, criminal case and local database management, minimizing the time spent in the workflow and helps to compare genetic profiles. For the development of this software system, all data related to the storage and processing of samples, workflows and requirements that incorporate the system have been considered. The system uses the following software languages: HTML, CSS, and JavaScript in Web technology, with NodeJS platform as server, which has great efficiency in the input and output of data. In addition, the data are stored in a relational database (MySQL), which is free, allowing a better acceptance for users. The software system here developed allows more agility to the workflow and analysis of samples, contributing to the rapid insertion of the genetic profiles in the national database and to increase resolution of crimes. The next step of this research is its validation, in order to operate in accordance with current Brazilian national legislation.

Keywords: database, forensic genetics, genetic analysis, sample management, software solution

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Authors: Mohammed T. Hayajneh

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Machining of Metal Matrix Composites (MMCs) is very significant process and has been a main problem that draws many researchers to investigate the characteristics of MMCs during different machining process. The poor machining properties of hard particles reinforced MMCs make drilling process a rather interesting task. Unlike drilling of conventional materials, many problems can be seriously encountered during drilling of MMCs, such as tool wear and cutting forces. Cutting tool wear is a very significant concern in industries. Cutting tool wear not only influences the quality of the drilled hole, but also affects the cutting tool life. Prediction the cutting tool life during drilling is essential for optimizing the cutting conditions. However, the relationship between tool life and cutting conditions, tool geometrical factors and workpiece material properties has not yet been established by any machining theory. In this research work, fuzzy subtractive clustering system has been used to model the cutting tool life in drilling of Al₂O₃ particle reinforced aluminum alloy composite to investigate of the effect of cutting conditions on cutting tool life. This investigation can help in controlling and optimizing of cutting conditions when the process parameters are adjusted. The built model for prediction the tool life is identified by using drill diameter, cutting speed, and cutting feed rate as input data. The validity of the model was confirmed by the examinations under various cutting conditions. Experimental results have shown the efficiency of the model to predict cutting tool life.

Keywords: composite, fuzzy, tool life, wear

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Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

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Authors: Fereshteh Akbari, Farrokh Jalali Mosalam, Ali Hedayatifar, Amirreza Aminjavaheri

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The soil nailing is an effective method to stabilize slopes and retaining structures. Consequently, the lateral and vertical displacement of retaining walls are important criteria to evaluate the safety risks of adjacent structures. This paper is devoted to the optimization problems of retaining walls based on ABAQOUS Software. The various parameters such as nail length, orientation, arrangement, horizontal spacing, and bond skin friction, on lateral and vertical displacement of retaining walls are investigated. In order to ensure accuracy, the mobilized shear stress acting around the perimeter of the nail-soil interface is also modeled in ABAQOUS software. The observed trend of results is compared to the previous researches.

Keywords: retaining walls, soil nailing, ABAQOUS software, lateral displacement, vertical displacement

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Authors: Ioannis P. Panapakidis, Marios N. Moschakis

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The Greek Energy Market is structured as a mandatory pool where the producers make their bid offers in day-ahead basis. The System Operator solves an optimization routine aiming at the minimization of the cost of produced electricity. The solution of the optimization problem leads to the calculation of the System Marginal Price (SMP). Accurate forecasts of the SMP can lead to increased profits and more efficient portfolio management from the producer`s perspective. Aim of this study is to provide a comparative analysis of various machine learning models such as artificial neural networks and neuro-fuzzy models for the prediction of the SMP of the Greek market. Machine learning algorithms are favored in predictions problems since they can capture and simulate the volatilities of complex time series.

Keywords: deregulated energy market, forecasting, machine learning, system marginal price

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Authors: Diego Romero, Fabio Rojas, J. Alejandro Urrego

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The sounding rockets are aerospace vehicles that were developed in the mid-20th century, and Colombia has been involved in research that was carried out with the aim of innovating with this technology. The rockets are university research programs with the collaboration of the local government, with a simple strategy, develop and reduce the greatest costs associated with the production of a kind type of technology. In this way, in this document presents the failure analysis of a solid rocket motor, with the real compatibly to reach the thermosphere with a low-cost fuel. This solid rocket motor is the latest development of the Uniandes Aerospace Project (PUA for its Spanish acronym), an undergraduate and postgraduate research group at Universidad de los Andes (Bogotá, Colombia), dedicated to incurring in this type of technology. This motor has been carried out on Candy-type solid fuel, which is a compound of potassium nitrate and sorbitol, and the investigation has allowed the production of solid motors powerful enough to reach space, and which represents a unique technological advance in Latin America and an important development in experimental rocketry.To outline the main points the explosion in a static test is an important to explore and demonstrate the ways to develop technology, methodologies, production and manufacturing, being a solid rocket motor with 30 kN of thrust. In conclusion, this analysis explores different fields such as: design, manufacture, materials, production, first fire and more, with different engineering tools with principal objective find root failure. Following the engineering analysis methodology, was possible to design a new version of motor, with learned lessons new manufacturing specification, therefore, when publishing this project, it is intended to be a reference for future research in this field and benefit the industry.

