Search results for: failure prediction

Authors: Hilya Mudrika Arini, Nur Aini Masruroh, Budi Hartono

Abstract:

There are more than 20 terrorist threats from 2002 to 2012 in Indonesia. Despite of this fact, preventive solution through studies in the field of national security in Indonesia has not been conducted comprehensively. This study aims to provide a preventive solution by developing prediction model of the terrorist threat in Indonesia by using Bayesian network. There are eight stages to build the model, started from literature review, build and verify Bayesian belief network to what-if scenario. In order to build the model, four experts from different perspectives are utilized. This study finds several significant findings. First, news and the readiness of terrorist group are the most influent factor. Second, according to several scenarios of the news portion, it can be concluded that the higher positive news proportion, the higher probability of terrorist threat will occur. Therefore, the preventive solution to reduce the terrorist threat in Indonesia based on the model is by keeping the positive news portion to a maximum of 38%.

Keywords: Bayesian network, decision analysis, national security system, text mining

Procedia PDF Downloads 379

Authors: Hamid Kazemi, Abbasali Sadeghi

Abstract:

Assessment of seismic behavior of frame structures is just done for evaluating life and financial damages or lost. The new structural seismic behavior assessment methods have been proposed, so it is necessary to define a formulation as a damage index, which the damage amount has been quantified and qualified. In this paper, four new steel moment-resisting frames with intermediate ductility and different height (2, 5, 8, and 12-story) with regular geometry and simple rectangular plan were supposed and designed. The three existing groups’ damage indices were studied, each group consisting of local index (Drift, Maximum Roof Displacement, Banon Failure, Kinematic, Banon Normalized Cumulative Rotation, Cumulative Plastic Rotation and Ductility), global index (Roufaiel and Meyer, Papadopoulos, Sozen, Rosenblueth, Ductility and Base Shear), and story (Banon Failure and Inter-story Rotation). The necessary parameters for these damage indices have been calculated under the effect of far-fault ground motion records by Non-linear Dynamic Time History Analysis. Finally, prioritization of damage indices is defined based on more conservative values in terms of more damageability rate. The results show that the selected damage index has an important effect on estimation of the damage state. Also, failure, drift, and Rosenblueth damage indices are more conservative indices respectively for local, story and global damage indices.

Keywords: damage index, far-fault ground motion records, non-linear time history analysis, SeismoStruct software, steel moment-resisting frame

Procedia PDF Downloads 280

Authors: Gabrielle Peck, Ryan Hayes

Abstract:

This research presents the design and creation of a drone gas analyser, aimed at addressing the need for independent data collection and analysis of gas emissions during large-scale fires, particularly wasteland fires. The analyser drone, comprising a lightweight gas analysis system attached to a remote-controlled drone, enables the real-time assessment of smoke toxicity and the monitoring of gases released into the atmosphere during such incidents. The key components of the analyser unit included two gas line inlets connected to glass wool filters, a pump with regulated flow controlled by a mass flow controller, and electrochemical cells for detecting nitrogen oxides, hydrogen cyanide, and oxygen levels. Additionally, a non-dispersive infrared (NDIR) analyser is employed to monitor carbon monoxide (CO), carbon dioxide (CO₂), and hydrocarbon concentrations. Thermocouples can be attached to the analyser to monitor temperature, as well as McCaffrey probes combined with pressure transducers to monitor air velocity and wind direction. These additions allow for monitoring of the large fire and can be used for predictions of fire spread. The innovative system not only provides crucial data for assessing smoke toxicity but also contributes to fire prediction and management. The remote-controlled drone's mobility allows for safe and efficient data collection in proximity to the fire source, reducing the need for human exposure to hazardous conditions. The data obtained from the gas analyser unit facilitates informed decision-making by emergency responders, aiding in the protection of both human health and the environment. This abstract highlights the successful development of a drone gas analyser, illustrating its potential for enhancing smoke toxicity analysis and fire prediction capabilities. The integration of this technology into fire management strategies offers a promising solution for addressing the challenges associated with wildfires and other large-scale fire incidents. The project's methodology and results contribute to the growing body of knowledge in the field of environmental monitoring and safety, emphasizing the practical utility of drones for critical applications.

Keywords: fire prediction, drone, smoke toxicity, analyser, fire management

Procedia PDF Downloads 72

Authors: Vahid Nourani, Atefeh Ashrafi

Abstract:

Prediction of treated wastewater quality is a matter of growing importance in water treatment procedure. In this way artificial neural network (ANN), as a robust data-driven approach, has been widely used for forecasting the effluent quality of wastewater treatment. However, developing ANN model based on appropriate input variables is a major concern due to the numerous parameters which are collected from treatment process and the number of them are increasing in the light of electronic sensors development. Various studies have been conducted, using different clustering methods, in order to classify most related and effective input variables. This issue has been overlooked in the selecting dominant input variables among wastewater treatment parameters which could effectively lead to more accurate prediction of water quality. In the presented study two ANN models were developed with the aim of forecasting effluent quality of Tabriz city’s wastewater treatment plant. Biochemical oxygen demand (BOD) was utilized to determine water quality as a target parameter. Model A used Principal Component Analysis (PCA) for input selection as a linear variance-based clustering method. Model B used those variables identified by the mutual information (MI) measure. Therefore, the optimal ANN structure when the result of model B compared with model A showed up to 15% percent increment in Determination Coefficient (DC). Thus, this study highlights the advantage of PCA method in selecting dominant input variables for ANN modeling of wastewater plant efficiency performance.

