Search results for: failure prediction

Authors: Heramb Somthankar, Anujoy Chakraborty

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This paper presents a classification study on yield prediction in semiconductor production using machine learning approaches. A complicated semiconductor production process is generally monitored continuously by signals acquired from sensors and measurement sites. A monitoring system contains a variety of signals, all of which contain useful information, irrelevant information, and noise. In the case of each signal being considered a feature, "Feature Selection" is used to find the most relevant signals. The open-source UCI SECOM Dataset provides 1567 such samples, out of which 104 fail in quality assurance. Feature extraction and selection are performed on the dataset, and useful signals were considered for further study. Afterward, common machine learning algorithms were employed to predict whether the signal yields pass or fail. The most relevant algorithm is selected for prediction based on the accuracy and loss of the ML model.

Keywords: deep learning, feature extraction, feature selection, machine learning classification algorithms, semiconductor production monitoring, signal processing, time-series analysis

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Authors: Godwin Chigozie Okpara

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This paper using standard GARCH, EGARCH, and TARCH models on day of the week return series (of 246 days) from the Nigerian Stock market estimated the model variants’ VaR. An asymmetric return distribution and fat-tail phenomenon in financial time series were considered by estimating the models with normal, student t and generalized error distributions. The analysis based on Akaike Information Criterion suggests that the EGARCH model with student t innovation distribution can furnish more accurate estimate of VaR. In the light of this, we apply the likelihood ratio tests of proportional failure rates to VaR derived from EGARCH model in order to determine the short and long positions VaR performances. The result shows that as alpha ranges from 0.05 to 0.005 for short positions, the failure rate significantly exceeds the prescribed quintiles while it however shows no significant difference between the failure rate and the prescribed quantiles for long positions. This suggests that investors and portfolio managers in the Nigeria stock market have long trading position or can buy assets with concern on when the asset prices will fall. Precisely, the VaR estimates for the long position range from -4.7% for 95 percent confidence level to -10.3% for 99.5 percent confidence level.

Keywords: downside risk, value-at-risk, failure rate, kupiec LR tests, GARCH models

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Authors: Bao Thach Nguyen, Abbas Mohajerani

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The California bearing ratio (CBR) has been acknowledged as an important parameter to characterize the bearing capacity of earth structures, such as earth dams, road embankments, airport runways, bridge abutments, and pavements. Technically, the CBR test can be carried out in the laboratory or in the field. The CBR test is time-consuming and is infrequently performed due to the equipment needed and the fact that the field moisture content keeps changing over time. Over the years, many correlations have been developed for the prediction of CBR by various researchers, including the dynamic cone penetrometer, undrained shear strength, and Clegg impact hammer. This paper reports and discusses some of the results from a study on the prediction of CBR. In the current study, the CBR test was performed in the laboratory on some fine-grained subgrade soils collected from various locations in Victoria. Based on the test results, a satisfactory empirical correlation was found between the CBR and the physical properties of the experimental soils.

Keywords: California bearing ratio, fine-grained soils, soil physical properties, pavement, soil test

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Authors: Jacky Liu

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This paper develops a team sports outcome prediction system with potential for wide-ranging applications across various disciplines. Despite significant advancements in predictive analytics, existing studies in sports outcome predictions possess considerable limitations, including insufficient feature engineering and underutilization of advanced machine learning techniques, among others. To address these issues, we extend the Sports Cross Industry Standard Process for Data Mining (SRP-CRISP-DM) framework and propose a unique, comprehensive predictive system, using National Basketball Association (NBA) data as an example to test this extended framework. Our approach follows a holistic methodology in feature engineering, employing both Time Series and Non-Time Series Data, as well as conducting Explanatory Data Analysis and Feature Selection. Furthermore, we contribute to the discourse on target variable choice in team sports outcome prediction, asserting that point spread prediction yields higher profits as opposed to game-winner predictions. Using machine learning algorithms, particularly XGBoost, results in a significant improvement in predictive accuracy of team sports outcomes. Applied to point spread betting strategies, it offers an astounding annual return of approximately 900% on an initial investment of $100. Our findings not only contribute to academic literature, but have critical practical implications for sports betting. Our study advances the understanding of team sports outcome prediction a burgeoning are in complex system predictions and pave the way for potential profitability and more informed decision making in sports betting markets.

