Search results for: missing at random (MAR)

Authors: S. Gokul Prassad, S. Aakash, K. Malar Mohan

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In the process to cope with the challenges faced by the automobile industry in providing ride comfort, the electronics and control systems play a vital role. The control systems in an automobile monitor various parameters, controls the performances of the systems, thereby providing better handling characteristics. The automobile suspension system is one of the main systems that ensure the safety, stability and comfort of the passengers. The system is solely responsible for the isolation of the entire automobile from harmful road vibrations. Thus, integration of the control systems in the automobile suspension system would enhance its performance. The diverse road conditions of India demand the need of an efficient suspension system which can provide optimum ride comfort in all road conditions. For any passenger vehicle, the design of the suspension system plays a very important role in assuring the ride comfort and handling characteristics. In recent years, the air suspension system is preferred over the conventional suspension systems to ensure ride comfort. In this article, the ride comfort of the adaptive suspension system is compared with that of the passive suspension system. The schema is created in MATLAB/Simulink environment. The system is controlled by a proportional integral differential controller. Tuning of the controller was done with the Particle Swarm Optimization (PSO) algorithm, since it suited the problem best. Ziegler-Nichols and Modified Ziegler-Nichols tuning methods were also tried and compared. Both the static responses and dynamic responses of the systems were calculated. Various random road profiles as per ISO 8608 standard are modelled in the MATLAB environment and their responses plotted. Open-loop and closed loop responses of the random roads, various bumps and pot holes are also plotted. The simulation results of the proposed design are compared with the available passive suspension system. The obtained results show that the proposed adaptive suspension system is efficient in controlling the maximum over shoot and the settling time of the system is reduced enormously.

Keywords: automobile suspension, MATLAB, control system, PID, PSO

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Authors: Zimu Li, Zheng Wan

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The Suez Canal plays an important role in global shipping networks and is one of the most frequently used waterways in the world. The 2021 canal obstruction by ship Ever Given in March 2021, however, completed blocked the Suez Canal for a week and caused significant disruption to world trade. Therefore, it is very important to quantitatively analyze the impact of the accident on the robustness of the global shipping network. However, the current research on maritime transportation networks is usually limited to local or small-scale networks in a certain region. Based on the complex network theory, this study establishes a global shipping complex network covering 2713 nodes and 137830 edges by using the real trajectory data of the global marine transport ship automatic identification system in 2018. At the same time, two attack modes, deliberate (Suez Canal Blocking) and random, are defined to calculate the changes in network node degree, eccentricity, clustering coefficient, network density, network isolated nodes, betweenness centrality, and closeness centrality under the two attack modes, and quantitatively analyze the actual impact of Suez Canal Blocking on the robustness of global shipping network. The results of the network robustness analysis show that Suez Canal blocking was more destructive to the shipping network than random attacks of the same scale. The network connectivity and accessibility decreased significantly, and the decline decreased with the distance between the port and the canal, showing the phenomenon of distance attenuation. This study further analyzes the impact of the blocking of the Suez Canal on Chinese ports and finds that the blocking of the Suez Canal significantly interferes withChina's shipping network and seriously affects China's normal trade activities. Finally, the impact of the global supply chain is analyzed, and it is found that blocking the canal will seriously damage the normal operation of the global supply chain.

Keywords: global shipping networks, ship AIS trajectory data, main channel, complex network, eigenvalue change