Keywords: Artificial Neural Networks, biochemical oxygen demand, principal component analysis, mutual information, Tabriz wastewater treatment plant, wastewater treatment plant

Procedia PDF Downloads 114

Authors: Bernd Engel, Sara Salman Hassan Al-Maeeni

Abstract:

A substantial amount of natural resources are nowadays consumed at a growing rate, as humans all over the world used materials obtained from the Earth. Machinery manufacturing industry is one of the major resource consumers on a global scale. Even though the incessant finding out of the new material, metals, and resources, it is urgent for the industry to develop methods to use the Earth's resources intelligently and more sustainable than before. Re-engineering of machine tools regarding design and failure analysis is an approach whereby out-of-date machines are upgraded and returned to useful life. To ensure the reliable future performance of the used machine components, it is essential to investigate the machine component failure through the material, design, and surface examinations. This paper presents an experimental approach aimed at inspecting the shaft of the rotary draw bending machine as a case to study. The testing methodology, which is based on the principle of the three-point bending test, allows assessing the shaft elastic behavior under loading. Furthermore, the shaft elastic characteristics include the maximum linear deflection, and maximum bending stress was determined by using an analytical approach and finite element (FE) analysis approach. In the end, the results were compared with the ones obtained by the experimental approach. In conclusion, it is seen that the measured bending deflection and bending stress were well close to the permissible design value. Therefore, the shaft can work in the second life cycle. However, based on previous surface tests conducted, the shaft needs surface treatments include re-carburizing and refining processes to ensure the reliable surface performance.

Keywords: deflection, FE analysis, shaft, stress, three-point bending

Procedia PDF Downloads 140

Authors: Rachel Y. Zhang, Christopher K. Anderson

Abstract:

A critical aspect of revenue management is a firm’s ability to predict demand as a function of price. Historically hotels have used simple time series models (regression and/or pick-up based models) owing to the complexities of trying to build casual models of demands. Machine learning approaches are slowly attracting attention owing to their flexibility in modeling relationships. This study provides an overview of approaches to forecasting hospitality demand – focusing on the opportunities created by machine learning approaches, including K-Nearest-Neighbors, Support vector machine, Regression Tree, and Artificial Neural Network algorithms. The out-of-sample performances of above approaches to forecasting hotel demand are illustrated by using a proprietary sample of the market level (24 properties) transactional data for Las Vegas NV. Causal predictive models can be built and evaluated owing to the availability of market level (versus firm level) data. This research also compares and contrast model accuracy of firm-level models (i.e. predictive models for hotel A only using hotel A’s data) to models using market level data (prices, review scores, location, chain scale, etc… for all hotels within the market). The prospected models will be valuable for hotel revenue prediction given the basic characters of a hotel property or can be applied in performance evaluation for an existed hotel. The findings will unveil the features that play key roles in a hotel’s revenue performance, which would have considerable potential usefulness in both revenue prediction and evaluation.

Keywords: hotel revenue, k-nearest-neighbors, machine learning, neural network, prediction model, regression tree, support vector machine

Procedia PDF Downloads 117

Authors: Jawairia Umar, Tanveer Hussain, Zulfiqar Ali, Muhammad Maqsood

Abstract:

Different knitted structures and knitted parameters play a vital role in the stretch and recovery management of compression sportswear in addition to the materials use to generate this stretch and recovery behavior of the fabric. The present work was planned to predict the different performance indicators of a compression sportswear fabric with some ground parameters i.e. base yarn stitch length (polyester as base yarn and spandex as plating yarn involve to make a compression fabric) and linear density of the spandex which is a key material of any sportswear fabric. The prediction models were generated by response surface method for performance indicators such as stretch & recovery percentage, compression generated by the garment on body, total elongation on application of high power force and load generated on certain percentage extension in fabric. Certain physical properties of the fabric were also modeled using these two parameters.

Keywords: Compression, sportswear, stretch and recovery, statistical model, kikuhime

Procedia PDF Downloads 362

Authors: Hilalah Alturkistani, Alaa AlFaadhel, Nora AlJahani, Fatiha Djebbar

Abstract:

Cloud computing is one of the most widely used technology which provides opportunities and services to government entities, large companies, and standard users. However, cybersecurity risk management studies of cloud computing and resiliency approaches are lacking. This paper proposes resilient cloud cybersecurity risk assessment and management tailored specifically, to Dropbox with two approaches:1) technical-based solution motivated by a cybersecurity risk assessment of cloud services, and 2)a target personnel-based solution guided by cybersecurity-related survey among employees to identify their knowledge that qualifies them withstand to any cyberattack. The proposed work attempts to identify cloud vulnerabilities, assess threats and detect high risk components, to finally propose appropriate safeguards such as failure predicting and removing, redundancy or load balancing techniques for quick recovery and return to pre-attack state if failure happens.

