Search results for: damage prediction models

Authors: Mohammad Namazi, Mohammad Sadeghzadeh Maharluie

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Predicting earnings management is vital for the capital market participants, financial analysts and managers. The aim of this research is attempting to respond to this query: Is there a significant difference between the regression model and neural networks’ models in predicting earnings management, and which one leads to a superior prediction of it? In approaching this question, a Linear Regression (LR) model was compared with two neural networks including Multi-Layer Perceptron (MLP), and Generalized Regression Neural Network (GRNN). The population of this study includes 94 listed companies in Tehran Stock Exchange (TSE) market from 2003 to 2011. After the results of all models were acquired, ANOVA was exerted to test the hypotheses. In general, the summary of statistical results showed that the precision of GRNN did not exhibit a significant difference in comparison with MLP. In addition, the mean square error of the MLP and GRNN showed a significant difference with the multi variable LR model. These findings support the notion of nonlinear behavior of the earnings management. Therefore, it is more appropriate for capital market participants to analyze earnings management based upon neural networks techniques, and not to adopt linear regression models.

Keywords: earnings management, generalized linear regression, neural networks multi-layer perceptron, Tehran stock exchange

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Authors: Orod Zarrin, Mohesn Ramezan Shirazi, Hassan Moniri

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The use of pile foundations technique is developed to support structures and buildings on soft soil. The most important dynamic load that can affect the pile structure is earthquake vibrations. From the 1960s the comprehensive investigation of pile foundations during earthquake excitation indicate that, piles are subject to damage by affecting the superstructure integrity and serviceability. The main part of these research has been focused on the behavior of liquefiable soil and lateral spreading load on piles. During an earthquake, two types of stresses can damage the pile head, inertial load that is caused by superstructure and deformation which caused by the surrounding soil. Soil deformation and inertial load are associated with the acceleration developed in an earthquake. The acceleration amplitude at the ground surface depends on the magnitude of earthquakes, soil properties and seismic source distance. According to the investigation, the damage is between the liquefiable and non-liquefiable layers and also soft and stiff layers. This damage crushes the pile head by increasing the inertial load which is applied by the superstructure. On the other hand, the cracks on the piles due to the surrounding soil are directly related to the soil profile and causes cracks from small to large. And researchers have been listed the large cracks reason such as liquefaction, lateral spreading and inertial load. In the field of designing, elastic response of piles are always a challenge for designer in liquefaction soil, by allowing deflection at top of piles. Moreover, absence of plastic hinges in piles should be insured, because the damage in the piles is not observed directly. In this study, the performance and behavior of pile foundations during liquefaction and lateral spreading are investigated. And emphasize on the soil behavior in the liquefiable and non-liquefiable layers by different aspect of piles damage such as ranking, location and degree of damage are going to discuss.

Keywords: self-compacting concrete, fiber, tensile strength, post-cracking, direct and inverse technique

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Authors: Şeyma Saliha Fidan, Ozgur Serin, Ata Mugan

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While performing verification analyses under static and dynamic loads that composite structures used in aviation are exposed to, it is necessary to obtain the bearing strength limit value for mechanically connected composite structures. For this purpose, various tests are carried out in accordance with aviation standards. There are many companies in the world that perform these tests in accordance with aviation standards, but the test costs are very high. In addition, due to the necessity of producing coupons, the high cost of coupon materials, and the long test times, it is necessary to simulate these tests on the computer. For this purpose, various test coupons were produced by using reinforcement and alignment angles of the composite radomes, which were integrated into the aircraft. Glass fiber reinforced and Quartz prepreg is used in the production of the coupons. The simulations of the tests performed according to the American Society for Testing and Materials (ASTM) D5961 Procedure C standard were performed on the computer. The analysis model was created in three dimensions for the purpose of modeling the bolt-hole contact surface realistically and obtaining the exact bearing strength value. The finite element model was carried out with the Analysis System (ANSYS). Since a physical break cannot be made in the analysis studies carried out in the virtual environment, a hypothetical break is realized by reducing the material properties. The material properties reduction coefficient was determined as 10%, which is stated to give the most realistic approach in the literature. There are various theories in this method, which is called progressive failure analysis. Because the hashin theory does not match our experimental results, the puck progressive damage method was used in all coupon analyses. When the experimental and numerical results are compared, the initial damage and the resulting force drop points, the maximum damage load values ​​, and the bearing strength value are very close. Furthermore, low error rates and similar damage patterns were obtained in both test and simulation models. In addition, the effects of various parameters such as pre-stress, use of bushing, the ratio of the distance between the bolt hole center and the plate edge to the hole diameter (E/D), the ratio of plate width to hole diameter (W/D), hot-wet environment conditions were investigated on the bearing strength of the composite structure.