Keywords: candy propellant, candy rockets, explosion, failure analysis, static test, solid rocket motor

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Authors: Septiawan A. Saputro, Purnami Widyaningsih, Sutanto Sastraredja

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Measles is a disease which can spread caused by a virus and has been a priority’s Ministry of Health in Indonesia to be solved. Each infected person can be recovered and get immunity so that the spread of the disease can be constructed with susceptible infected recovered (SIR). To prevent the spread of measles transmission, the Ministry of Health holds vaccinations program. The aims of the research are to derive susceptible vaccinated infected recovered (SVIR) model, to determine the patterns of disease spread with SVIR model, and also to apply the SVIR model on the spread of measles in Indonesia. Based on the article, it can be concluded that the spread model of measles with vaccinations, that is SVIR model. It is a first-order differential equation system. The patterns of disease spread is determined by solution of the model. Based on that model Indonesia will be a measles-free nation in 2186 with the average of vaccinations scope about 88% and the average score of vaccinations failure about 4.9%. If it is simulated as Ministry of Health new programs with the average of vaccinations scope about 95% and the average score of vaccinations failure about 3%, then Indonesia will be a measles-free nation in 2184. Even with the average of vaccinations scope about 100% and no failure of vaccinations, Indonesia will be a measles-free nation in 2183. Indonesia’s target as a measles-free nation in 2020 has not been reached.

Keywords: measles, vaccination, susceptible infected recovered (SIR), susceptible vaccinated infected recovered (SVIR)

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Authors: Ntandazo Dlatu, Benjamin Longo-Mbenza, Andre Renzaho, Ruffin Appalata, Yolande Yvonne Valeria Matoumona Mavoungou, Mbenza Ben Longo, Kenneth Ekoru, Blaise Makoso, Gedeon Longo Longo

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Background: The gap between complexities related to the integration of Tuberculosis /HIV control and evidence-based knowledge motivated the initiation of the study. Therefore, the objective of this study was to explore correlations between national TB management guidelines, multiple deprivation indexes, quantiles, components and levels of Tuberculosis control programme using mathematical modeling in rural O.R. Tambo District Health Facilities, South Africa. Methods: The study design used mixed secondary data analysis and cross-sectional analysis between 2009 and 2013 across O.R Tambo District, Eastern Cape, South Africa using univariate/ bivariate analysis, linear multiple regression models, and multivariate discriminant analysis. Health inequalities indicators and component of an impediment to the tuberculosis control programme were evaluated. Results: In total, 62 400 records for TB notification were analyzed for the period 2009-2013. There was a significant but negative between Financial Year Expenditure (r= -0.894; P= 0.041) Seropositive HIV status(r= -0.979; P= 0.004), Population Density (r = -0.881; P= 0.048) and the number of TB defaulter in all TB cases. It was shown unsuccessful control of TB management program through correlations between numbers of new PTB smear positive, TB defaulter new smear-positive, TB failure all TB, Pulmonary Tuberculosis case finding index and deprivation-concentration-dispersion index. It was shown successful TB program control through significant and negative associations between declining numbers of death in co-infection of HIV and TB, TB deaths all TB and SMIAD gradient/ deprivation-concentration-dispersion index. The multivariate linear model was summarized by unadjusted r of 96%, adjusted R2 of 95 %, Standard Error of estimate of 0.110, R2 changed of 0.959 and significance for variance change for P=0.004 to explain the prediction of TB defaulter in all TB with equation y= 8.558-0.979 x number of HIV seropositive. After adjusting for confounding factors (PTB case finding the index, TB defaulter new smear-positive, TB death in all TB, TB defaulter all TB, and TB failure in all TB). The HIV and TB death, as well as new PTB smear positive, were identified as the most important, significant, and independent indicator to discriminate most deprived deprivation index far from other deprivation quintiles 2-5 using discriminant analysis. Conclusion: Elimination of poverty such as overcrowding, lack of sanitation and environment of highest burden of HIV might end the TB threat in O.R Tambo District, Eastern Cape, South Africa. Furthermore, ongoing adequate budget comprehensive, holistic and collaborative initiative towards Sustainable Developmental Goals (SDGs) is necessary for complete elimination of TB in poor O.R Tambo District.

Keywords: tuberculosis, HIV/AIDS, success, failure, control program, health inequalities, South Africa

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Authors: Narayan Gurung, Fawu Wang, Ranjan Kumar Dahal

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Nepal is highly prone to earthquakes and has witnessed at least one major earthquake in 80 to 90 years interval. The Gorkha earthquake, that measured 7.8 RS in magnitude and struck Nepal on 25th April 2015, after 81 years since Mw 8.3 Nepal Bihar earthquake in 1934, was the largest earthquake after Mw 8.3 Nepal Bihar earthquake. In this paper, an attempt has been made to highlight the lessons learnt from the MwW 7.8 Gorkha (Nepal) earthquake. Several types of damage patterns in buildings were observed for reinforced concrete buildings, as well as for unreinforced masonry and adobe houses in the earthquake of 25 April 2015. Many field visits in the affected areas were conducted, and thus, associated failure and damage patterns were identified and analyzed. Damage patterns in non-engineered buildings, middle and high-rise buildings, commercial complexes, administrative buildings, schools and other critical facilities are also included from the affected districts. For most buildings, the construction and structural deficiencies have been identified as the major causes of failure; however, topography, local soil amplification, foundation settlement, liquefaction associated damages and buildings built in hazard-prone areas were also significantly observed for the failure or damages to buildings and hence are reported. Finally, the lessons learnt from Mw 7.8 Gorkha (Nepal) earthquake are presented in order to mitigate impacts of future earthquakes in Nepal.