Keywords: cybersecurity risk management plan, resilient cloud computing, cyberattacks, cybersecurity risk assessment

Procedia PDF Downloads 119

Authors: Meysam Naeimi, Zili Li, Roumen Petrov, Rolf Dollevoet, Jilt Sietsma, Jun Wu

Abstract:

The substantial similarity of fatigue mechanism in a new test rig for rolling contact fatigue (RCF) has been investigated. A new reduced-scale test rig is designed to perform controlled RCF tests in wheel-rail materials. The fatigue mechanism of the rig is evaluated in this study using a combined finite element-fatigue prediction approach. The influences of loading conditions on fatigue crack initiation have been studied. Furthermore, the effects of some artificial defects (squat-shape) on fatigue lives are examined. To simulate the vehicle-track interaction by means of the test rig, a three-dimensional finite element (FE) model is built up. The nonlinear material behaviour of the rail steel is modelled in the contact interface. The results of FE simulations are combined with the critical plane concept to determine the material points with the greatest possibility of fatigue failure. Based on the stress-strain responses, by employing of previously postulated criteria for fatigue crack initiation (plastic shakedown and ratchetting), fatigue life analysis is carried out. The results are reported for various loading conditions and different defect sizes. Afterward, the cyclic mechanism of the test rig is evaluated from the operational viewpoint. The results of fatigue life predictions are compared with the expected number of cycles of the test rig by its cyclic nature. Finally, the estimative duration of the experiments until fatigue crack initiation is roughly determined.

Keywords: fatigue, test rig, crack initiation, life, rail, squats

Procedia PDF Downloads 500

Authors: Wendu Pang, Yaxin Luo, Junhong Li, Yu Zhao, Danni Cheng, Yufang Rao, Minzi Mao, Ke Qiu, Yijun Dong, Fei Chen, Jun Liu, Jian Zou, Haiyang Wang, Wei Xu, Jianjun Ren

Abstract:

We aimed to compare the prognostic prediction value of positive lymph node number (PLNN) to the American Joint Committee on Cancer (AJCC) tumor, lymph node, and metastasis (TNM) staging system for patients with hypopharyngeal squamous cell carcinoma (HPSCC). A total of 826 patients with HPSCC from the Surveillance, Epidemiology, and End Results database (2004–2015) were identified and split into two independent cohorts: training (n=461) and validation (n=365). Univariate and multivariate Cox regression analyses were used to evaluate the prognostic effects of PLNN in patients with HPSCC. We further applied six Cox regression models to compare the survival predictive values of the PLNN and AJCC TNM staging system. PLNN showed a significant association with overall survival (OS) and cancer-specific survival (CSS) (P < 0.001) in both univariate and multivariable analyses, and was divided into three groups (PLNN 0, PLNN 1-5, and PLNN>5). In the training cohort, multivariate analysis revealed that the increased PLNN of HPSCC gave rise to significantly poor OS and CSS after adjusting for age, sex, tumor size, and cancer stage; this trend was also verified by the validation cohort. Additionally, the survival model incorporating a composite of PLNN and TNM classification (C-index, 0.705, 0.734) performed better than the PLNN and AJCC TNM models. PLNN can serve as a powerful survival predictor for patients with HPSCC and is a surrogate supplement for cancer staging systems.

Keywords: hypopharyngeal squamous cell carcinoma, positive lymph nodes number, prognosis, prediction models, survival predictive values

Procedia PDF Downloads 130

Authors: D.Mendes, J. Henriques, P. Carvalho, T. Rocha, S. Paredes, R. Cabiddu, R. Trimer, R. Mendes, A. Borghi-Silva, L. Kaminsky, E. Ashley, R. Arena, J. Myers

Abstract:

The continued exploration of clinically relevant predictive models continues to be an important pursuit. Cardiorespiratory fitness (CRF) portends clinical vital information and as such its accurate prediction is of high importance. Therefore, the aim of the current study was to develop a data-driven model, based on computational intelligence techniques and, in particular, clustering approaches, to predict CRF. Two prediction models were implemented and compared: 1) the traditional Wasserman/Hansen Equations; and 2) an interpretable clustering approach. Data used for this analysis were from the 'FRIEND - Fitness Registry and the Importance of Exercise: The National Data Base'; in the present study a subset of 10690 apparently healthy individuals were utilized. The accuracy of the models was performed through the computation of sensitivity, specificity, and geometric mean values. The results show the superiority of the clustering approach in the accurate estimation of CRF (i.e., maximal oxygen consumption).