Keywords: puck, finite element, bolted joint, composite

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Authors: Mustafa Kaya

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After the 1999 Marmara earthquake in Turkey research by the Turkish Precast Union stated that 24.50% of the precast structures were damaged with some of this damage being observed in the beam to column connections of the structures. Since it is essential to provide those rendered homeless by the earthquake with safe, habitable accommodation repairing medium and slight levels of damage at the connection parts should be undertaken. In order to prove that a repaired connection was sufficiently strong, a precast beam to column post tensioned connection was tested in three phases. In phase one, the middle level damage was observed at 6% drift at these connections. As a result of the extra loads applied, little damage was observed. In the last phase, the four connections tested in the first phase were repaired using epoxy resin and then retested. The results from the tests on the repaired precast and the undamaged specimens showed that the repaired specimens were sufficiently strong, thus proving that repair to damaged precast beam to column post tensioned connections can be undertaken.

Keywords: precast beam to column connection, moment-resisting connection, post-tensioned connections, repair of precast connections

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Authors: Belkacem Laimouche

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With the field of artificial intelligence (AI) experiencing exponential growth, fueled by technological advancements that pave the way for increasingly innovative and promising applications, there is an escalating need to develop rigorous methods for assessing their performance in pursuit of transparency and equity. This article proposes a metrology-inspired statistical framework for evaluating bias and explainability in AI systems. Drawing from the principles of metrology, we propose a pioneering approach, using a concrete example, to evaluate the accuracy and precision of AI models, as well as to quantify the sources of measurement uncertainty that can lead to bias in their predictions. Furthermore, we explore a statistical approach for evaluating the explainability of AI systems based on their ability to provide interpretable and transparent explanations of their predictions.

Keywords: artificial intelligence, metrology, measurement uncertainty, prediction error, bias, machine learning algorithms, probabilistic models, interlaboratory comparison, data analysis, data reliability, measurement of bias impact on predictions, improvement of model accuracy and reliability

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Authors: S. P. Gomes, D. C. Goncalves, E. Ribeiro, M. C. L. Seelaender

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Skin is susceptible to photo damage induced by exposure to sunlight, or ultraviolet (UV) radiation, which induces breakdown of extracellular matrix, DNA degradation, skin cell lesion and apoptosis, and development of cancer. Phytonutrients demonstrate protective effects against UV damage. The purpose of this study was evaluating the effect of an antioxidant juice (AJ) contaning Brazilian natural products upon skin damage. The juice was produced by Metabolics®. Male Wistar rats were divided in 4 groups: Animals receiving the antioxidant juice (AJ): orange, carrot, honey, tomato extract, avocado, ginger and camu-camu (Brazilian fruit, a major source of vitamin C) ad libitum for 21 days; or water (C), subdivided in groups exposed or not to UV radiation for 2 non consecutive days, during five hours each day, after 15 days of juice supplementation. On the 22nd day, rats were killed by decapitation and epithelium samples from the dorsal skin removed, fixed in bouin and embedded in paraffin. The sections were stained with hematoxylin and eosin or mallory and picrosirius red. Isolated DNA was submitted to electrophoresis (1.8% agarose gel, 0.5% ethidium bromide). UV radiation significantly induced sunburn of superficial epithelial cells of C, AJ treatment reduced this effect. Collagen changes were observed in UV groups, yet AJ treatment prevented collagen degradation. UV radiation induced significant DNA degradation, in C, which was prevented by AJ treatment. The antioxidant juice consumed chronically protected against acute skin damage.

Keywords: nutraceuticals, antioxidants, photoprotection, uv radiation

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Authors: Sudhir Kumar Singh, Debashish Chakravarty

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Slope stability analysis is an important aspect in the field of geotechnical engineering. It is also important from safety, and economic point of view as any slope failure leads to loss of valuable lives and damage to property worth millions. This paper aims at mitigating the risk of slope failure by studying the effect of different joint sets on the stability of mine pit slopes under the influence of various external factors, namely degree of saturation, rainfall intensity, and seismic coefficients. Supervised machine learning approach has been utilized for making accurate and reliable predictions regarding the stability of slopes based on the value of Factor of Safety. Numerous cases have been studied for analyzing the stability of slopes using the popular Finite Element Method, and the data thus obtained has been used as training data for the supervised machine learning models. The input data has been trained on different supervised machine learning models, namely Random Forest, Decision Tree, Support vector Machine, and XGBoost. Distinct test data that is not present in training data has been used for measuring the performance and accuracy of different models. Although all models have performed well on the test dataset but Random Forest stands out from others due to its high accuracy of greater than 95%, thus helping us by providing a valuable tool at our disposition which is neither computationally expensive nor time consuming and in good accordance with the numerical analysis result.

Keywords: finite element method, geotechnical engineering, machine learning, slope stability

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Authors: Vasileiou Apostolos, Theodosiou Chrysa, Tsitroulis Ioannis, Maris Fotios

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The severity of extreme phenomena is due to their ability to cause severe damage in a small amount of time. It has been observed that floods affect the greatest number of people and induce the biggest damage when compared to the total of annual natural disasters. The detection of potential flood-prone areas constitutes one of the fundamental components of the European Natural Disaster Management Policy, directly connected to the European Directive 2007/60. The aim of the present paper is to develop a new methodology that combines geographical information, fuzzy logic and multi-criteria evaluation methods so that the most vulnerable areas are defined. Therefore, ten factors related to geophysical, morphological, climatological/meteorological and hydrological characteristics of the basin were selected. Afterwards, two models were created to detect the areas pronest to flooding. The first model defined the gravitas of each factor using Analytical Hierarchy Process (AHP) and the final map of possible flood spots were created using GIS and Boolean Algebra. The second model made use of the fuzzy logic and GIS combination and a respective map was created. The application area of the aforementioned methodologies was in Ardas basin due to the frequent and important floods that have taken place these last years. Then, the results were compared to the already observed floods. The result analysis shows that both models can detect with great precision possible flood spots. As the fuzzy logic model is less time-consuming, it is considered the ideal model to apply to other areas. The said results are capable of contributing to the delineation of high risk areas and to the creation of successful management plans dealing with floods.