Keywords: Gorkha earthquake, reinforced concrete structure, Nepal, lesson learnt

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Authors: Venencia Albert, Arulselvi Subramanian, Hara Prasad Pati, Asok K. Mukhophadhyay

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Acute traumatic coagulopathy in an endogenous dysregulation of the intrinsic coagulation system in response to the injury, associated with three-fold risk of poor outcome, and is more amenable to corrective interventions, subsequent to early identification and management. Multiple definitions for stratification of the patients' risk for early acute coagulopathy have been proposed, with considerable variations in the defining criteria, including several trauma-scoring systems based on prehospital data. We aimed to develop a clinically relevant definition for acute coagulopathy of trauma based on conventional coagulation assays and to assess its efficacy in comparison to recently established prehospital prediction models. Methodology: Retrospective data of all trauma patients (n = 490) presented to our level I trauma center, in 2014, was extracted. Receiver operating characteristic curve analysis was done to establish cut-offs for conventional coagulation assays for identification of patients with acute traumatic coagulopathy was done. Prospectively data of (n = 100) adult trauma patients was collected and cohort was stratified by the established definition and classified as "coagulopathic" or "non-coagulopathic" and correlated with the Prediction of acute coagulopathy of trauma score and Trauma-Induced Coagulopathy Clinical Score for identifying trauma coagulopathy and subsequent risk for mortality. Results: Data of 490 trauma patients (average age 31.85±9.04; 86.7% males) was extracted. 53.3% had head injury, 26.6% had fractures, 7.5% had chest and abdominal injury. Acute traumatic coagulopathy was defined as international normalized ratio ≥ 1.19; prothrombin time ≥ 15.5 s; activated partial thromboplastin time ≥ 29 s. Of the 100 adult trauma patients (average age 36.5±14.2; 94% males), 63% had early coagulopathy based on our conventional coagulation assay definition. Overall prediction of acute coagulopathy of trauma score was 118.7±58.5 and trauma-induced coagulopathy clinical score was 3(0-8). Both the scores were higher in coagulopathic than non-coagulopathic patients (prediction of acute coagulopathy of trauma score 123.2±8.3 vs. 110.9±6.8, p-value = 0.31; trauma-induced coagulopathy clinical score 4(3-8) vs. 3(0-8), p-value = 0.89), but not statistically significant. Overall mortality was 41%. Mortality rate was significantly higher in coagulopathic than non-coagulopathic patients (75.5% vs. 54.2%, p-value = 0.04). High prediction of acute coagulopathy of trauma score also significantly associated with mortality (134.2±9.95 vs. 107.8±6.82, p-value = 0.02), whereas trauma-induced coagulopathy clinical score did not vary be survivors and non-survivors. Conclusion: Early coagulopathy was seen in 63% of trauma patients, which was significantly associated with mortality. Acute traumatic coagulopathy defined by conventional coagulation assays (international normalized ratio ≥ 1.19; prothrombin time ≥ 15.5 s; activated partial thromboplastin time ≥ 29 s) demonstrated good ability to identify coagulopathy and subsequent mortality, in comparison to the prehospital parameter-based scoring systems. Prediction of acute coagulopathy of trauma score may be more suited for predicting mortality rather than early coagulopathy. In emergency trauma situations, where immediate corrective measures need to be taken, complex multivariable scoring algorithms may cause delay, whereas coagulation parameters and conventional coagulation tests will give highly specific results.

Keywords: trauma, coagulopathy, prediction, model

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Authors: Amirah Azzeri, Fatiha H. Shabaruddin, Scott A. McDonald, Rosmawati Mohamed, Maznah Dahlui