Procedia PDF Downloads 156

Authors: Kishor Chandra Kandpal, Amit Kumar

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The Indian Himalaya region harbours approximately 1748 plants of medicinal importance, and as per International Union for Conservation of Nature (IUCN), the 112 plant species among these are threatened and endangered. To ease the pressure on these plants, the government of India is encouraging its in-situ cultivation. The Saussurea costus, Valeriana jatamansi, and Picrorhiza kurroa have also been prioritized for large scale cultivation owing to their market demand, conservation value and medicinal properties. These species are found from 1000 m to 4000 m elevation ranges in the Indian Himalaya. Identification of these plants in the field requires taxonomic skills, which is one of the major bottleneck in the conservation and management of these plants. In recent years, Hyperspectral remote sensing techniques have been precisely used for the discrimination of plant species with the help of their unique spectral signatures. In this background, a spectral library of the above 03 medicinal plants was prepared by collecting the spectral data using a handheld spectroradiometer (325 to 1075 nm) from farmer’s fields of Himachal Pradesh and Uttarakhand states of Indian Himalaya. The Random forest (RF) model was implied on the spectral data for the classification of the medicinal plants. The 80:20 standard split ratio was followed for training and validation of the RF model, which resulted in training accuracy of 84.39 % (kappa coefficient = 0.72) and testing accuracy of 85.29 % (kappa coefficient = 0.77). This RF classifier has identified green (555 to 598 nm), red (605 nm), and near-infrared (725 to 840 nm) wavelength regions suitable for the discrimination of these species. The findings of this study have provided a technique for rapid and onsite identification of the above medicinal plants in the field. This will also be a key input for the classification of hyperspectral remote sensing images for mapping of these species in farmer’s field on a regional scale. This is a pioneer study in the Indian Himalaya region for medicinal plants in which the applicability of hyperspectral remote sensing has been explored.

Keywords: himalaya, hyperspectral remote sensing, machine learning; medicinal plants, random forests

Procedia PDF Downloads 185

Authors: Nouini Adrâa

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Background: The management of dizziness and vertigo can be challenging in the emergency department (ED). It is important to rapidly diagnose vertebrobasilar stroke (VBS), as therapeutic options such as thrombolysis and anticoagulation require prompt decisions. Objective: This study aims to assess the rate of misdiagnosis in patients with dizziness caused by VBS in the ED. Methods and Results: The cohort was comprised of 82 patients with a mean age of 55 years; 51% were women and 49% were men. Among dizzy patients, 15% had VBS. We used Cohen’s kappa test to quantify the agreement between two raters – namely, emergency physicians and neurologists – regarding the causes of dizziness in the ED. The agreement between emergency physicians and neurologists is low for the final diagnosis of central vertigo disorders and moderate for the final diagnosis of VBS. The sensitivity of ED clinal examination for benign conditions such as BPPV was low at 56%. The positive predictive value of the ED clinical examination for VBS was also low at 50%. Conclusion: There is a substantial rate of misdiagnosis in patients with dizziness caused by VBS in the ED. To reduce the number of missing diagnoses of VBS in the future, there is a need to train emergency physicians in neuro vestibular examinations, including the HINTS examination for acute vestibular syndrome (AVS) and the Dix-Hallpike (DH) maneuver for episodic vestibular syndrome. Using video head impulse tests could help reduce the rate of misdiagnosis of VBS in the ED.

Keywords: dizziness, vertigo, vestibular disease, emergency

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Authors: Simone Tedeschi, Francesco Crespi, Paolo Liberati, Massimo Paradiso, Antonio Sciala

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This paper aims at contributing to the understanding of smokers’ responses to cigarette prices increases with a focus on heterogeneity, both across individuals and price levels. To do this, a stated preference quasi-experimental design grounded in a random utility framework is proposed to evaluate the effect on smokers’ utility of the price level and variation, along with social conditioning and health impact perception. The analysis is based on individual-level data drawn from a unique survey gathering very detailed information on Italian smokers’ habits. In particular, qualitative information on the individual reactions triggered by changes in prices of different magnitude and composition are exploited. The main findings stemming from the analysis are the following; the average price elasticity of cigarette consumption is comparable with previous estimates for advanced economies (-.32). However, the decomposition of this result across five latent-classes of smokers, reveals extreme heterogeneity in terms of price responsiveness, implying a potential price elasticity that ranges between 0.05 to almost 1. Such heterogeneity is in part explained by observable characteristics such as age, income, gender, education as well as (current and lagged) smoking intensity. Moreover, price responsiveness is far from being independent from the size of the prospected price increase. Finally, by comparing even and uneven price variations, it is shown that uniform across-brand price increases are able to limit the scope of product substitutions and downgrade. Estimated price-response heterogeneity has significant implications for tax policy. Among them, first, it provides evidence and a rationale for why the aggregate price elasticity is likely to follow a strictly increasing pattern as a function of the experienced price variation. This information is crucial for forecasting the effect of a given tax-driven price change on tax revenue. Second, it provides some guidance on how to design excise tax reforms to balance public health and revenue goals.