Keywords: analytical hierarchy process, flood prone areas, fuzzy logic, geographic information system

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Authors: Binger Lu

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It remains unclear whether second language (L2) speakers could use discourse context cues to predict upcoming information as native speakers do during online comprehension. Some researchers propose that L2 learners may have a reduced ability to generate predictions during discourse processing. At the same time, there is evidence that discourse-level cues are weighed more heavily in L2 processing than in L1. Previous studies showed that L1 prediction is sensitive to the global validity of predictive cues. The current study aims to explore whether and to what extent L2 learners can dynamically and strategically adjust their prediction in accord with the global validity of predictive cues in L2 discourse comprehension as native speakers do. In a self-paced reading experiment, Chinese native speakers (N=128), C-E bilinguals (N=128), and English native speakers (N=128) read high-predictable (e.g., Jimmy felt thirsty after running. He wanted to get some water from the refrigerator.) and low-predictable (e.g., Jimmy felt sick this morning. He wanted to get some water from the refrigerator.) discourses in two-sentence frames. The global validity of predictive cues was manipulated by varying the ratio of predictable (e.g., Bill stood at the door. He opened it with the key.) and unpredictable fillers (e.g., Bill stood at the door. He opened it with the card.), such that across conditions, the predictability of the final word of the fillers ranged from 100% to 0%. The dependent variable was reading time on the critical region (the target word and the following word), analyzed with linear mixed-effects models in R. C-E bilinguals showed reliable prediction across all validity conditions (β = -35.6 ms, SE = 7.74, t = -4.601, p< .001), and Chinese native speakers showed significant effect (β = -93.5 ms, SE = 7.82, t = -11.956, p< .001) in two of the four validity conditions (namely, the High-validity and MedLow conditions, where fillers ended with predictable words in 100% and 25% cases respectively), whereas English native speakers didn’t predict at all (β = -2.78 ms, SE = 7.60, t = -.365, p = .715). There was neither main effect (χ^²(3) = .256, p = .968) nor interaction (Predictability: Background: Validity, χ^²(3) = 1.229, p = .746; Predictability: Validity, χ^²(3) = 2.520, p = .472; Background: Validity, χ^²(3) = 1.281, p = .734) of Validity with speaker groups. The results suggest that prediction occurs in L2 discourse processing but to a much less extent in L1, witha significant effect in some conditions of L1 Chinese and anull effect in L1 English processing, consistent with the view that L2 speakers are more sensitive to discourse cues compared with L1 speakers. Additionally, the pattern of L1 and L2 predictive processing was not affected by the global validity of predictive cues. C-E bilinguals’ predictive processing could be partly transferred from their L1, as prior research showed that discourse information played a more significant role in L1 Chinese processing.

Keywords: bilingualism, discourse processing, global validity, prediction, self-paced reading

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Authors: Kuo-Chun Chien, Chia-Hui Chang, Ren-Der Sun

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Legal Judgment Prediction (LJP) is a subtask for legal AI. Its main purpose is to use the facts of a case to predict the judgment result. In Taiwan's criminal procedure, when prosecutors complete the investigation of the case, they will decide whether to prosecute the suspect and which article of criminal law should be used based on the facts and evidence of the case. In this study, we collected 305,240 indictments from the public inquiry system of the procuratorate of the Ministry of Justice, which included 169 charges and 317 articles from 21 laws. We take the crime facts in the indictments as the main input to jointly learn the prediction model for law source, article, and charge simultaneously based on the pre-trained Bert model. For single article cases where the frequency of the charge and article are greater than 50, the prediction performance of law sources, articles, and charges reach 97.66, 92.22, and 60.52 macro-f1, respectively. To understand the big performance gap between articles and charges, we used a bipartite graph to visualize the relationship between the articles and charges, and found that the reason for the poor prediction performance was actually due to the wording precision. Some charges use the simplest words, while others may include the perpetrator or the result to make the charges more specific. For example, Article 284 of the Criminal Law may be indicted as “negligent injury”, "negligent death”, "business injury", "driving business injury", or "non-driving business injury". As another example, Article 10 of the Drug Hazard Control Regulations can be charged as “Drug Control Regulations” or “Drug Hazard Control Regulations”. In order to solve the above problems and more accurately predict the article and charge, we plan to include the article content or charge names in the input, and use the sentence-pair classification method for question-answer problems in the BERT model to improve the performance. We will also consider a sequence-to-sequence approach to charge prediction.