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Objective: A scaled-up treatment cascade with direct-acting antiviral (DAA) therapy is necessary to achieve global WHO targets for hepatitis C virus (HCV) elimination in Malaysia. Recently, limited access to Sofosbuvir/Daclatasvir (SOF/DAC) is available through compulsory licensing, with future access to Sofosbuvir/Velpatasvir (SOF/VEL) expected through voluntary licensing due to recent agreements. SOF/VEL has superior clinical outcomes, particularly for cirrhotic stages, but has higher drug acquisition costs compared to SOF/DAC. It has been proposed that a stratified treatment cascade might be the most cost-efficient approach for Malaysia whereby all HCV patients are treated with SOF/DAC except for patients with cirrhosis who are treated with SOF/VEL. This study aimed to conduct a five-year budget impact analysis from the provider perspective of the proposed stratified treatment cascade for HCV treatment in Malaysia. Method: A disease progression model that was developed based on model-predicted HCV epidemiology data in Malaysia was used for the analysis, where all HCV patients in scenario A were treated with SOF/DAC for all disease stages while in scenario B, SOF/DAC was used only for non-cirrhotic patients and SOF/VEL was used for the cirrhotic patients. The model projections estimated the annual numbers of patients in care and the numbers of patients to be initiated on DAA treatment nationally. Healthcare costs associated with DAA therapy and disease stage monitoring was included to estimate the downstream cost implications. For scenario B, the estimated treatment uptake of SOF/VEL for cirrhotic patients were 25%, 50%, 75%, 100% and 100% for 2018, 2019, 2020, 2021 and 2022 respectively. Healthcare costs were estimated based on standard clinical pathways for DAA treatment described in recent guidelines. All costs were reported in US dollars (conversion rate US$1=RM4.09, the price year 2018). Scenario analysis was conducted for 5% and 10% reduction of SOF/VEL acquisition cost anticipated from the competitive market pricing of generic DAA in Malaysia. Results: The stratified treatment cascade with SOF/VEL in Scenario B was found to be cost-saving compared to Scenario A. A substantial portion of the cost reduction was due to the costs associated with DAA therapy which resulted in USD 40 thousand (year 1) to USD 443 thousand (year 5) savings annually, with cumulative savings of USD 1.1 million after 5 years. Cost reductions for disease stage monitoring were seen in year three onwards which resulted in cumulative savings of USD 1.1 thousand. Scenario analysis estimated cumulative savings of USD 1.24 to USD 1.35 million when the acquisition cost of SOF/VEL was reduced. Conclusion: A stratified treatment cascade with SOF/VEL was expected to be cost-saving and can results in a budget impact reduction in overall healthcare expenditure in Malaysia compared to treatment with SOF/DAC. The better clinical efficacy with SOF/VEL is expected to halt patients’ HCV disease progression and may reduce downstream costs of treating advanced disease stages. The findings of this analysis may be useful to inform healthcare policies for HCV treatment in Malaysia.

Keywords: Malaysia, direct acting antiviral, compulsory licensing, voluntary licensing

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Authors: Wade Ghribi, Abdelmoty M. Ahmed, Ahmed Said Badawy, Belgacem Bouallegue

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In higher education institutions, the most pressing priority is to improve student performance and retention. Large volumes of student data are used in Educational Data Mining techniques to find new hidden information from students' learning behavior, particularly to uncover the early symptom of at-risk pupils. On the other hand, data with noise, outliers, and irrelevant information may provide incorrect conclusions. By identifying features of students' data that have the potential to improve performance prediction results, comparing and identifying the most appropriate ensemble learning technique after preprocessing the data, and optimizing the hyperparameters, this paper aims to develop a reliable students' performance prediction model for Higher Education Institutions. Data was gathered from two different systems: a student information system and an e-learning system for undergraduate students in the College of Computer Science of a Saudi Arabian State University. The cases of 4413 students were used in this article. The process includes data collection, data integration, data preprocessing (such as cleaning, normalization, and transformation), feature selection, pattern extraction, and, finally, model optimization and assessment. Random Forest, Bagging, Stacking, Majority Vote, and two types of Boosting techniques, AdaBoost and XGBoost, are ensemble learning approaches, whereas Decision Tree, Support Vector Machine, and Artificial Neural Network are supervised learning techniques. Hyperparameters for ensemble learning systems will be fine-tuned to provide enhanced performance and optimal output. The findings imply that combining features of students' behavior from e-learning and students' information systems using Majority Vote produced better outcomes than the other ensemble techniques.

Keywords: educational data mining, student performance prediction, e-learning, classification, ensemble learning, higher education

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Authors: Valery Gulyayev, Sergey Glazunov, Elena Andrusenko, Nataliya Shlyun

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Enhancement of technology and techniques for drilling deep directed oil and gas bore-wells are of essential industrial significance because these wells make it possible to increase their productivity and output. Generally, they are used for drilling in hard and shale formations, that is why their drivage processes are followed by the emergency and failure effects. As is corroborated by practice, the principal drilling drawback occurring in drivage of long curvilinear bore-wells is conditioned by the need to obviate essential force hindrances caused by simultaneous action of the gravity, contact and friction forces. Primarily, these forces depend on the type of the technological regime, drill string stiffness, bore-hole tortuosity and its length. They can lead to the Eulerian buckling of the drill string and its sticking. To predict and exclude these states, special mathematic models and methods of computer simulation should play a dominant role. At the same time, one might note that these mechanical phenomena are very complex and only simplified approaches (‘soft string drag and torque models’) are used for their analysis. Taking into consideration that now the cost of directed wells increases essentially with complication of their geometry and enlargement of their lengths, it can be concluded that the price of mistakes of the drill string behavior simulation through the use of simplified approaches can be very high and so the problem of correct software elaboration is very urgent. This paper deals with the problem of simulating the regimes of drilling deep curvilinear bore-wells with prescribed imperfect geometrical trajectories of their axial lines. On the basis of the theory of curvilinear flexible elastic rods, methods of differential geometry, and numerical analysis methods, the 3D ‘stiff-string drag and torque model’ of the drill string bending and the appropriate software are elaborated for the simulation of the tripping in and out regimes and drilling operations. It is shown by the computer calculations that the contact and friction forces can be calculated and regulated, providing predesigned trouble-free modes of operation. The elaborated mathematic models and software can be used for the emergency situations prognostication and their exclusion at the stages of the drilling process design and realization.