Keywords: smoking behaviour, preference heterogeneity, price responsiveness, cigarette taxation, random utility models

Procedia PDF Downloads 135

Authors: Jéssica Ferreira Rodrigues, Júlio Silvio De Sousa Bueno Filho, Vanessa Rios De Souza, Ana Carla Marques Pinheiro

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This study aimed to evaluate the implications of the block size and testing order on efficiency and precision of preference estimation for Dulce de leche samples. Efficiency was defined as the inverse of the average variance of pairwise comparisons among treatments. Precision was defined as the inverse of the variance of treatment means (or effects) estimates. The experiment was originally designed to test 16 treatments as a series of 8 Sudoku 16x16 designs being 4 randomized independently and 4 others in the reverse order, to yield balance in testing order. Linear mixed models were assigned to the whole experiment with 112 testers and all their grades, as well as their partially balanced subgroups, namely: a) experiment with the four initial EU; b) experiment with EU 5 to 8; c) experiment with EU 9 to 12; and b) experiment with EU 13 to 16. To record responses we used a nine-point hedonic scale, it was assumed a mixed linear model analysis with random tester and treatments effects and with fixed test order effect. Analysis of a cumulative random effects probit link model was very similar, with essentially no different conclusions and for simplicity, we present the results using Gaussian assumption. R-CRAN library lme4 and its function lmer (Fit Linear Mixed-Effects Models) was used for the mixed models and libraries Bayesthresh (default Gaussian threshold function) and ordinal with the function clmm (Cumulative Link Mixed Model) was used to check Bayesian analysis of threshold models and cumulative link probit models. It was noted that the number of samples tested in the same session can influence the acceptance level, underestimating the acceptance. However, proving a large number of samples can help to improve the samples discrimination.

Keywords: acceptance, block size, mixed linear model, testing order, testing order

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Authors: Pei Yi Lin

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Objective: The objective of this study is to use machine learning methods to build an early prediction classifier model for acute delirium to improve the quality of medical care for intensive care patients. Background: Delirium is a common acute and sudden disturbance of consciousness in critically ill patients. After the occurrence, it is easy to prolong the length of hospital stay and increase medical costs and mortality. In 2021, the incidence of delirium in the intensive care unit of internal medicine was as high as 59.78%, which indirectly prolonged the average length of hospital stay by 8.28 days, and the mortality rate is about 2.22% in the past three years. Therefore, it is expected to build a delirium prediction classifier through big data analysis and machine learning methods to detect delirium early. Method: This study is a retrospective study, using the artificial intelligence big data database to extract the characteristic factors related to delirium in intensive care unit patients and let the machine learn. The study included patients aged over 20 years old who were admitted to the intensive care unit between May 1, 2022, and December 31, 2022, excluding GCS assessment <4 points, admission to ICU for less than 24 hours, and CAM-ICU evaluation. The CAMICU delirium assessment results every 8 hours within 30 days of hospitalization are regarded as an event, and the cumulative data from ICU admission to the prediction time point are extracted to predict the possibility of delirium occurring in the next 8 hours, and collect a total of 63,754 research case data, extract 12 feature selections to train the model, including age, sex, average ICU stay hours, visual and auditory abnormalities, RASS assessment score, APACHE-II Score score, number of invasive catheters indwelling, restraint and sedative and hypnotic drugs. Through feature data cleaning, processing and KNN interpolation method supplementation, a total of 54595 research case events were extracted to provide machine learning model analysis, using the research events from May 01 to November 30, 2022, as the model training data, 80% of which is the training set for model training, and 20% for the internal verification of the verification set, and then from December 01 to December 2022 The CU research event on the 31st is an external verification set data, and finally the model inference and performance evaluation are performed, and then the model has trained again by adjusting the model parameters. Results: In this study, XG Boost, Random Forest, Logistic Regression, and Decision Tree were used to analyze and compare four machine learning models. The average accuracy rate of internal verification was highest in Random Forest (AUC=0.86), and the average accuracy rate of external verification was in Random Forest and XG Boost was the highest, AUC was 0.86, and the average accuracy of cross-validation was the highest in Random Forest (ACC=0.77). Conclusion: Clinically, medical staff usually conduct CAM-ICU assessments at the bedside of critically ill patients in clinical practice, but there is a lack of machine learning classification methods to assist ICU patients in real-time assessment, resulting in the inability to provide more objective and continuous monitoring data to assist Clinical staff can more accurately identify and predict the occurrence of delirium in patients. It is hoped that the development and construction of predictive models through machine learning can predict delirium early and immediately, make clinical decisions at the best time, and cooperate with PADIS delirium care measures to provide individualized non-drug interventional care measures to maintain patient safety, and then Improve the quality of care.