Keywords: legal judgment prediction, deep learning, natural language processing, BERT, data visualization

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Authors: Manea A. I. Alqrad, Alaa Sirwi, Sabrin R. M. Ibrahim, Hossam M. Abdallah, Gamal A. Mohamed

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Phytochemical study of the methanolic extract of the air dried powdered of the parts of Viscum schemperi Engl. (Family: Viscaceae) using different chromatographic techniques led to the isolation of five compounds: -amyrenone (1), betulinic acid (2), (3β)-olean-12-ene-3,23-diol (3), -oleanolic acid (4), and α-oleanolic acid (5). Their structures were established based on physical, chemical, and spectral data. Anti-inflammatory and anti-apoptotic activities of oleanolic acid in a mouse model of acute hepatorenal damage were assessed. This study showed the efficacy of oleanolic acid to counteract thioacetamide-induced hepatic and kidney injury in mice through the reduction of hepatocyte oxidative damage, suppression of inflammation, and apoptosis. More importantly, oleanolic acid suppressed thioacetamide-induced hepatic and kidney injury by inhibiting NF-κB/TNF-α-mediated inflammation/apoptosis and enhancing SIRT1/Nrf2/Heme-oxygenase signalling pathway. These promising pharmacological activities suggest the potential use of oleanolic acid against hepatorenal damage.

Keywords: oleanolic acid, viscum schimperi, thioacetamide, SIRT1/Nrf2/NF-κB, hepatorenal damage

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Authors: Josip Arneric, Blanka Skrabic Peric

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Forecasting of volatility, i.e. returns fluctuations, has been a topic of interest to portfolio managers, option traders and market makers in order to get higher profits or less risky positions. Based on the fact that volatility is time varying in high frequency data and that periods of high volatility tend to cluster, the most common used models are GARCH type models. As standard GARCH models show high volatility persistence, i.e. integrated behaviour of the conditional variance, it is difficult the predict volatility using standard GARCH models. Due to practical limitations of these models different approaches have been proposed in the literature, based on Markov switching models. In such situations models in which the parameters are allowed to change over time are more appropriate because they allow some part of the model to depend on the state of the economy. The empirical analysis demonstrates that Markov switching GARCH model resolves the problem of excessive persistence and outperforms uni-regime GARCH models in forecasting volatility for selected emerging markets.

Keywords: emerging markets, Markov switching, GARCH model, transition probabilities

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Authors: Manavjit Singh Dhindsa, Ursula Das, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

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These days, wildland fires, also known as forest fires, are more prevalent than ever. Wildfires have major repercussions that affect ecosystems, communities, and the environment in several ways. Wildfires lead to habitat destruction and biodiversity loss, affecting ecosystems and causing soil erosion. They also contribute to poor air quality by releasing smoke and pollutants that pose health risks, especially for individuals with respiratory conditions. Wildfires can damage infrastructure, disrupt communities, and cause economic losses. The economic impact of firefighting efforts, combined with their direct effects on forestry and agriculture, causes significant financial difficulties for the areas impacted. This research explores different forest fire spread models and presents a comprehensive review of various techniques and methodologies used in the field. A forest fire spread model is a computational or mathematical representation that is used to simulate and predict the behavior of a forest fire. By applying scientific concepts and data from empirical studies, these models attempt to capture the intricate dynamics of how a fire spreads, taking into consideration a variety of factors like weather patterns, topography, fuel types, and environmental conditions. These models assist authorities in understanding and forecasting the potential trajectory and intensity of a wildfire. Emphasizing the need for a comprehensive understanding of wildfire dynamics, this research explores the approaches, assumptions, and findings derived from various models. By using a comparison approach, a critical analysis is provided by identifying patterns, strengths, and weaknesses among these models. The purpose of the survey is to further wildfire research and management techniques. Decision-makers, researchers, and practitioners can benefit from the useful insights that are provided by synthesizing established information. Fire spread models provide insights into potential fire behavior, facilitating authorities to make informed decisions about evacuation activities, allocating resources for fire-fighting efforts, and planning for preventive actions. Wildfire spread models are also useful in post-wildfire mitigation strategies as they help in assessing the fire's severity, determining high-risk regions for post-fire dangers, and forecasting soil erosion trends. The analysis highlights the importance of customized modeling approaches for various circumstances and promotes our understanding of the way forest fires spread. Some of the known models in this field are Rothermel’s wildland fuel model, FARSITE, WRF-SFIRE, FIRETEC, FlamMap, FSPro, cellular automata model, and others. The key characteristics that these models consider include weather (includes factors such as wind speed and direction), topography (includes factors like landscape elevation), and fuel availability (includes factors like types of vegetation) among other factors. The models discussed are physics-based, data-driven, or hybrid models, also utilizing ML techniques like attention-based neural networks to enhance the performance of the model. In order to lessen the destructive effects of forest fires, this initiative aims to promote the development of more precise prediction tools and effective management techniques. The survey expands its scope to address the practical needs of numerous stakeholders. Access to enhanced early warning systems enables decision-makers to take prompt action. Emergency responders benefit from improved resource allocation strategies, strengthening the efficacy of firefighting efforts.