Keywords: curvilinear drilling, drill string tripping in and out, contact forces, resistance forces

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Authors: Celso L. Fernando, Toshio Yoshii, Takahiro Tsubota

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Understanding the causes of a road accident and predicting their occurrence is key to preventing deaths and serious injuries from road accident events. Traditional statistical methods such as the Poisson and the Logistics regressions have been used to find the association of the traffic environmental factors with the accident occurred; recently, an artificial neural network, ANN, a computational technique that learns from historical data to make a more accurate prediction, has emerged. Although the ability to make accurate predictions, the ANN has difficulty dealing with highly unbalanced attribute patterns distribution in the training dataset; in such circumstances, the ANN treats the minority group as noise. However, in the real world data, the minority group is often the group of interest; e.g., in the road traffic accident data, the events of the accident are the group of interest. This study proposes a combination of the k-means with the ANN to improve the predictive ability of the neural network model by alleviating the effect of the unbalanced distribution of the attribute patterns in the training dataset. The results show that the proposed method improves the ability of the neural network to make a prediction on a highly unbalanced distributed attribute patterns dataset; however, on an even distributed attribute patterns dataset, the proposed method performs almost like a standard neural network.

Keywords: accident risks estimation, artificial neural network, deep learning, k-mean, road safety

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Authors: Yilin Liao, Hewen Li, Paula McConvey

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Speed skaters often face a risk of concussion when they fall on the ice floor and impact crash mats during practices and competitive races. Several variables, including those related to the skater, the crash mat, and the impact position (body side/head/feet impact), are believed to influence the severity of the skater's concussion. While computer simulation modeling can be employed to analyze these accidents, the simulation process is time-consuming and does not provide rapid information for coaches and teams to assess the skater's injury risk in competitive events. This research paper promotes the exploration of the feasibility of using AI techniques for evaluating skater’s potential concussion severity, and to develop a fast concussion prediction tool using artificial neural networks to reduce the risk of treatment delays for injured skaters. The primary data is collected through virtual tests and physical experiments designed to simulate skater-mat impact. It is then analyzed to identify patterns and correlations; finally, it is used to train and fine-tune the artificial neural networks for accurate prediction. The development of the prediction tool by employing machine learning strategies contributes to the application of AI methods in sports science and has theoretical involvements for using AI techniques in predicting and preventing sports-related injuries.

Keywords: artificial neural networks, concussion, machine learning, impact, speed skater

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Authors: Yilin Liao, Hewen Li, Paula McConvey

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Concussion for speed skaters often occurs when skaters fall on the ice and impact the crash mats during practices and competition races. Gaining insight into the impact of interactions is of essential interest as it is directly related to skaters’ potential health risks and injuries. Precise concussion measurements are challenging and very difficult, making computer simulation the only reliable way to analyze accidents. This research aims to create the crash mat and skater’s multi-body model using Solidworks, develop a computer simulation model for skater-mat impact using ANSYS software, and predict the skater’s concussion degree by evaluating the “head injury criteria” (HIC) through the resulting accelerations. The developed method and results help understand the relationship between impact parameters and concussion risk for speed skaters and inform the design of crash mats and skating rink layouts more specifically by considering athletes’ health risks.

Keywords: computer simulation modeling, concussion, impact, speed skater

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Authors: Mahshid Arabi

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In recent years, remarkable advancements in the fields of artificial intelligence and machine learning have led to the development of facial recognition technologies. These technologies are now employed in a wide range of applications, including security, surveillance, healthcare, and education. In the field of education, the identification of university entrance exam candidates has been one of the fundamental challenges. Traditional methods such as using ID cards and handwritten signatures are not only inefficient and prone to fraud but also susceptible to errors. In this context, utilizing advanced technologies like facial recognition can be an effective and efficient solution to increase the accuracy and reliability of identity verification in entrance exams. This article examines the use of FaceMatch software for recognizing the faces of university entrance exam candidates in Iran. The main objective of this research is to evaluate the efficiency and accuracy of FaceMatch software in identifying university entrance exam candidates to prevent fraud and ensure the authenticity of individuals' identities. Additionally, this research investigates the advantages and challenges of using this technology in Iran's educational systems. This research was conducted using an experimental method and random sampling. In this study, 1000 university entrance exam candidates in Iran were selected as samples. The facial images of these candidates were processed and analyzed using FaceMatch software. The software's accuracy and efficiency were evaluated using various metrics, including accuracy rate, error rate, and processing time. The research results indicated that FaceMatch software could accurately identify candidates with a precision of 98.5%. The software's error rate was less than 1.5%, demonstrating its high efficiency in facial recognition. Additionally, the average processing time for each candidate's image was less than 2 seconds, indicating the software's high efficiency. Statistical evaluation of the results using precise statistical tests, including analysis of variance (ANOVA) and t-test, showed that the observed differences were significant, and the software's accuracy in identity verification is high. The findings of this research suggest that FaceMatch software can be effectively used as a tool for identifying university entrance exam candidates in Iran. This technology not only enhances security and prevents fraud but also simplifies and streamlines the exam administration process. However, challenges such as preserving candidates' privacy and the costs of implementation must also be considered. The use of facial recognition technology with FaceMatch software in Iran's educational systems can be an effective solution for preventing fraud and ensuring the authenticity of university entrance exam candidates' identities. Given the promising results of this research, it is recommended that this technology be more widely implemented and utilized in the country's educational systems.