Keywords: cardiorespiratory fitness, data-driven models, knowledge extraction, machine learning

Procedia PDF Downloads 272

Authors: Alan Wan

Abstract:

In recent years, the body of literature on frequentist model averaging in statistics has grown significantly. Most of this work focuses on models with different mean structures but leaves out the variance consideration. In this paper, we consider a regression model with multiplicative heteroscedasticity and develop a model averaging method that combines maximum likelihood estimators of unknown parameters in both the mean and variance functions of the model. Our weight choice criterion is based on a minimisation of a plug-in estimator of the model average estimator's squared prediction risk. We prove that the new estimator possesses an asymptotic optimality property. Our investigation of finite-sample performance by simulations demonstrates that the new estimator frequently exhibits very favourable properties compared to some existing heteroscedasticity-robust model average estimators. The model averaging method hedges against the selection of very bad models and serves as a remedy to variance function misspecification, which often discourages practitioners from modeling heteroscedasticity altogether. The proposed model average estimator is applied to the analysis of two real data sets.

Keywords: heteroscedasticity-robust, model averaging, multiplicative heteroscedasticity, plug-in, squared prediction risk

Procedia PDF Downloads 355

Authors: Hyon I. Paek, Sang Rim Kim, Hyon A. Ryu

Abstract:

In functional linear regression, one typical problem is to reduce dimension. Compared with multivariate linear regression, functional linear regression is regarded as an infinite-dimensional case, and the main task is to reduce dimensions of functional response and functional predictors. One common approach is to adapt functional principal component analysis (FPCA) on functional predictors and then use a few leading functional principal components (FPC) to predict the functional model. The leading FPCs estimated by the typical FPCA explain a major variation of the functional predictor, but these leading FPCs may not be mostly correlated with the functional response, so they may not be significant in the prediction for response. In this paper, we propose a supervised functional principal component analysis method for a functional response model with FPCs obtained by considering the correlation of the functional response. Our method would have a better prediction accuracy than the typical FPCA method.

Keywords: supervised, functional principal component analysis, functional response, functional linear regression

Procedia PDF Downloads 54

Authors: Yilei Song, Linlin Tian, Ning Zhao

Abstract:

Turbulence energetics and structures in the wake of large-scale wind turbines under the stably-stratified atmospheric boundary layer (SABL) can be complicated due to the presence of low-level jets (LLJs), a region of higher wind speeds than the geostrophic wind speed. With a modified one-k-equation, eddy viscosity model specified for atmospheric flows as the sub-grid scale (SGS) model, a realistic atmospheric state of the stable ABL is well reproduced by large-eddy simulation (LES) techniques. Corresponding to the precursor stably stratification, the detailed wake properties of a standard 5-MW wind turbine represented as an actuator line model are provided. An engineering model is proposed for wake prediction based on the simulation statistics and gets validated. Results confirm that the proposed wake model can provide good predictions for wind turbines under the SABL.

Keywords: large-eddy simulation, stably-stratified atmospheric boundary layer, wake model, wind turbine wake

Procedia PDF Downloads 162

Authors: André Luís Brasil Cavalcante, Juan Felix Rodriguez Rebolledo, Lucas Parreira de Faria Borges

Abstract:

A methodology for the probabilistic analysis of active earth pressure on retaining wall for c-Φ soil backfill is described in this paper. The Rosenblueth point estimate method is used to measure the failure probability of a gravity retaining wall. The basic principle of this methodology is to use two point estimates, i.e., the standard deviation and the mean value, to examine a variable in the safety analysis. The simplicity of this framework assures to its wide application. For the calculation is required 2ⁿ repetitions during the analysis, since the system is governed by n variables. In this study, a probabilistic model based on the Rosenblueth approach for the computation of the overturning probability of failure of a retaining wall is presented. The obtained results have shown the advantages of this kind of models in comparison with the deterministic solution. In a relatively easy way, the uncertainty on the wall and fill parameters are taken into account, and some practical results can be obtained for the retaining structure design.

Keywords: retaining wall, active earth pressure, backfill, probabilistic analysis

Procedia PDF Downloads 401

Authors: Charles Amoatey

Abstract:

The issue of risk in the mining industry is a global phenomenon and the Ghanaian mining industry is not exempted. The main purpose of this study is to identify the critical risk factors affecting the mining industry. The study takes an integrated view of the mining industry by examining the contribution of various risk factors to mining project failure in Ghana. A questionnaire survey was conducted to solicit the critical risk factors from key mining practitioners. About 80 respondents from 11 mining firms participated in the survey. The study identified 22 risk factors contributing to mining project failure in Ghana. The five most critical risk factors based on both probability of occurrence and impact were: (1) unstable commodity prices, (2) inflation/exchange rate, (3) land degradation, (4) high cost of living and (5) government bureaucracy for obtaining licenses. Furthermore, the study found that risk assessment in the mining sector has a direct link with mining project sustainability. Mitigation measures for addressing the identified risk factors were discussed. The key findings emphasize the need for a comprehensive risk management culture in the entire mining industry.