Keywords: artificial intelligence, deep learning, forest fire management, fire risk assessment, fire simulation, machine learning, remote sensing, wildfire modeling

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Authors: Mehdi Mirzaei Alavijeh, Farzad Jalilian

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Background: Marijuana is the most widely used illicit drug worldwide, especially among adolescents and young adults, which can cause numerous complications. The aim of this study was to determine the pattern, motivation use, and factors related to marijuana use among Iranian youths based on the integrative model of behavioral prediction Methods: A cross-sectional study was conducted among 174 youths marijuana user in Kermanshah County and Isfahan County, during summer 2014 which was selected with the convenience sampling for participation in this study. A self-reporting questionnaire was applied for collecting data. Data were analyzed by SPSS version 21 using bivariate correlations and linear regression statistical tests. Results: The mean marijuana use of respondents was 4.60 times at during week [95% CI: 4.06, 5.15]. Linear regression statistical showed, the structures of integrative model of behavioral prediction accounted for 36% of the variation in the outcome measure of the marijuana use at during week (R2 = 36% & P < 0.001); and among them attitude, marijuana refuse, and subjective norms were a stronger predictors. Conclusion: Comprehensive health education and prevention programs need to emphasize on cognitive factors that predict youth’s health-related behaviors. Based on our findings it seems, designing educational and behavioral intervention for reducing positive belief about marijuana, marijuana self-efficacy refuse promotion and reduce subjective norms encourage marijuana use has an effective potential to protect youths marijuana use.

Keywords: marijuana, youth, integrative model of behavioral prediction, Iran

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Authors: M. Palizvan, M. T. Abadi, M. H. Sadr

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Due to variations in damage mechanisms in the microscale, the behavior of fiber-reinforced composites is nonlinear and difficult to model. To make use of computational advantages, homogenization method is applied to the micro-scale model in order to minimize the cost at the expense of detail of local microscale phenomena. In this paper, the effective stiffness is calculated using the homogenization of nonlinear behavior of a composite representative volume element (RVE) containing fiber-matrix debonding. The damage modes for the RVE are considered by using cohesive elements and contacts for the cohesive behavior of the interface between fiber and matrix. To predict more realistic responses of composite materials, different random distributions of fibers are proposed besides square and hexagonal arrays. It was shown that in some cases, there is quite different damage behavior in different fiber distributions. A comprehensive comparison has been made between different graphs.

Keywords: homogenization, cohesive zone model, fiber-matrix debonding, RVE

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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: Lourdes Yareth Herrera-Chavez, Alberto Ruiz, Victor H. Lopez

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Superalloys are used in components that operate at high temperatures such as pressure vessels and heat exchanger tubing. Design standards for these components must consider creep resistance among other criteria. Fusion welding processes are commonly used in the industry to join such components. Fusion processes commonly generate three distinctive zones, i.e. heat affected zone (HAZ), namely weld metal (WM) and base metal (BM). In nickel-based superalloy, the microstructure developed during fusion welding dictates the mechanical response of the welded component and it is very important to establish these effects in the mechanical response of the component. In this work, two plates of Inconel 600 superalloy were Gas Metal Arc Welded (GMAW). Creep samples were cut and milled to specifications and creep tested at a temperature (650 °C) using stress level of 350, 300, 275, 250 and 200 MPa. Microstructural analysis results showed a progressive creep damage evolution that depends on the stress levels with a preferential accumulation of creep damage at the heat affected zone where the creep rupture preferentially occurs owing to an austenitic matrix with grain boundary precipitated of the type Cr23C6. The fractured surfaces showed dimple patterns of cavity and voids. Results indicated that the damage mechanism is due to cavity growth by the combined effect of the power law and diffusion creep.

Keywords: austenitic microstructure, creep damage evolution, heat affected zone, vickers microhardness

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Authors: E. Benga, T. Tengen, A. Alugongo

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Periodic and continuous inventory models are the two classical management tools used to handle inventories. These models have advantages and disadvantages. The implementation of both continuous (r,Q) inventory and periodic (R, S) inventory models in most manufacturing plants comes with higher cost. Such high inventory costs are due to the fact that most manufacturing plants are not flexible enough. Since demand and lead-time are two important variables of every inventory models, their effect on the flexibility of the manufacturing plant matter most. Unfortunately, these effects are not clearly understood by managers. The reason is that the decision parameters of the continuous (r, Q) inventory and periodic (R, S) inventory models are not designed to effectively deal with the issues of uncertainties such as poor manufacturing performances, delivery performance supplies performances. There is, therefore, a need to come up with a predictive and hybrid inventory model that can combine in some sense the feature of the aforementioned inventory models. A linear combination technique is used to hybridize both continuous (r, Q) inventory and periodic (R, S) inventory models. The behavior of such hybrid inventory model is described by a differential equation and then optimized. From the results obtained after simulation, the continuous (r, Q) inventory model is more effective than the periodic (R, S) inventory models in the short run, but this difference changes as time goes by. Because the hybrid inventory model is more cost effective than the continuous (r,Q) inventory and periodic (R, S) inventory models in long run, it should be implemented for strategic decisions.