Keywords: facial recognition, FaceMatch software, Iran, university entrance exam

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Authors: Ganbat Danaa, Chuluundorj Puntsag

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The steel structures of the Oyu-Tolgoi mining Concentrator plant are corroded during operation, which raises doubts about the continued use of some important structures of the plant, which is one of the problems facing the plant's regular operation. As a part of the main operation of the plant, the bottom part of the pressure vessel, which plays an important role in the reliable operation of the concentrate filter-drying unit, was heavily corroded, so it was necessary to study by engineering calculations, modeling, and simulation using modern advanced engineering programs and methods. The purpose of this research is to investigate whether the corroded part of the pressure vessel can be used normally in the future using advanced engineering software and to predetermine the remaining life of the time of the pressure vessel based on engineering calculations. When the thickness of the bottom part of the pressure vessel was thinned by 0.5mm due to corrosion detected by non-destructive testing, finite element analysis using ANSYS WorkBench software was used to determine the mechanical stress, strain and safety factor in the wall and bottom of the pressure vessel operating under 2.2 MPa working pressure, made conclusions on whether it can be used in the future. According to the recommendations, by using sand-blast cleaning and anti-corrosion paint, the normal, continuous and reliable operation of the Concentrator plant can be ensured, such as ordering new pressure vessels and reducing the installation period. By completing this research work, it will be used as a benchmark for assessing the corrosion condition of steel parts of pressure vessels and other metallic and non-metallic structures operating under severe conditions of corrosion, static and dynamic loads, and other deformed steels to make analysis of the structures and make it possible to evaluate and control the integrity and reliable operation of the structures.

Keywords: corrosion, non-destructive testing, finite element analysis, safety factor, structural reliability

Procedia PDF Downloads 43

Authors: Amit Kumar Verma

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The present study deals with fire prediction model in Terai Arc Landscape, one of the most dramatic ecosystems in Asia where large, wide-ranging species such as tiger, rhinos, and elephant will thrive while bringing economic benefits to the local people. Forest fires cause huge economic and ecological losses and release considerable quantities of carbon into the air and is an important factor inflating the global burden of carbon emissions. Forest fire is an important factor of behavioral cum ecological habit of tiger in wild. Post fire changes i.e. micro and macro habitat directly affect the tiger habitat or land. Vulnerability of fire depicts the changes in microhabitat (humus, soil profile, litter, vegetation, grassland ecosystem). Microorganism like spider, annelids, arthropods and other favorable microorganism directly affect by the forest fire and indirectly these entire microorganisms are responsible for the development of tiger (Panthera tigris) habitat. On the other hand, fire brings depletion in prey species and negative movement of tiger from wild to human- dominated areas, which may leads the conflict i.e. dangerous for both tiger & human beings. Early forest fire prediction through mapping the risk zones can help minimize the fire frequency and manage forest fires thereby minimizing losses. Satellite data plays a vital role in identifying and mapping forest fire and recording the frequency with which different vegetation types are affected. Thematic hazard maps have been generated by using IDW technique. A prediction model for fire occurrence is developed for TAL. The fire occurrence records were collected from state forest department from 2000 to 2014. Disciminant function models was used for developing a prediction model for forest fires in TAL, random points for non-occurrence of fire have been generated. Based on the attributes of points of occurrence and non-occurrence, the model developed predicts the fire occurrence. The map of predicted probabilities classified the study area into five classes very high (12.94%), high (23.63%), moderate (25.87%), low(27.46%) and no fire (10.1%) based upon the intensity of hazard. model is able to classify 78.73 percent of points correctly and hence can be used for the purpose with confidence. Overall, also the model works correctly with almost 69% of points. This study exemplifies the usefulness of prediction model of forest fire and offers a more effective way for management of forest fire. Overall, this study depicts the model for conservation of tiger’s natural habitat and forest conservation which is beneficial for the wild and human beings for future prospective.

Keywords: fire prediction model, forest fire hazard, GIS, landsat, MODIS, TAL

Procedia PDF Downloads 340

Authors: Hanan Hayat, Russell Lock

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The importance of usability in ensuring software quality has been well established in literature and widely accepted by software development practitioners. Consequently, numerous usability evaluation methods have been developed. However, the availability of large variety of evaluation methods alongside insufficient studies that critically analyse them resulted in an ambiguous process of selection amongst non-usability-expert practitioners. This study investigates the factors affecting the selection of usability evaluation methods within a project by interviewing a software development team. The results of the data gathered are then analysed and integrated in developing a framework. The framework developed poses a solution to the selection processes of usability evaluation methods by adjusting to individual projects resources and goals. It has the potential to be further evaluated to verify its applicability and usability within the domain of this study.