Keywords: machine learning, team sports, game outcome prediction, sports betting, profits simulation

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Authors: Deba Kumar Sarma, Sanjib Kr. Rajbongshi

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Surface finish is an important product quality in machining. At first, experiments were carried out to investigate the effect of the cutting tool nose radius (considering 1mm and 0.65mm) in prediction of surface finish with process parameters of cutting speed, feed and depth of cut. For all possible cutting conditions, full factorial design was considered as two levels four parameters. Commercial Mild Steel bar and High Speed Steel (HSS) material were considered as work-piece and cutting tool material respectively. In order to obtain functional relationship between process parameters and surface roughness, neural network was used which was found to be capable for the prediction of surface roughness within a reasonable degree of accuracy. It was observed that tool nose radius of 1mm provides better surface finish in comparison to 0.65 mm. Also, it was observed that feed rate has a significant influence on surface finish.

Keywords: full factorial design, neural network, nose radius, surface finish

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Authors: Mustapha Kamel Mihoubi, Essadik Kerkar, Abdelhamid Hebbouche

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According to the randomness of most of the factors affecting the stability of a gravity dam, probability theory is generally used to TESTING the risk of failure and there is a confusing logical transition from the state of stability failed state, so the stability failure process is considered as a probable event. The control of risk of product failures is of capital importance for the control from a cross analysis of the gravity of the consequences and effects of the probability of occurrence of identified major accidents and can incur a significant risk to the concrete dam structures. Probabilistic risk analysis models are used to provide a better understanding the reliability and structural failure of the works, including when calculating stability of large structures to a major risk in the event of an accident or breakdown. This work is interested in the study of the probability of failure of concrete dams through the application of the reliability analysis methods including the methods used in engineering. It is in our case of the use of level II methods via the study limit state. Hence, the probability of product failures is estimated by analytical methods of the type FORM (First Order Reliability Method), SORM (Second Order Reliability Method). By way of comparison, a second level III method was used which generates a full analysis of the problem and involving an integration of the probability density function of, random variables are extended to the field of security by using of the method of Mont-Carlo simulations. Taking into account the change in stress following load combinations: normal, exceptional and extreme the acting on the dam, calculation results obtained have provided acceptable failure probability values which largely corroborate the theory, in fact, the probability of failure tends to increase with increasing load intensities thus causing a significant decrease in strength, especially in the presence of combinations of unique and extreme loads. Shear forces then induce a shift threatens the reliability of the structure by intolerable values of the probability of product failures. Especially, in case THE increase of uplift in a hypothetical default of the drainage system.

Keywords: dam, failure, limit state, monte-carlo, reliability, probability, sliding, Taylor

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Authors: Yousaf Tariq

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There are many reasons which are involved in electronic equipment failure i.e. temperature, humidity, dust, smoke etc. Corrosive gases are also one of the factor which may involve in failure of equipment. Sensitivity of electronic equipment increased when “lead-free” regulation enforced on manufacturers. In data center, equipment like hard disk, servers, printed circuit boards etc. have been exposed to gaseous contamination due to increase in sensitivity. There is a worldwide standard to protect electronic industrial electronic from corrosive gases. It is well known as “ANSI/ISA S71.04 – 1985 - Environmental Conditions for Control Systems: Airborne Contaminants. ASHRAE Technical Committee (TC) 9.9 members also recommended ISA standard in their whitepaper on Gaseous and Particulate Contamination Guideline for data centers. TC 9.9 members represented some of the major IT equipment manufacturers e.g. IBM, HP, Cisco etc. As per standard practices, first step is to monitor air quality in data center. If contamination level shows more than G1, it means that gas-phase air filtration is required other than dust/smoke air filtration. It is important that outside fresh air entering in data center should have pressurization/re-circulated process in order to absorb corrosive gases and to maintain level within specified limit. It is also important that air quality monitoring should be conducted once in a year. Temperature and humidity should also be monitored as per standard practices to maintain level within specified limit.

Keywords: corrosive gases, corrosion, electronic equipment failure, ASHRAE, hard disk

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Authors: Souvik Das, Sandip Bhattacharya, Goutam Mukhopadhyay, Manashi Adhikary

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Wires breakages during cold drawing are a complex phenomenon; wire breakages may be induced by improper wire-rod quality, inappropriate heat-treated microstructure, and/or lubrication breakdown on the wire surface. A comprehensive metallurgical investigation of failed/broken wire samples is therefore essential for understanding the origin of failure. Frequent breakage of wires during drawing is a matter of serious concern to the wire drawers as it erodes their already slim margins through reduced productivity and loss in yield. The present paper highlights the failure investigation of wires of Low Relaxation Prestressed High Carbon grade during cold drawing due to entrapment of hard constituents detached from the roller entry guide during rolling operations. The hardness measurement of this entrapped location indicates 54.9 Rockwell Hardness as against the rest portion 33.4 Rockwell Hardness. The microstructure chemical analysis and X-ray mapping analysis data of the entrapment location confirmed complex chromium carbide originated from D2-steel used in entry guide during the rolling process. Since the harder entrapped phase could not be deformed in the same manner as the parent phase, the failure of the wire rod occurs during hot rolling.