Keywords: periodic inventory, continuous inventory, hybrid inventory, optimization, manufacturing plant

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Authors: Percy Galvez, Anatoly Petukhin, Paul Somerville, Ken Miyakoshi, Kojiro Irikura, Daniel Peter

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Physics-based dynamic rupture modelling is necessary for estimating parameters such as rupture velocity and slip rate function that are important for ground motion simulation, but poorly resolved by observations, e.g. by seismic source inversion. In order to generate a large number of physically self-consistent rupture models, whose rupture process is consistent with the spatio-temporal heterogeneity of past earthquakes, we use multicycle simulations under the heterogeneous rate-and-state (RS) friction law for a 45deg dip-slip fault. We performed a parametrization study by fully dynamic rupture modeling, and then, a set of spontaneous source models was generated in a large magnitude range (Mw > 7.0). In order to validate rupture models, we compare the source scaling relations vs. seismic moment Mo for the modeled rupture area S, as well as average slip Dave and the slip asperity area Sa, with similar scaling relations from the source inversions. Ground motions were also computed from our models. Their peak ground velocities (PGV) agree well with the GMPE values. We obtained good agreement of the permanent surface offset values with empirical relations. From the heterogeneous rupture models, we analyzed parameters, which are critical for ground motion simulations, i.e. distributions of slip, slip rate, rupture initiation points, rupture velocities, and source time functions. We studied cross-correlations between them and with the friction weakening distance Dc value, the only initial heterogeneity parameter in our modeling. The main findings are: (1) high slip-rate areas coincide with or are located on an outer edge of the large slip areas, (2) ruptures have a tendency to initiate in small Dc areas, and (3) high slip-rate areas correlate with areas of small Dc, large rupture velocity and short rise-time.

Keywords: earthquake dynamics, strong ground motion prediction, seismic engineering, source characterization

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Authors: Kanchan Mondal, Dasharath Koulage, Dattatray Manerikar, Asmita Ghate

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This paper presents an additive reliability model using warranty data and Finite Element Analysis (FEA) data. Warranty data for any product gives insight to its underlying issues. This is often used by Reliability Engineers to build prediction model to forecast failure rate of parts. But there is one major limitation in using warranty data for prediction. Warranty periods constitute only a small fraction of total lifetime of a product, most of the time it covers only the infant mortality and useful life zone of a bathtub curve. Predicting with warranty data alone in these cases is not generally provide results with desired accuracy. Failure rate of a mechanical part is driven by random issues initially and wear-out or usage related issues at later stages of the lifetime. For better predictability of failure rate, one need to explore the failure rate behavior at wear out zone of a bathtub curve. Due to cost and time constraints, it is not always possible to test samples till failure, but FEA-Fatigue analysis can provide the failure rate behavior of a part much beyond warranty period in a quicker time and at lesser cost. In this work, the authors proposed an Additive Weibull Model, which make use of both warranty and FEA fatigue analysis data for predicting failure rates. It involves modeling of two data sets of a part, one with existing warranty claims and other with fatigue life data. Hazard rate base Weibull estimation has been used for the modeling the warranty data whereas S-N curved based Weibull parameter estimation is used for FEA data. Two separate Weibull models’ parameters are estimated and combined to form the proposed Additive Weibull Model for prediction.

Keywords: bathtub curve, fatigue, FEA, reliability, warranty, Weibull

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Authors: Sendi Zohra, Belhadjsalah Hedi, Labergere Carl, Saanouni Khemais

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The modeling of mechanical problems including both material and geometric nonlinearities with Finite Element Method (FEM) remains challenging. Meshless methods offer special properties to get rid of well-known drawbacks of the FEM. The main objective of Meshless Methods is to eliminate the difficulty of meshing and remeshing the entire structure by simply insertion or deletion of nodes, and alleviate other problems associated with the FEM, such as element distortion, locking and others. In this study, a robust numerical implementation of an Element Free Galerkin Method for an elastoplastic coupled to damage problem is presented. Several results issued from the numerical simulations by a DynamicExplicit resolution scheme are analyzed and critically compared with Element Finite Method results. Finally, different numerical examples are carried out to demonstrate the efficiency of this method.

Keywords: damage, dynamic explicit, elastoplasticity, isotropic hardening, meshless

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Authors: Itti Bist, Snehasis Bhakta, Di Jiang, Tia E. Keyes, Aaron Martin, Robert J. Forster, James F. Rusling

Abstract:

DNA damage from metabolites of lipophilic drugs and pollutants, generated by enzymes, represents a major toxicity pathway in humans. These metabolites can react with DNA to form either 8-oxo-7,8-dihydro-2-deoxyguanosine (8-oxodG), which is the oxidative product of DNA or covalent DNA adducts, both of which are genotoxic and hence considered important biomarkers to detect cancer in humans. Therefore, detecting reactions of metabolites with DNA is an effective approach for the safety assessment of new chemicals and drugs. Here we describe a novel electrochemiluminescent (ECL) sensor array which can detect DNA oxidation and chemical DNA damage in a single array, facilitating a more accurate diagnostic tool for genotoxicity screening. Layer-by-layer assembly of DNA and enzyme are assembled on the pyrolytic graphite array which is housed in a microfluidic device for sequential detection of two type of the DNA damages. Multiple enzyme reactions are run on test compounds using the array, generating toxic metabolites in situ. These metabolites react with DNA in the films to cause DNA oxidation and chemical DNA damage which are detected by ECL generating osmium compound and ruthenium polymer, respectively. The method is further validated by the formation of 8-oxodG and DNA adduct using similar films of DNA/enzyme on magnetic bead biocolloid reactors, hydrolyzing the DNA, and analyzing by liquid chromatography-mass spectrometry (LC-MS). Hence, this combined DNA/enzyme array/LC-MS approach can efficiently explore metabolic genotoxic pathways for drugs and environmental chemicals.