Keywords: risk, assessment, mining, Ghana

Procedia PDF Downloads 435

Authors: Mariya Dayana, Budhmal Jain

Abstract:

Kannur International Airport Ltd. (KIAL) is a new Greenfield airport project with airside development on an undulating terrain with an average height of 90m above Mean Sea Level (MSL) and a maximum height of 142m. To accommodate the desired Runway length and Runway End Safety Area (RESA) at both the ends along the proposed alignment, it resulted in 45.5 million cubic meters in cutting and filling. The insufficient availability of land for the construction of free slope embankment at RESA 07 end resulted in the design and construction of Reinforced Soil Slope (RSS) with a maximum slope of 65 degrees. An embankment fill of average 70m height with steep slopes located in high rainfall area is a unique feature of this project. The design and construction was challenging being asymmetrical with curves and bends. The fill was reinforced with high strength Uniaxial geogrids laid perpendicular to the slope. Weld mesh wrapped with coir mat acted as the facia units to protect it against surface failure. Face anchorage were also provided by wrapping the geogrids along the facia units where the slope angle was steeper than 45 degrees. Considering high rainfall received on this table top airport site, extensive drainage system was designed for the high embankment fill. Gabion wall up to 10m height were also designed and constructed along the boundary to accommodate the toe of the RSS fill beside the jeepable track at the base level. The design of RSS fill was done using ReSSA software and verified in PLAXIS 2D modeling. Both slip surface failure and wedge failure cases were considered in static and seismic analysis for local and global failure cases. The site won excavated laterite soil was used as the fill material for the construction. Extensive field and laboratory tests were conducted during the construction of RSS system for quality assurance. This paper represents a case study detailing the design and construction of a very high embankment using geosynthetics for the provision of Runway length and RESA area.

Keywords: airport, embankment, gabion, high strength uniaxial geogrid, kial, laterite soil, plaxis 2d

Procedia PDF Downloads 146

Authors: Vijay Pal Singh, Yogesh Chandra Kotiyal

Abstract:

Structures are a combination of various load carrying members which transfer the loads to the foundation from the superstructure safely. At the design stage, the loading of the structure is defined and appropriate material choices are made based upon their properties, mainly related to strength. The strength of materials kept on reducing with time because of many factors like environmental exposure and deformation caused by unpredictable external loads. Hence, to predict the strength of materials used in structures, various techniques are used. Among these techniques, Non-Destructive Techniques (NDT) are the one that can be used to predict the strength without damaging the structure. In the present study, the compressive strength of concrete has been predicted using Artificial Neural Network (ANN). The predicted strength was compared with the experimentally obtained actual compressive strength of concrete and equations were developed for different models. A good co-relation has been obtained between the predicted strength by these models and experimental values. Further, the co-relation has been developed using two NDT techniques for prediction of strength by regression analysis. It was found that the percentage error has been reduced between the predicted strength by using combined techniques in place of single techniques.

Keywords: rebound, ultra-sonic pulse, penetration, ANN, NDT, regression

Procedia PDF Downloads 408

Authors: Umberto Cerasani, Luca Cerasani

Abstract:

Methods for earthquakes forecasting have been intensively investigated in the last decades, but there is still no universal solution agreed by seismologists. Rock failure is most often preceded by a tiny elastic movement in the failure area and by the appearance of micro-cracks. These micro-cracks could be detected at the soil surface and represent useful earth-quakes precursors. The aim of this study was to verify whether tiny raw acceleration signals (in the 10⁻¹ to 10⁻⁴ cm/s² range) prior to the arrival of main primary-waves could be exploitable and related to earthquakes magnitude. Mathematical tools such as Fast Fourier Transform (FFT), moving average and wavelets have been applied on raw acceleration data available on the ITACA web site, and the study focused on one of the most unpredictable earth-quakes, i.e., the August 24th, 2016 at 01H36 one that occurred in the central Italy area. It appeared that these tiny acceleration signals preceding main P-waves have different patterns both on frequency and time domains for high magnitude earthquakes compared to lower ones.

Keywords: earthquake, accelerometer, earthquake forecasting, seism

Procedia PDF Downloads 125

Authors: K. Tejasri, K. Suvarna Vani, S. Prathyusha, S. Ramya

Abstract:

Proteins are chained sequences of amino acids which are brought together by the peptide bonds. Many varying formations of the chains are possible due to multiple combinations of amino acids and rotation in numerous positions along the chain. Protein structure prediction is one of the crucial goals worked towards by the members of bioinformatics and theoretical chemistry backgrounds. Among the four different structure levels in proteins, we emphasize mainly the secondary level structure. Generally, the secondary protein basically comprises alpha-helix and beta-sheets. Multi-class classification problem of data with disparity is truly a challenge to overcome and has to be addressed for the beta strands. Imbalanced data distribution constitutes a couple of the classes of data having very limited training samples collated with other classes. The secondary structure data is extracted from the protein primary sequence, and the beta-strands are predicted using suitable machine learning algorithms.