Keywords: LRPC, D2-steel, chromium carbide, roller guide

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Authors: Luis Marquez, Ge Zhu, Vikas Srivastava

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High-density polyethylene (HDPE) is one of the most commonly used thermoplastic polymer materials for water and gas pipelines. Slow crack growth failure is a well-known phenomenon in high-density polyethylene material and causes brittle failure well below the yield point with no obvious sign. The failure of transportation pipelines can cause catastrophic environmental and economic consequences. Using the non-destructive testing method to predict slow crack growth failure behavior is the primary preventative measurement employed by the pipeline industry but is often costly and time-consuming. Phenomenological slow crack growth models are useful to predict the slow crack growth behavior in the polymer material due to their ability to evaluate slow crack growth under different temperature and loading conditions. We developed a quantitative method to assess the slow crack growth behavior in the high-density polyethylene pipeline material under different thermal conditions based on existing physics-based phenomenological models. We are also working on developing an experimental protocol and quantitative model that can address slow crack growth behavior under different chemical exposure conditions to improve the safety, reliability, and resilience of HDPE-based pipeline infrastructure.

Keywords: mechanics of materials, physics-based modeling, civil engineering, fracture mechanics

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Authors: Qiming Zhang, Youda Ye, Qinxue Jiang

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Accurate prediction of external flowfield and aero-heating at the wall of hypersonic vehicle is very crucial for the design of aircrafts. Unstructured/hybrid meshes have more powerful advantages than structured meshes in terms of pre-processing, parallel computing and mesh adaptation, so it is imperative to develop high-resolution numerical methods for the calculation of aerothermal environment on unstructured/hybrid meshes. The inviscid flux scheme is one of the most important factors affecting the accuracy of unstructured/ hybrid mesh heat flux calculation. Here, a new hybrid flux scheme is developed and the approach of interface type selection is proposed: i.e. 1) using the exact Riemann scheme solution to calculate the flux on the faces parallel to the wall; 2) employing Sterger-Warming (S-W) scheme to improve the stability of the numerical scheme in other interfaces. The results of the heat flux fit the one observed experimentally and have little dependence on grids, which show great application prospect in unstructured/ hybrid mesh.

Keywords: aero-heating prediction, computational fluid dynamics, hybrid meshes, hybrid schemes

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Authors: Belabed Lazhar

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The dimensioning of the anchored retaining screens passes always by the analysis of their stability. The calculation of anchoring lengths is practically carried out according to the mechanical model suggested by Kranz which is often criticized. The safety is evaluated through the comparison of interior force and external force. The force of anchoring over the length cut behind the failure solid is neglected. The failure surface cuts anchoring in the medium length of sealing. In this article, one proposes a new mechanical model which overcomes these disadvantages (simplifications) and gives interesting results.

Keywords: retaining walls, anchoring, stability, mechanical modeling, safety

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Authors: Abdoullah Namdar, Fadzil Mat Yahaya

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Many synthetic additives have been using for improve cement mortar and concrete characteristics, but natural additive is a friendly environment option. The quantity of (2% and 4%) seashell powder has been replaced in cement mortar, and compared with plain cement mortar in early age of 7 days. The strain gauges have been installed on beams and cube, for monitoring fluctuation of flexural and compressive strength. Main objective of this paper is to study effect of linear static force on flexural and compressive strength of modified cement mortar. The results have been indicated that the replacement of appropriate proportion of seashell powder enhances cement mortar mechanical properties. The replacement of 2% seashell causes improvement of deflection, time to failure and maximum load to failure on concrete beam and cube, the same occurs for compressive modulus elasticity. Increase replacement of seashell to 4% reduces all flexural strength, compressive strength and strain of cement mortar.