Keywords: usability evaluation, evaluating usability in non-user entered designs, usability evaluation methods (UEM), usability evaluation in projects

Procedia PDF Downloads 144

Authors: Dyana Zainudin, Atta Ur-Rahman, Thaier Hamed

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This paper explains about human nature concept as to understand the significance of information security in employees’ mentality including leaders in an organisation. By studying on a theory concept of the latest Von Solms fourth waves, information security governance basically refers to the concept of a set of methods, techniques and tools that responsible for protecting resources of a computer system to ensure service availability, confidentiality and integrity of information. However, today’s information security dilemma relates to the acceptance of employees mentality. The major causes are a lack of communication and commitment. These types of management in an organisation are labelled as immoral/amoral management which effects on information security compliance. A recovery action is taken based on ‘learn a lesson from incident events’ rather than prevention. Therefore, the paper critically analysed the Von Solms fourth waves’ theory with current human events and its correlation by studying secondary data and also from qualitative analysis among employees in public sectors. ‘Three-dimensions to failure’ of information security dilemma are explained as deny, don’t know and don’t care. These three-dimensions are the most common vulnerable behaviour owned by employees. Therefore, by avoiding the three-dimensions to failure may improve the vulnerable behaviour of employees which is often related to immoral/amoral management.

Keywords: information security management system, information security behaviour, information security governance, information security culture

Procedia PDF Downloads 191

Authors: Faraj A. E. Alhegagi, Bassam F. A. Alhajaji

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Ductile and brittle fractures are the two main modes for the failure of engineering components. Fractures are classified with respect to several characteristics, such as strain to fracture, ductile or brittle crystallographic mode, shear or cleavage, and the appearance of fracture, granular or transgranular. Cleavage is a brittle fracture involves transcrystalline fracture along specific crystallographic planes and in certain directions. Fracture of duplex stainless steels takes place transgranularly by cleavage of the ferrite phase. On the other hand, ductile fracture occurs after considerable plastic deformation prior to failure and takes place by void nucleation, growth, and coalescence to provide an easy fracture path. Twinning causes depassivation more readily than slip and appears at stress lower than the theoretical yield stress. Consequently, damage due to twinning can occur well before that due to slip. Stainless steels are clean materials with the low efficiency of second particles phases on the fracture mechanism. The ferrite cleavage and austenite tear off are the main mode by which duplex stainless steels fails. In this study, the cracking mode and path of specimens of duplex stainless steels were investigated. Zeron 100 specimens were heat treated to different times cooled down and pulled to failure. The fracture surface was investigated by scanning electron microscopy (SEM) concentrating on the cracking mechanism, path, and origin. Cracking mechanisms were studied for those grains either as ferrite or austenite grains identified according to fracture surface features. Cracks propagated through the ferrite and the austenite two phases were investigated. Cracks arrested at the grain boundary were studied as well. For specimens aged for 100h, the ferrite phase was noted to crack by cleavage along well-defined planes while austenite ridges were clearly observed within the ferrite grains. Some grains were observed to fail with topographic features that were not clearly identifiable as ferrite cleavage or austenite tearing. Transgranular cracking was observed taking place in the ferrite phase on well-defined planes. No intergranular cracks were observed for the tested material. The austenite phase was observed to serve as a crack bridge and crack arrester.

Keywords: austenite ductile tear off, cracking mode, ferrite cleavage, stainless steels failure

Procedia PDF Downloads 130

Authors: Abdullahi Jibrin, Aishetu Abdulkadir

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The development of allometric models is crucial to accurate forest biomass/carbon stock assessment. The aim of this study was to develop a set of biomass prediction models that will enable the determination of total tree aboveground biomass for savannah woodland area in Niger State, Nigeria. Based on the data collected through biometric measurements of 1816 trees and destructive sampling of 36 trees, five species specific and one site specific models were developed. The sample size was distributed equally between the five most dominant species in the study site (Vitellaria paradoxa, Irvingia gabonensis, Parkia biglobosa, Anogeissus leiocarpus, Pterocarpus erinaceous). Firstly, the equations were developed for five individual species. Secondly these five species were mixed and were used to develop an allometric equation of mixed species. Overall, there was a strong positive relationship between total tree biomass and the stem diameter. The coefficient of determination (R2 values) ranging from 0.93 to 0.99 P < 0.001 were realised for the models; with considerable low standard error of the estimates (SEE) which confirms that the total tree above ground biomass has a significant relationship with the dbh. The F-test value for the biomass prediction models were also significant at p < 0.001 which indicates that the biomass prediction models are valid. This study recommends that for improved biomass estimates in the study site, the site specific biomass models should preferably be used instead of using generic models.