Keywords: proteins, secondary structure elements, beta-sheets, beta-strands, alpha-helices, machine learning algorithms

Procedia PDF Downloads 81

Authors: Daniel Thompson, Sophie Cerutti, Muhammad Peerbux, Hansraj Bookun

Abstract:

Introduction: Diabetic nephropathy is the leading cause end stage renal failure in Australia, responsible for 36% of cases. Patients who require dialysis may be suitable for haemodialysis through an arteriovenous fistula (AVF), and preoperatively careful planning is required to select suitable vessels for a long-lasting fistula that provides suitable dialysis access. Due to high levels of vascular disease in diabetic patients, we sought to investigate whether there is a difference in the types of autologous AVFs created for diabetic patients in renal failure compared to their non-diabetic counterparts. Method: Data was collected from the Australasian Vascular Audit, for all vascular surgery completed at St. Vincent’s Hospital Melbourne between 2011-2020. Patients were selected by operative type, creation of AVF, and compared in two groups, diabetic patients and patients without diabetes. Chi-squared test was utilised to determine significance. Results: Data analysis is ongoing and will be complete with updated abstract in time for the conference. Discussion: Diabetic nephropathy is the cause for roughly a third of end stage renal failure in Australia. Diabetic patients present with a unique set of challenges when it comes to dialysis access due to increased risk of peripheral vascular disease and arterial calcification. Care must be taken in the creation of fistulas to minimise complications and increase the chance of long-lasting access. Our study investigates the difference in autologous AVFs between diabetics and non-diabetics, and results may be used to influence location of fistula creation. Further research may be used to investigate patency rates of fistulas in diabetics vs non-diabetics which would further influence treatment decisions.

Keywords: dialysis, diabetes, renal access, fistula

Procedia PDF Downloads 119

Authors: Paul Saah, Charles Mbohwa, Nelson Sizwe Madonsela

Abstract:

In the context of developing countries, this study explores a crucial component of economic development by examining the reasons behind the failure of small and medium-sized enterprises (SMEs). SMEs are acknowledged as essential drivers of economic expansion, job creation, and poverty alleviation in emerging countries. This research uses South Africa as a case study to evaluate the reasons why SMEs fail in developing nations. This study explores a quantitative research methodology to investigate the complex causes of SME failures using statistical tools and reliability tests. To ensure the viability of data collection, a sample size of 400 small business owners was chosen using a non-probability selection technique. A closed-ended questionnaire was the primary technique used to obtain detailed information from the participants. Data was analysed and interpreted using computer software packages such as the Statistical Package for the Social Sciences (SPSS). According to the findings, the main reasons why SMEs fail in developing nations are a lack of strategic business planning, a lack of funding, poor management, a lack of innovation, a lack of business research and a low level of education and training. The results of this study show that SMEs can be sustainable and successful as long as they comprehend and use the suggested small business success determining variables into their daily operations. This implies that the more SMEs in developing countries implement the proposed determinant factors of small business success in their business operations the more the businesses are likely to succeed and vice versa.

Keywords: failure, developing countries, SMEs, economic development, South Africa

Procedia PDF Downloads 58

Authors: Valentina Futoryanova, Hugh Hunt

Abstract:

One of the common failure modes of the diesel engine turbochargers is high cycle fatigue of the turbine wheel blades. Mistuning of the blades due to the casting process is believed to contribute to the failure mode. Laser vibrometer is used to characterize mistuning for a population of turbine wheels through the analysis of the blade response to piezo speaker induced noise. The turbine wheel design under investigation is radial and is typically used in 6-12 L diesel engine applications. Amplitudes and resonance frequencies are reviewed and summarized. The study also includes test results for a paddle wheel that represents a perfectly tuned system and acts as a reference. Mass spring model is developed for the paddle wheel and the model suitability is tested against the actual data. Randomization is applied to the stiffness matrix to model the mistuning effect in the turbine wheels. Experimental data is shown to have good agreement with the model.

Keywords: vibration, radial turbines, mistuning, turbine blades, modal analysis, periodic structures, finite element

Procedia PDF Downloads 416

Authors: Michaela Dušková, Kateřina Šimůnková, Martin Hill, Hana Hruškovičová, Hana Pospíšilová, Eva Králíková, Luboslav Stárka

Abstract:

Smoking represents the most widespread substance dependence in the world. Several studies show the nicotine's ability to alter women hormonal homeostasis. Women smokers have higher testosterone and lower estradiol levels throughout life compared to non-smoker women. We monitored the effect of smoking discontinuation on steroid spectrum with 40 premenopausal and 60 postmenopausal women smokers. These women had been examined before they discontinued smoking and also after 6, 12, 24, and 48 weeks of abstinence. At each examination, blood was collected to determine steroid spectrum (measured by GC-MS), LH, FSH, and SHBG (measured by IRMA). Repeated measures ANOVA model was used for evaluation of the data. The study has been approved by the local Ethics Committee. Given the small number of premenopausal women who endured not to smoke, only the first 6 week period data could be analyzed. A slight increase in androgens after the smoking discontinuation occurred. In postmenopausal women, an increase in testosterone, dihydrotestosterone, dehydroepiandrosterone, and other androgens occurred, too. Nicotine replacement therapy, weight changes, and age does not play any role in the androgen level increase. The higher androgens levels correlated with failure in smoking cessation. Women smokers have higher androgen levels, which might play a role in smoking dependence development. Women successful in smoking cessation, compared to the non-successful ones, have lower androgen levels initially and also after smoking discontinuation. The question is what androgen levels women have before they start smoking.