Keywords: compressive strength, flexural strength, compressive modulus elasticity, time to failure, deflection

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Authors: Majid Samiee Zonoozian

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For such important constructions as jacket type platforms, scrupulous attention in analysis, design and calculation processes is needed. The reliability assessment method has been established into an extensively used method to behavior safety calculation of jacket platforms. In the present study, a methodology for the reliability calculation of an offshore jacket platform in contradiction of the extreme wave loading state is available. Therefore, sensitivity analyses are applied to acquire the nonlinear response of jacket-type platforms against extreme waves. The jacket structure is modeled by applying a nonlinear finite-element model with regards to the tubular members' behave. The probability of a member’s failure under extreme wave loading is figured by a finite-element reliability code. The FORM and SORM approaches are applied for the calculation of safety directories and reliability indexes have been detected. A case study for a fixed jacket-type structure positioned in the Persian Gulf is studied by means of the planned method. Furthermore, to define the failure standards, equations suggested by the 21st version of the API RP 2A-WSD for The jacket-type structures’ tubular members designing by applying the mixed axial bending and axial pressure. Consequently, the effect of wave Loades in the reliability index was considered.

Keywords: Jacket-Type structure, reliability, failure probability, tubular members

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Authors: Rehan Waheed, Abdul Shakoor

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Distortion produced during welding of thin metal plates is a problem in many industries. The purpose of this research was to study distortion produced during welding in 2mm Mild Steel plate by simulating the welding process using Finite Element Analysis. Simulation of welding process requires a couple field transient analyses. At first a transient thermal analysis is performed and the temperature obtained from thermal analysis is used as input in structural analysis to find distortion. An actual weld sample is prepared and the weld distortion produced is measured. The simulated and actual results were in quite agreement with each other and it has been found that there is profound deflection at center of plate. Temperature dependent material properties play significant role in prediction of weld distortion. The results of this research can be used for prediction and control of weld distortion in large steel structures by changing different weld parameters.

Keywords: welding simulation, FEA, welding distortion, temperature dependent mechanical properties

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Authors: Hyunchul Ahn, William X. S. Wong

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Corporate credit rating prediction is one of the most important topics, which has been studied by researchers in the last decade. Over the last decade, researchers are pushing the limit to enhance the exactness of the corporate credit rating prediction model by applying several data-driven tools including statistical and artificial intelligence methods. Among them, multiclass support vector machine (MSVM) has been widely applied due to its good predictability. However, heuristics, for example, parameters of a kernel function, appropriate feature and instance subset, has become the main reason for the critics on MSVM, as they have dictate the MSVM architectural variables. This study presents a hybrid MSVM model that is intended to optimize all the parameter such as feature selection, instance selection, and kernel parameter. Our model adopts genetic algorithm (GA) to simultaneously optimize multiple heterogeneous design factors of MSVM.

Keywords: corporate credit rating prediction, Feature selection, genetic algorithms, instance selection, multiclass support vector machines

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Authors: Xiaomin Zhong, Alexander Pate, Ya-Ting Yang, Ali Fahmi, Darren M. Ashcroft, Ben Goldacre, Brian Mackenna, Amir Mehrkar, Sebastian C. J. Bacon, Jon Massey, Louis Fisher, Peter Inglesby, Kieran Hand, Tjeerd van Staa, Victoria Palin

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Background: This study aimed to predict risks of potentially inappropriate antibiotic type and repeat prescribing and assess changes during COVID-19. Methods: With the approval of NHS England, we used the OpenSAFELY platform to access the TPP SystmOne electronic health record (EHR) system and selected patients prescribed antibiotics from 2019 to 2021. Multinomial logistic regression models predicted the patient’s probability of receiving an inappropriate antibiotic type or repeating the antibiotic course for each common infection. Findings: The population included 9.1 million patients with 29.2 million antibiotic prescriptions. 29.1% of prescriptions were identified as repeat prescribing. Those with same-day incident infection coded in the EHR had considerably lower rates of repeat prescribing (18.0%), and 8.6% had a potentially inappropriate type. No major changes in the rates of repeat antibiotic prescribing during COVID-19 were found. In the ten risk prediction models, good levels of calibration and moderate levels of discrimination were found. Important predictors included age, prior antibiotic prescribing, and region. Patients varied in their predicted risks. For sore throat, the range from 2.5 to 97.5th percentile was 2.7 to 23.5% (inappropriate type) and 6.0 to 27.2% (repeat prescription). For otitis externa, these numbers were 25.9 to 63.9% and 8.5 to 37.1%, respectively. Interpretation: Our study found no evidence of changes in the level of inappropriate or repeat antibiotic prescribing after the start of COVID-19. Repeat antibiotic prescribing was frequent and varied according to regional and patient characteristics. There is a need for treatment guidelines to be developed around antibiotic failure and clinicians provided with individualised patient information.