Keywords: biosensor, electrochemiluminescence, DNA damage, microfluidic array

Procedia PDF Downloads 363

Authors: Zeeshan Muzammal, Usama Latif, Fouzia Younas, Syed Muhammad Hassan, Samia Razaq

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Unexpected rainfall has always been an issue in the lifetime of crops and brings destruction for the farmers who harvest them. Unfortunately, Pakistan is one of the countries in which untimely rain impacts badly on crops like wash out of seeds and pesticides etc. Pakistan’s GDP is related to agriculture, especially in rural areas farmers sometimes quit farming because leverage of huge loss to their crops. Through our surveys and research, we came to know that farmers in the rural areas of Pakistan need rain information to avoid damages to their crops from rain. We developed a prototype using ICTs to inform the farmers about rain one week in advance. Our proposed solution has two ways of informing the farmers. In first we send daily messages about weekly prediction and also designed a helpline where they can call us to ask about possibility of rain.

Keywords: ICTD, farmers, mobile based, Pakistan, rural areas, weather prediction

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Authors: José Leandro Melgar Néris, Claudinéia Brazil, Luciane Teresa Salvi, Isabel Cristina Damin

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The impact the climate change is not recent, the main variable in the hydrological cycle is the sequence and shortage of a drought, which has a significant impact on the socioeconomic, agricultural and environmental spheres. This study aims to characterize and quantify, based on precipitation climatic projections, the rainy and dry events in the region of the Uruguay River Basin, through the Standardized Precipitation Index (SPI). The database is the image that is part of the Intercomparison of Model Models, Phase 5 (CMIP5), which provides condition prediction models, organized according to the Representative Routes of Concentration (CPR). Compared to the normal set of climates in the Uruguay River Watershed through precipitation projections, seasonal precipitation increases for all proposed scenarios, with a low climate trend. From the data of this research, the idea is that this article can be used to support research and the responsible bodies can use it as a subsidy for mitigation measures in other hydrographic basins.

Keywords: climate change, climatic model, dry events, precipitation projections

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Authors: Ritu Karwasra, Surender Singh

Abstract:

Inflammation is an important physiological response of the body’s self-defense system that helps in eliminating and protecting organism from harmful stimuli and in tissue repair. It is a highly regulated protective response which helps in eliminating the initial cause of cell injury, and initiates the process of repair. The present study was designed to evaluate the ameliorative effect of pomegranate rind extract on pain and inflammation. Hydroalcoholic standardized rind extract of pomegranate at doses 50, 100 and 200 mg/kg and indomethacin (3 mg/kg) was tested against eddy’s hot plate induced thermal algesia, carrageenan (acute inflammation) and Complete Freund’s Adjuvant (chronic inflammation) induced models in Wistar rats. Parameters analyzed were inhibition of paw edema, measurement of joint diameter, levels of GSH, TBARS, SOD, TNF-α, radiographic imaging, tissue histology and synovial expression of pro-inflammatory cytokine receptor (TNF-R1). Radiological and light microscopical analysis were carried out to find out the bone damage in CFA-induced chronic inflammatory model. Findings of the present study revealed that pomegranate rind extract at a dose of 200 mg/kg caused a significant (p<0.05) reduction in paw swelling in both the inflammatory models. Nociceptive threshold was also significantly (p<0.05) improved. Immunohistochemical analysis of TNF-R1 in CFA-induced group showed elevated level, whereas reduction in level of TNF-R1 was observed in pomegranate (200 mg/kg). Henceforth, we might say that pomegranate produced a dose-dependent reduction in inflammation and pain along with the reduction in levels of oxidative stress markers and tissue histology, and the effect was found to be comparable to that of indomethacin. Thus, it can be concluded that pomegranate is a potential therapeutic target in the pathogenesis of inflammation and pain, and punicalagin is the major constituents found in rind extract might be responsible for the activity.