Keywords: addiction, smoking, cessation, androgens

Procedia PDF Downloads 367

Authors: Messis A., Adjebli A., Ayeche R., Talbi M., Tighilet K., Louardiane M.

Abstract:

Cancers are the second cause of death worldwide. Prevalence and incidence of cancers is getting increased by aging and population growth. This study aims to predict and modeling the evolution of breast, Colorectal, Lung, Bladder and Prostate cancers over the period of 2014-2019. In this study, data were analyzed using time series analysis with double exponential smoothing method to forecast the future pattern. To describe and fit the appropriate models, Minitab statistical software version 17 was used. Between 2014 and 2019, the overall trend in the raw number of new cancer cases registered has been increasing over time; the change in observations over time has been increasing. Our forecast model is validated since we have good prediction for the period 2020 and data not available for 2021 and 2022. Time series analysis showed that the double exponential smoothing is an efficient tool to model the future data on the raw number of new cancer cases.

Keywords: cancer, time series, prediction, double exponential smoothing

Procedia PDF Downloads 71

Authors: Youn-Sung Kim, Hye-Mi Kim, Sang-Hoon Seo, Jaden Cha

Abstract:

This International standard IEC 61508 sets out a generic approach for all safety lifecycle activities for systems comprised of electrical/electronic/programmable electronic (E/E/PE) elements that are used to perform safety functions. The control unit in stage control system is safety related facilities to control state and speed for stage system running, and it performs safety-critical function by stage control system. The controller unit is part of safety loops corresponding to the IEC 61508 and classified as logic part in the safety loop. In this paper, we analyze using FMEDA (Failure Mode Effect and Diagnostic Analysis) to verification for fault tolerance methods and functional safety of control unit. Moreover, we determined SIL (Safety Integrity Level) for control unit according to the safety requirements defined in IEC 61508-2 based on an analyzed functional safety.

Keywords: safety function, failure mode effect, IEC 61508-2, diagnostic analysis, stage control system

Procedia PDF Downloads 265

Authors: Ehsan Alishahi, Chuang Deng

Abstract:

Commercial applications of bulk metallic glasses (BMGs) were restricted due to the sudden brittle failure mode which was the main drawback in these new class of materials. Therefore, crystalline-amorphous (C-A) composites were introduced as a toughening strategy in BMGs. In spite of numerous researches in the area of metallic C-A composites, the fundamental structure-property relation in these composites that are not exactly known yet. In this study, it is aimed to investigate the fundamental properties of crystalline-amorphous interface in a model system of Cu/CuZr by using molecular dynamics simulations. Several parameters including interface energy and mechanical properties were investigated by means of atomic models and employing Embedded Atom Method (EAM) potential function. It is found that the crystalline-amorphous interfacial energy weakly depends on the orientation of the crystalline layer, which is in stark contrast to that in a regular crystalline grain boundary. Additionally, the results showed that the interface controls the yielding of the crystalline-amorphous composites during uniaxial tension either by serving as sources for dislocation nucleation in the crystalline layer or triggering local shear transformation zones in amorphous layer. The critical resolved shear stress required to nucleate the first dislocation is also found to strongly depend on the crystalline orientation. Furthermore, it is found that the interaction between dislocations and shear localization at the crystalline-amorphous interface oriented in different directions can lead to a change in the deformation mode. For instance, while the dislocation and shear banding are aligned to each other in {0 0 1} interface plane, the misorientation angle between these failure mechanisms causing more homogeneous deformation in {1 1 0} and {1 1 1} crystalline-amorphous interfaces. These results should help clarify the failure mechanism of crystalline-amorphous composites under various loading conditions.

Keywords: crystalline-amorphous, composites, orientation, plasticity

Procedia PDF Downloads 282

Authors: Rajender Dahiya

Abstract:

Investigation outcomes of major process incidents have been consistent for decades and validate that the causes and consequences are often identical. The debate remains as we continue to experience similar process incidents even with enormous development of new tools, technologies, industry standards, codes, regulations, and learning processes? The objective of this paper is to investigate the most common causes of major industrial incidents and reveal industry challenges and best practices to prevent such incidents. The author, in his current role, performs audits and inspections of a variety of high-hazard industries in North America, including petroleum refineries, chemicals, petrochemicals, manufacturing, etc. In this paper, he shares real life scenarios, examples, and case studies from high hazards operating facilities including key challenges and best practices. This case study will provide a clear understanding of the importance of near miss incident investigation. The incident was a Safe operating limit excursion. The case describes the deficiencies in management programs, the competency of employees, and the culture of the corporation that includes hazard identification and risk assessment, maintaining the integrity of safety-critical equipment, operating discipline, learning from process safety near misses, process safety competency, process safety culture, audits, and performance measurement. Failure to identify the hazards and manage the risks of highly hazardous materials and processes is one of the primary root-causes of an incident, and failure to learn from past incidents is the leading cause of the recurrence of incidents. Several investigations of major incidents discovered that each showed several warning signs before occurring, and most importantly, all were preventable. The author will discuss why preventable incidents were not prevented and review the mutual causes of learning failures from past major incidents. The leading causes of past incidents are summarized below. Management failure to identify the hazard and/or mitigate the risk of hazardous processes or materials. This process starts early in the project stage and continues throughout the life cycle of the facility. For example, a poorly done hazard study such as HAZID, PHA, or LOPA is one of the leading causes of the failure. If this step is performed correctly, then the next potential cause is. Management failure to maintain the integrity of safety critical systems and equipment. In most of the incidents, mechanical integrity of the critical equipment was not maintained, safety barriers were either bypassed, disabled, or not maintained. The third major cause is Management failure to learn and/or apply learning from the past incidents. There were several precursors before those incidents. These precursors were either ignored altogether or not taken seriously. This paper will conclude by sharing how a well-implemented operating management system, good process safety culture, and competent leaders and staff contributed to managing the risks to prevent major incidents.