Keywords: antibiotics, infection, COVID-19 pandemic, antibiotic stewardship, primary care

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Authors: Hossein Kheirollahi, Yunhua Luo

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Studying stress and strain trends in the femur and recognizing femur failure mechanism is very important for preventing hip fracture in the elderly. The aim of this study was to identify high stress and strain regions in the femur during normal walking and falling to find the mechanical behavior and failure mechanism of the femur. We developed a finite element model of the femur from the subject’s quantitative computed tomography (QCT) image and used it to identify potentially high stress and strain regions during the single-leg stance and the sideways fall. It was found that fracture may initiate from the superior region of femoral neck and propagate to the inferior region during a high impact force such as sideways fall. The results of this study showed that the femur bone is more sensitive to strain than stress which indicates the effect of strain, in addition to effect of stress, should be considered for failure analysis.

Keywords: finite element analysis, hip fracture, strain, stress

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Authors: Vipin Kumar Singhal, Rohit Kumar Mittal, Pavitra Ranjan Maiti

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Damage analysis is one of the preliminary activities to be done after an earthquake so as to enhance the seismic building design technologies and prevent similar type of failure in future during earthquakes. This research article investigates the damage pattern and most probable reason of failure by observing photographs of seven major buildings collapsed/damaged which were evenly spread over the region during Mw7.8, Nepal earthquake 2015 followed by more than 400 aftershocks of Mw4 with one aftershock reaching a magnitude of Mw7.3. Over 250,000 buildings got damaged, and more than 9000 people got injured in this earthquake. Photographs of these buildings were collected after the earthquake and the cause of failure was estimated along with the severity of damage and comment on the reparability of structure has been made. Based on observations, it was concluded that the damage in reinforced concrete buildings was less compared to masonry structures. The number of buildings damaged was high near Kathmandu region due to high building density in that region. This type of damage analysis can be used as a cost effective and quick method for damage assessment during earthquakes.

Keywords: Nepal earthquake, damage analysis, damage assessment, damage scales

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Authors: Kongrat Nokkaew

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This article assessed the surface erosion and stability of cut and fill slope in the excavation of the detention basin, Kalasin Province, Thailand. The large excavation project was built to enlarge detention basin for relieving repeated flooding and drought which usually happen in this area. However, at the end of the 1st rainstorm season, severely erosions slope failures were widespread observed. After investigation, the severity of erosions and slope failure were classified into five level from sheet erosion (Level 1), rill erosion (Level 2, 3), gully erosion (Level 4), and slope failure (Level 5) for proposing slope remediation. The preliminary investigation showed that lack of runoff control were the major factors of the surface erosions while insufficient compacted of the fill slope leaded to slopes failures. The slope stability of four selected slope failure was back calculated by using Simplified Bishop with Seep-W. The result show that factor of safety of slope located on non-plasticity sand was less than one, representing instability of the embankment slope. Such analysis agreed well with the failures observed in the field.

Keywords: surface erosion, slope stability, detention basin, cut and fill

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Authors: Abdulaziz Alakeel, Roaa Alamri, Abdulrahman Alomair, Mohammed Fouda

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Introduction: Ibrutinib is a selective, potent, and irreversible small-molecule inhibitor of Bruton's tyrosine kinase (BTK). It forms a covalent bond with a cysteine residue (CYS-481) at the active site of Btk, leading to inhibition of Btk enzymatic activity. The drug is indicated to treat certain type of cancers such as mantle cell lymphoma (MCL), chronic lymphocytic leukaemia and Waldenström's macroglobulinaemia (WM). Cardiac failure is a condition referred to inability of heart muscle to pump adequate blood to human body organs. There are multiple types of cardiac failure including left and right-sided heart failure, systolic and diastolic heart failures. The aim of this review is to evaluate the risk of cardiac failure associated with the use of ibrutinib and to suggest regulatory recommendations if required. Methodology: Signal Detection team at the National Pharmacovigilance Center (NPC) of Saudi Food and Drug Authority (SFDA) performed a comprehensive signal review using its national database as well as the World Health Organization (WHO) database (VigiBase), to retrieve related information for assessing the causality between cardiac failure and ibrutinib. We used the WHO- Uppsala Monitoring Centre (UMC) criteria as standard for assessing the causality of the reported cases. Results: Case Review: The number of resulted cases for the combined drug/adverse drug reaction are 212 global ICSRs as of July 2020. The reviewers have selected and assessed the causality for the well-documented ICSRs with completeness scores of 0.9 and above (35 ICSRs); the value 1.0 presents the highest score for best-written ICSRs. Among the reviewed cases, more than half of them provides supportive association (four probable and 15 possible cases). Data Mining: The disproportionality of the observed and the expected reporting rate for drug/adverse drug reaction pair is estimated using information component (IC), a tool developed by WHO-UMC to measure the reporting ratio. Positive IC reflects higher statistical association while negative values indicates less statistical association, considering the null value equal to zero. The results of (IC=1.5) revealed a positive statistical association for the drug/ADR combination, which means “Ibrutinib” with “Cardiac Failure” have been observed more than expected when compared to other medications available in WHO database. Conclusion: Health regulators and health care professionals must be aware for the potential risk of cardiac failure associated with ibrutinib and the monitoring of any signs or symptoms in treated patients is essential. The weighted cumulative evidences identified from causality assessment of the reported cases and data mining are sufficient to support a causal association between ibrutinib and cardiac failure.