Keywords: critically ill patients, machine learning methods, delirium prediction, classifier model

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Authors: Margaret S. Wright

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Background/Significance: During many recent public health emergencies/disasters, public health nursing data has been missing or delayed, potentially impacting the decision-making and response. Data used as evidence for decision-making in response, planning, and mitigation has been erratic and slow, decreasing the ability to respond. Methodology: Applying best practices in data management and data use in public health settings, and guided by the concepts outlined in ‘Disaster Standards of Care’ models leads to the development of recommendations for a model of best practices in data management and use in public health disasters/emergencies by public health nurses. As the ‘patient’ in public health disasters/emergencies is the community (local, regional or national), guidelines for patient documentation are incorporated in the recommendations. Findings: Using model public health nurses could better plan how to prepare for, respond to, and mitigate disasters in their communities, and better participate in decision-making in all three phases bringing public health nursing data to the discussion as part of the evidence base for decision-making.

Keywords: data management, decision making, disaster planning documentation, public health nursing

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Authors: Fidelia A. Orji, Julita Vassileva

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This research aims to develop machine learning models for students' academic performance and study strategy prediction, which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) used in building the models are chosen based on prior studies, which revealed that the attributes are essential in students’ learning process. Previous studies revealed the individual effects of each of these attributes on students’ learning progress. However, few studies have investigated the combined effect of the attributes in predicting student study strategy and academic performance to reduce the dropout rate. To bridge this gap, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Keywords: classification models, learning strategy, predictive modeling, regression models, student academic performance, student motivation, supervised machine learning

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Authors: Christel Elisabeth Bonin

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The score of the 2 ½ hour ‘magnum opus’ named ‘Epitaph’ was reconstructed 10 years after Charles Mingus’ death in 1979. Most of the movements were probably composed in the late 1950s. As the finale was missing, Gunther Schuller, the conductor of the world premiere in 1989, decided to improvise one with the orchestra, using Mingus as a guide. The aim of this paper is to analyze ‘Main Score Part I ‘ and ‘Main Score Part II’ and to look into the score of Mingus’ reconstructed compositions under particular observation of the new finale, ‘Main Score Reprise’. There, Mingus left instructions for a return to the opening section of ‘Epitaph’. By examining ‘Epitaph’ in the historical context of Jazz between 1955 to 1967 and the 1980s and comparing the finale of ‘Epitaph’, created - or better said: improvised - by the musicians of the 1989 world premiere with the opening section, at first it will be interesting to discover at which point Gunther Schuller followed Mingus creative process and brought it to life in 1989. Finally, it will be speculated if Charles Mingus composition still represents a foresight of Jazz nearly 30 years after its creation.