Keywords: incident investigation, risk management, loss prevention, process safety, accident prevention

Procedia PDF Downloads 44

Authors: D. W. Mohammed, J. Bowen, S. N. Kukureka

Abstract:

Successful design and fabrication of flexible devices for electrode components requires a low sheet resistance, high optical transmittance, high mechanical reliability. Indium tin oxide (ITO) film is currently the predominant transparent conductive oxide (TCO) film in potential applications such as flexible organic light- emitting diodes, flat-panel displays, solar cells, and thin film transistors (TFTs). However ITO films are too brittle and their resistivity is rather high in some cases compared with ITO/Ag/ ITO, and they cannot completely meet flexible optoelectronic device requirements. Therefore, in this work the mechanical properties of ITO /Ag/ITO multilayer film that deposited on Polyethylene terephthalate (PET) compared with the single layered ITO sample were investigated using bending fatigue, twisting fatigue and thermal cycling experiments. The electrical resistance was monitored during the application of mechanical and thermal loads to see the pattern of relationship between the load and the electrical continuity as a consequent of failure. Scanning electron microscopy and atomic force microscopy were used to provide surface characterization of the mechanically-tested samples. The effective embedment of the Ag layer between upper and lower ITO films led to metallic conductivity and superior flexibility to the single ITO electrode, due to the high failure strain of the ductile Ag layer. These results indicate that flexible ITO/Ag/ITO multilayer electrodes are a promising candidate for use as transparent conductor in flexible displays. They provided significantly reduced sheet resistance compared to ITO, and improved bending and twisting properties both as a function of radius, angle and thermal cycling.

Keywords: ITO/Ag/ITO multilayer, failure strain, mechanical properties, PET

Procedia PDF Downloads 284

Authors: Ch. Sridevi, S. P. Shailender Kumar, B. Gurudayal, A. Chalapathi Rao, K. Koteswara Rao, P. Srinivasulu

Abstract:

System reliability and system availability evaluation plays a crucial role in ensuring the seamless operation of complex satellite data reception system with consistent performance for longer periods. This paper presents a novel approach for the same using a case study on one of the antenna systems at satellite data reception ground station in India. The methodology involves analyzing system's components, their failure rates, system's architecture, generation of logical reliability block diagram model and estimating the reliability of the system using the component level mean time between failures considering exponential distribution to derive a baseline estimate of the system's reliability. The model is then validated with collected system level field failure data from the operational satellite data reception systems that includes failure occurred, failure time, criticality of the failure and repair times by using statistical techniques like median rank, regression and Weibull analysis to extract meaningful insights regarding failure patterns and practical reliability of the system and to assess the accuracy of the developed reliability model. The study mainly focused on identification of critical units within the system, which are prone to failures and have a significant impact on overall performance and brought out a reliability model of the identified critical unit. This model takes into account the interdependencies among system components and their impact on overall system reliability and provides valuable insights into the performance of the system to understand the Improvement or degradation of the system over a period of time and will be the vital input to arrive at the optimized design for future development. It also provides a plug and play framework to understand the effect on performance of the system in case of any up gradations or new designs of the unit. It helps in effective planning and formulating contingency plans to address potential system failures, ensuring the continuity of operations. Furthermore, to instill confidence in system users, the duration for which the system can operate continuously with the desired level of 3 sigma reliability was estimated that turned out to be a vital input to maintenance plan. System availability and station availability was also assessed by considering scenarios of clash and non-clash to determine the overall system performance and potential bottlenecks. Overall, this paper establishes a comprehensive methodology for reliability and availability analysis of complex satellite data reception systems. The results derived from this approach facilitate effective planning contingency measures, and provide users with confidence in system performance and enables decision-makers to make informed choices about system maintenance, upgrades and replacements. It also aids in identifying critical units and assessing system availability in various scenarios and helps in minimizing downtime and optimizing resource allocation.

Keywords: exponential distribution, reliability modeling, reliability block diagram, satellite data reception system, system availability, weibull analysis

Procedia PDF Downloads 69