Keywords: cardiac failure, drug safety, ibrutinib, pharmacovigilance, signal detection

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Authors: Rashini Maduka, C. R. Wijesinghe, A. R. Weerasinghe

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Drug-drug interactions (DDIs) can happen when two or more drugs are taken together. Today DDIs have become a serious health issue due to adverse drug effects. In vivo and in vitro methods for identifying DDIs are time-consuming and costly. Therefore, in-silico-based approaches are preferred in DDI identification. Most machine learning models for DDI prediction are used chemical and biological drug properties as features. However, some drug features are not available and costly to extract. Therefore, it is better to make automatic feature engineering. Furthermore, people who have diabetes already suffer from other diseases and take more than one medicine together. Then adverse drug effects may happen to diabetic patients and cause unpleasant reactions in the body. In this study, we present a model with a graph convolutional autoencoder and a graph decoder using a dataset from DrugBank version 5.1.3. The main objective of the model is to identify unknown interactions between antidiabetic drugs and the drugs taken by diabetic patients for other diseases. We considered automatic feature engineering and used Known DDIs only as the input for the model. Our model has achieved 0.86 in AUC and 0.86 in AP.

Keywords: drug-drug interaction prediction, graph embedding, graph convolutional networks, adverse drug effects

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Authors: Isha Saxena, Behzad Kazemtabrizi, Matthias C. M. Troffaes, Christopher Crabtree

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The calculations done at the beginning of the life of a wind farm are rarely reliable, which makes it important to conduct research and study the failure and repair rates of the wind turbines under various conditions. This miscalculation happens because the current models make a simplifying assumption that the failure/repair rate remains constant over time. This means that the reliability function is exponential in nature. This research aims to create a more accurate model using sensory data and a data-driven approach. The data cleaning and data processing is done by comparing the Power Curve data of the wind turbines with SCADA data. This is then converted to times to repair and times to failure timeseries data. Several different mathematical functions are fitted to the times to failure and times to repair data of the wind turbine components using Maximum Likelihood Estimation and the Posterior expectation method for Bayesian Parameter Estimation. Initial results indicate that two parameter Weibull function and exponential function produce almost identical results. Further analysis is being done using the complex system analysis considering the failures of each electrical and mechanical component of the wind turbine. The aim of this project is to perform a more accurate reliability analysis that can be helpful for the engineers to schedule maintenance and repairs to decrease the downtime of the turbine.

Keywords: reliability, bayesian parameter inference, maximum likelihood estimation, weibull function, SCADA data

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Authors: Nane Giloyan

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The rapid development of social networks in the 21st century increases the interests towards the role and impact of social media on the success or failure of a revolution. Even though studies are investigating the role of social media on the outcome of a revolution, still, the conclusions on this matter are ambiguous so far. Hence, this research aims to investigate the role of social media in the success or failure of a revolution and make a contribution to the literature gap. The study aims to examine the research question whether the use of social media explains the success or failure of revolutions in 2008 and 2018 in Armenia. The research question is investigated through content analysis of two cases; failed revolution in 2008 and the successful revolution in 2018 in Armenia. The secondary data analysis was based on information devoted to two revolutions using local and major international news articles, journal and critical articles, in Armenian, Russian and English, also videos, posts and live streams of the revolutionary leaders. There can be many factors explaining the success or failure of a revolution. However, the investigation of the factors and their role to explain the outcome of a revolution other than the use of social media is beyond the scope of this research study. The study holds other variables constant and concludes that in the cases of 2008 and 2018 revolutions in Armenia the mobilization of society through social media explains the differences in the outcomes (failed or successful). The comparative case study of the revolutions in 2008 and 2018 in Armenia emphasizes the important role and impact of the use of social media on the success or failure of a revolution. The results highlight that the use of the Internet, particularly social media and live streams, by the opposition was the essential difference between two revolutions. Social media platforms, live streams, and communication apps that were absent in the revolutionary situation in 2008 were fundamental to the Armenian Velvet Revolution in 2018. The changes in the situation in favor of the opposition, so the outcome of the protests, were mainly based on the Internet-based mobilization of the society. It is also important to take into consideration that the country experienced a great increase in penetration rates over the decade. The percentage of access to the Internet drastically increased between 2008 and 2018. This fact may help to have a clearer understanding of the use of the Internet and social media by the opposition and the reliance on social media by society. According to the results of the continent analysis, the use of social media to direct the protests and to mobilize the society, have a vital role and positive impact on the outcome of a revolution. Thus the study concludes that it is the use of social media to initiate, organize, and direct the protests that explain the success or failure of two Armenian revolutions.