Keywords: allometriy, biomass, carbon stock , model, regression equation, woodland, inventory

Procedia PDF Downloads 431

Authors: Li Zhang, Yuehong Su

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Lightpipe as a mature solar light tube technique has been employed worldwide. Accurately assessing the performance of lightpipe and evaluate daylighting available has been a challenging topic. Previous research had used regression model and computational simulation methods to estimate the performance of lightpipe. However, due to the nonlinear nature of solar light transferring in lightpipe, the methods mentioned above express inaccurate and time-costing issues. In the present study, a neural network model as an alternative method is investigated to predict the transmittance of lightpipe. Four types of commercial lightpipe with bended angle 0°, 30°, 45° and 60° are discussed under clear, intermediate and overcast sky conditions respectively. The neural network is generated in MATLAB by using the outcomes of an optical software Photopia simulations as targets for networks training and testing. The coefficient of determination (R²) for each model is higher than 0.98, and the mean square error (MSE) is less than 0.0019, which indicate the neural network strong predictive ability and the use of the neural network method could be an efficient technique for determining the performance of lightpipe.

Keywords: neural network, bended lightpipe, transmittance, Photopia

Procedia PDF Downloads 136

Authors: Suleyman Karabal, Timothy L. Burnett, Egemen Avcu, Andrew H. Sherry, Philip J. Withers

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SA508 Grade 3 steel is widely used in the construction of nuclear pressure vessels, where its fracture toughness plays a critical role in ensuring operational safety and reliability. Understanding the ductile failure mechanisms in this steel grade is crucial for designing robust pressure vessels that can withstand severe nuclear environment conditions. In the present study, round bar specimens of SA508 Grade 3 steel with four distinct notch geometries were subjected to tensile loading while capturing continuous 2D images at 5-second intervals in order to monitor any alterations in their geometries to construct true stress-strain curves of the specimens. 3D reconstructions of X-ray computed tomography (CT) images at high-resolution (a spatial resolution of 0.82 μm) allowed for a comprehensive assessment of the influences of second-phase particles (i.e., manganese sulfide inclusions and cementite particles) on ductile failure initiation as a function of applied plastic strain. Additionally, based on 2D and 3D images, plasticity modeling was executed, and the results were compared to experimental data. A specific ‘two-parameter criterion’ was established and calibrated based on the correlation between stress triaxiality and equivalent plastic strain at failure initiation. The proposed criterion demonstrated substantial agreement with the experimental results, thus enhancing our knowledge of ductile fracture behavior in this steel grade. The implementation of X-ray CT and fractography analysis provided new insights into the diverse roles played by different populations of second-phase particles in fracture initiation under varying stress triaxiality conditions.

Keywords: ductile fracture, two-parameter criterion, x-ray computed tomography, stress triaxiality

Procedia PDF Downloads 73

Authors: Rohini Hariharan, Yazhini R, Bhamidipati Naga Shrikarti

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In the realm of cricket, particularly within the context of the Indian Premier League (IPL), the ability to predict team scores accurately holds significant importance for both cricket enthusiasts and stakeholders alike. This paper presents a comprehensive study on IPL score prediction utilizing various machine learning algorithms, including Support Vector Machines (SVM), XGBoost, Multiple Regression, Linear Regression, K-nearest neighbors (KNN), and Random Forest. Through meticulous data preprocessing, feature engineering, and model selection, we aimed to develop a robust predictive framework capable of forecasting team scores with high precision. Our experimentation involved the analysis of historical IPL match data encompassing diverse match and player statistics. Leveraging this data, we employed state-of-the-art machine learning techniques to train and evaluate the performance of each model. Notably, Multiple Regression emerged as the top-performing algorithm, achieving an impressive accuracy of 77.19% and a precision of 54.05% (within a threshold of +/- 10 runs). This research contributes to the advancement of sports analytics by demonstrating the efficacy of machine learning in predicting IPL team scores. The findings underscore the potential of advanced predictive modeling techniques to provide valuable insights for cricket enthusiasts, team management, and betting agencies. Additionally, this study serves as a benchmark for future research endeavors aimed at enhancing the accuracy and interpretability of IPL score prediction models.

Keywords: indian premier league (IPL), cricket, score prediction, machine learning, support vector machines (SVM), xgboost, multiple regression, linear regression, k-nearest neighbors (KNN), random forest, sports analytics

Procedia PDF Downloads 30

Authors: Habib Akbarzadeh Bengar, Mohammad Asadi Kiadehi, Ali Rameeh

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The result of the past earthquakes has shown that insufficient amount of stirrups and brittle behavior of concrete lead to the shear and flexural failure in reinforced concrete (RC) members. In this paper, an analytical model proposed to predict the nonlinear behavior of RC and SFRC elements and frames. In this model, some important parameter such as shear effect, varying axial load, and longitudinal bar buckling are considered. The results of analytical model were verified with experimental tests. The results of verification have shown that the proposed analytical model can predict the nonlinear behavior of RC and SFRC members and also frames accurately. In addition, the results have shown that use of steel fibers increased bearing capacity and ductility of RC frame. Due to this enhancement in shear strength and ductility, insufficient amount of stirrups, which resulted in shear failure, can be offset with usage of the steel fibers. In addition to the steps taken, to analyze the effects of fibers percentages on the bearing capacity and ductility of frames parametric studies have been performed to investigate of these effects.

Keywords: nonlinear analysis, SFRC frame, shear failure, varying an axial load

Procedia PDF Downloads 202