Keywords: carrageenan, inflammation, nociceptive-threshold, pomegranate, histopathology

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Authors: Ali Al-Ghalib, Fouad Mohammad

Abstract:

The identification of damage in reinforced concrete structures subjected to incremental cracking performance exploiting vibration data is recognized as a challenging topic in the published and heavily cited literature. Therefore, this paper attempts to shine light on the extent of dynamic methods when applied to reinforced concrete beams simulated with various scenarios of defects. For this purpose, three different reinforced concrete beams are tested through the course of the study. The three beams are loaded statically to failure in incremental successive load cycles and later rehabilitated. After each static load stage, the beams are tested under free-free support condition using experimental modal analysis. The beams were all of the same length and cross-sectional area (2.0x0.14x0.09)m, but they were different in concrete compressive strength and the type of damage presented. The experimental modal parameters as damage identification parameters were showed computationally expensive, time consuming and require substantial inputs and considerable expertise. Nonetheless, they were proved plausible for the condition monitoring of the current case study as well as structural changes in the course of progressive loads. It was accentuated that a satisfactory localization and quantification for structural changes (Level 2 and Level 3 of damage identification problem) can only be achieved reasonably through considering frequencies and mode shapes of a system in a proper analytical model. A convenient post analysis process for various datasets of vibration measurements for the three beams is conducted in order to extract, check and correlate the basic modal parameters; namely, natural frequency, modal damping and mode shapes. The results of the extracted modal parameters and their combination are utilized and discussed in this research as quantification parameters.

Keywords: experimental modal analysis, damage identification, structural health monitoring, reinforced concrete beam

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Authors: Armineh Nourbakhsh, Sameena Shah, Carolyn Rose

Abstract:

Layout-aware language models have been used to create multimodal representations for documents that are in image form, achieving relatively high accuracy in document understanding tasks. However, the large number of parameters in the resulting models makes building and using them prohibitive without access to high-performing processing units with large memory capacity. We propose an alternative approach that can create efficient representations without the need for a neural visual backbone. This leads to an 80% reduction in the number of parameters compared to the smallest SOTA model, widely expanding applicability. In addition, our layout embeddings are pre-trained on spatial and visual cues alone and only fused with text embeddings in downstream tasks, which can facilitate applicability to low-resource of multi-lingual domains. Despite using 2.5% of training data, we show competitive performance on two form understanding tasks: semantic labeling and link prediction.

Keywords: layout understanding, form understanding, multimodal document understanding, bias-augmented attention

Procedia PDF Downloads 143

Authors: Mohamed Gaith, Zaid Haddadin, Abdulah Wahbe, Mahmoud Hamam, Mahmoud Qunees, Mohammad Al Khatib, Mohammad Bsaileh, Abd Al-Aziz Jaber, Ahmad Aqra’a

Abstract:

The crack is one of the most common types of failure in pipelines that convey fluid, and early detection of the crack may assist to avoid the piping system from experiencing catastrophic damage, which would otherwise be fatal. The influence of flow velocity and the presence of a crack on the performance of a tapered simply supported pipe containing moving fluid is explored using the finite element approach in this study. ANSYS software is used to simulate the pipe as Bernoulli's beam theory. In this paper, the fluctuation of natural frequencies and matching mode shapes for various scenarios owing to changes in fluid speed and the presence of damage is discussed in detail.

Keywords: damage detection, finite element, tapered pipe, vibration characteristics

Procedia PDF Downloads 164

Authors: Ahmad R. Zamani, Luigi Sanguigno, Angelo R. Maligno

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A 3D woven composite, designed for automotive applications, is studied using Abaqus Finite Element (FE) software suite. Python scripts were developed to build FE models of the woven composite in Complete Abaqus Environment (CAE). They can read TexGen or WiseTex files and automatically generate consistent meshes of the fabric and the matrix. A user menu is provided to help define parameters for the FE models, such as type and size of the elements in fabric and matrix as well as the type of matrix-fabric interaction. Node-to-node constraints were imposed to guarantee periodicity of the deformed shapes at the boundaries of the representative volume element of the composite. Tensile loads in three axes and biaxial loads in x-y directions have been applied at different Fibre Volume Fractions (FVFs). A simple damage model was implemented via an Abaqus user material (UMAT) subroutine. Existing tools for homogenization were also used, including voxel mesh generation from TexGen as well as Abaqus Micromechanics plugin. Linear relations between homogenised elastic properties and the FVFs are given. The FE models of composite exhibited balanced behaviour with respect to warp and weft directions in terms of both stiffness and strength.

Keywords: 3D woven composite (3DWC), meso-scale finite element model, homogenisation of elastic material properties, Abaqus Python scripting

Procedia PDF Downloads 140

Authors: Xiao Zhou, Jianlin Cheng

Abstract:

A single amino acid mutation can have a significant impact on the stability of protein structure. Thus, the prediction of protein stability change induced by single site mutations is critical and useful for studying protein function and structure. Here, we presented a deep learning network with the dropout technique for predicting protein stability changes upon single amino acid substitution. While using only protein sequence as input, the overall prediction accuracy of the method on a standard benchmark is >85%, which is higher than existing sequence-based methods and is comparable to the methods that use not only protein sequence but also tertiary structure, pH value and temperature. The results demonstrate that deep learning is a promising technique for protein stability prediction. The good performance of this sequence-based method makes it a valuable tool for predicting the impact of mutations on most proteins whose experimental structures are not available. Both the downloadable software package and the user-friendly web server (DNpro) that implement the method for predicting protein stability changes induced by amino acid mutations are freely available for the community to use.

Keywords: bioinformatics, deep learning, protein stability prediction, biological data mining

Procedia PDF Downloads 457