Keywords: social media, revolution, Armenia, success, failure

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Authors: Ravija Gunawardana, Banuka Athuraliya

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Machine learning has emerged as a powerful tool for disease diagnosis and prediction. The use of machine learning algorithms has the potential to improve the accuracy of disease prediction, thereby enabling medical professionals to provide more effective and personalized treatments. This study focuses on developing a machine-learning model for disease prediction using symptoms and X-ray images. The importance of this study lies in its potential to assist medical professionals in accurately diagnosing diseases, thereby improving patient outcomes. Respiratory diseases are a significant cause of morbidity and mortality worldwide, and chest X-rays are commonly used in the diagnosis of these diseases. However, accurately interpreting X-ray images requires significant expertise and can be time-consuming, making it difficult to diagnose respiratory diseases in a timely manner. By incorporating machine learning algorithms, we can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The study utilized the Mask R-CNN algorithm, which is a state-of-the-art method for object detection and segmentation in images, to process chest X-ray images. The model was trained and tested on a large dataset of patient information, which included both symptom data and X-ray images. The performance of the model was evaluated using a range of metrics, including accuracy, precision, recall, and F1-score. The results showed that the model achieved an accuracy rate of over 90%, indicating that it was able to accurately detect and segment regions of interest in the X-ray images. In addition to X-ray images, the study also incorporated symptoms as input data for disease prediction. The study used three different classifiers, namely Random Forest, K-Nearest Neighbor and Support Vector Machine, to predict diseases based on symptoms. These classifiers were trained and tested using the same dataset of patient information as the X-ray model. The results showed promising accuracy rates for predicting diseases using symptoms, with the ensemble learning techniques significantly improving the accuracy of disease prediction. The study's findings indicate that the use of machine learning algorithms can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The model developed in this study has the potential to assist medical professionals in diagnosing respiratory diseases more accurately and efficiently. However, it is important to note that the accuracy of the model can be affected by several factors, including the quality of the X-ray images, the size of the dataset used for training, and the complexity of the disease being diagnosed. In conclusion, the study demonstrated the potential of machine learning algorithms for disease prediction using symptoms and X-ray images. The use of these algorithms can improve the accuracy of disease diagnosis, ultimately leading to better patient care. Further research is needed to validate the model's accuracy and effectiveness in a clinical setting and to expand its application to other diseases.

Keywords: K-nearest neighbor, mask R-CNN, random forest, support vector machine

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Authors: Monapat Sasingha, Suttisak Soralump

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This research presents the behavior of slope of the road along the canal stabilized by short piles. In this investigation, the centrifuge machine was used, modelling the condition of the water levels in the canal. The centrifuge tests were performed at 35 g. To observe the movement of the soil, visual analysis was performed to evaluate the failure behavior. Conclusively, the use of short piles to stabilize the canal slope proved to be an effective solution. However, the certain amount of settlement was found behind the short pile rows.

Keywords: centrifuge test, slope failure, embankment, stability of slope

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Authors: Fahad Raj Khan, Suleman Khan

Abstract:

There is a lack of consensus about the predictive value of raised blood glucose levels in terms of major adverse cardiac events (MACEs) in non-diabetic patients admitted for acute decompensated heart failure. The purpose of this research was to examine the long-term prognosis of acute decompensated heart failure (ADHF) in non-diabetic persons who had increased blood glucose levels, i.e., stress hyperglycemia, at the time of their ADHF hospitalization. The research involved 650 non-diabetic patients. Based on their admission stress hyperglycemia, they were divided into two groups.ie with and without (SHGL). The two groups' one-year outcomes for major adverse cardiac events (MACEs) were compared, and key predictors of MACEs were discovered. For statistical analysis, the two-tailed Mann-Whitney U test, Fisher's exact test, and binary logistic regression analysis were utilized. SHGL was found in 353 (54.3%) individuals. It was more frequent in men than in women. About 27% of patients with SHGL had previously been admitted for ADHF. Almost 62% were hypertensive, whereas 14 % had CKD. MACEs were significantly predicted by SHGL, HTN, prior hospitalization for ADHF, CKD, and cardiogenic shock upon admission. SHGL at the time of ADHF admission, independent of DM status, may be a predictive indication of MACEs.

Keywords: stress hyperglycemia, acute heart failure, major adverse cardiac events, MACEs

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Authors: Asra Yousuf, Ajaykumar Tannirkulum

Abstract:

Inferring rural-urban migration trends can help design effective policies that promote better urban planning and rural development. In this paper, we describe how machine learning algorithms can be applied to predict internal migration decisions of people. We consider data collected from household surveys in Tamil Nadu to train our model. To measure the performance of the model, we use data on past migration from National Sample Survey Organisation of India. The factors for training the model include socioeconomic characteristic of each individual like age, gender, place of residence, outstanding loans, strength of the household, etc. and his past migration history. We perform a comparative analysis of the performance of a number of machine learning algorithm to determine their prediction accuracy. Our results show that machine learning algorithms provide a stronger prediction accuracy as compared to statistical models. Our goal through this research is to propose the use of data science techniques in understanding human decisions and behaviour in developing countries.

Keywords: development, migration, internal migration, machine learning, prediction

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Authors: Queen Suraajini Rajendran, Sai Hung Cheung

Abstract:

Statistical down scaling models are required to connect the global climate model outputs and the local weather variables for climate change impact prediction. For reliable climate change impact studies, the uncertainty associated with the model including natural variability, uncertainty in the climate model(s), down scaling model, model inadequacy and in the predicted results should be quantified appropriately. In this work, a new approach is developed by the authors for statistical classification, statistical down scaling and uncertainty assessment and is applied to Singapore rainfall. It is a robust Bayesian uncertainty analysis methodology and tools based on coupling dependent modeling error with classification and statistical down scaling models in a way that the dependency among modeling errors will impact the results of both classification and statistical down scaling model calibration and uncertainty analysis for future prediction. Singapore data are considered here and the uncertainty and prediction results are obtained. From the results obtained, directions of research for improvement are briefly presented.

Keywords: statistical downscaling, global climate model, climate change, uncertainty

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Authors: Jinyong Yao, Hongzhi Li, Chao Du, Jiao Li

Abstract:

Reliability of long-term storage products is related to the availability of the whole system, and the evaluation of storage life is of great necessity. These products are usually highly reliable and little failure information can be collected. In this paper, an analytical method based on data from accelerated storage life test is proposed to evaluate the reliability index of the long-term storage products. Firstly, singularities are eliminated by data normalization and residual analysis. Secondly, with the pre-processed data, the degradation path model is built to obtain the pseudo life values. Then by life distribution hypothesis, we can get the estimator of parameters in high stress levels and verify failure mechanisms consistency. Finally, the life distribution under the normal stress level is extrapolated via the acceleration model and evaluation of the true average life available. An application example with the camera stabilization device is provided to illustrate the methodology we proposed.

Keywords: accelerated storage life test, failure mechanisms consistency, life distribution, reliability

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Authors: Cristian Păuna

Abstract:

After the widespread release of electronic trading, automated trading systems have become a significant part of the business intelligence system of any modern financial investment company. An important part of the trades is made completely automatically today by computers using mathematical algorithms. The trading decisions are taken almost instantly by logical models and the orders are sent by low-latency automatic systems. This paper will present a real-time price prediction methodology designed especially for algorithmic trading. Based on the price cyclicality function, the methodology revealed will generate price cyclicality bands to predict the optimal levels for the entries and exits. In order to automate the trading decisions, the cyclicality bands will generate automated trading signals. We have found that the model can be used with good results to predict the changes in market behavior. Using these predictions, the model can automatically adapt the trading signals in real-time to maximize the trading results. The paper will reveal the methodology to optimize and implement this model in automated trading systems. After tests, it is proved that this methodology can be applied with good efficiency in different timeframes. Real trading results will be also displayed and analyzed in order to qualify the methodology and to compare it with other models. As a conclusion, it was found that the price prediction model using the price cyclicality function is a reliable trading methodology for algorithmic trading in the financial market.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, price prediction

Procedia PDF Downloads 170