Keywords: epitaph, Charles Mingus, Gunter Schuller, jazz reception, bebop, hardbop, Duke Ellington, black, brown and beige, African-American music, free-jazz

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Authors: Husnain Mushtaq, Mary Anjum, Muhammad Aleem

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The main objective of this paper is used to develop a high security in Automatic Teller Machine (ATM). In these system bankers will collect the mobile numbers from the customers and then provide a code on their mobile number. In most country existing ATM machine use the magnetic card reader. The customer is identifying by inserting an ATM card with magnetic card that hold unique information such as card number and some security limitations. By entering a personal identification number, first the customer is authenticated then will access bank account in order to make cash withdraw or other services provided by the bank. Cases of card fraud are another problem once the user’s bank card is missing and the password is stolen, or simply steal a customer’s card & PIN the criminal will draw all cash in very short time, which will being great financial losses in customer, this type of fraud has increase worldwide. So to resolve this problem we are going to provide the solution using “Mobile SMS code” and ATM “PIN code” in order to improve the verify the security of customers using ATM system and confidence in the banking area.

Keywords: PIN, inquiry, biometric, magnetic strip, iris recognition, face recognition

Procedia PDF Downloads 343

Authors: Sadeeq Launi

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NGOs, the world over, have been recognized as part of the institutions that complement government activities in providing services to the people, particularly in respect of human development. This study examined the role played by the NGOs in human development in Bauchi State, Nigeria, between 2004 and 2013. The emphasis was on reproductive health and access to education role of the selected NGOs. All the research questions, objectives and hypotheses were stated in line with these variables. The theoretical framework that guided the study was the participatory development approach. Being a survey research, data were generated from both primary and secondary sources with questionnaires and interviews as the instruments for generating the primary data. The population of the study was made up of the staff of the selected NGOs, beneficiaries, health staff and school teachers in Bauchi State. The sample drawn from these categories were 90, 107 and 148 units respectively. Stratified random and simple random sampling techniques were adopted for NGOs staff, and Health staff and school teachers data were analyzed quantitatively and qualitatively and hypotheses were tested using Pearson Chi-square test through SPSS computer statistical package. The study revealed that despite the challenges facing NGOs operations in the study area, NGOs rendered services in the areas of health and education This research recommends among others that, both government and people should be more cooperative to NGOs to enable them provide more efficient and effective services. Governments at all levels should be more dedicated to increasing accessibility and affordability of basic education and reproductive health care facilities and services in Bauchi state through committing more resources to the Health and Education sectors, this would support and facilitate the complementary role of NGOs in providing teaching facilities, drugs, and other reproductive health services in the States. More enlightenment campaigns should be carried out by governments to sensitize the public, particularly women on the need to embrace immunization programmes for their children and antenatal care services being provided by both the government and NGOs.

Keywords: access to education, human development, NGOs, reproductive health

Procedia PDF Downloads 159

Authors: Jose A. Alvarez Aldegunde, Adrian Fernandez Sanchez, Matthew D. Menden, Bernardo Vila Rodriguez

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Up to five statistical pre-processings have been carried out considering the pollutant records of the stations present in Cartagena de Indias, Colombia, also taking into account the childhood asthma incidence surveys conducted in hospitals in the city by the Health Ministry of Colombia for this study. These pre-processings have consisted of different techniques such as the determination of the quality of data collection, determination of the quality of the registration network, identification and debugging of errors in data collection, completion of missing data and purified data, as well as the improvement of the time scale of records. The characterization of the quality of the data has been conducted by means of density analysis of the pollutant registration stations using ArcGis Software and through mass balance techniques, making it possible to determine inconsistencies in the records relating the registration data between stations following the linear regression. The results obtained in this process have highlighted the positive quality in the pollutant registration process. Consequently, debugging of errors has allowed us to identify certain data as statistically non-significant in the incidence and series of contamination. This data, together with certain missing records in the series recorded by the measuring stations, have been completed by statistical imputation equations. Following the application of these prior processes, the basic series of incidence data for respiratory disease and pollutant records have allowed the characterization of the influence of pollutants on respiratory diseases such as, for example, childhood asthma. This characterization has been carried out using statistical correlation methods, including visual correlation, simple linear regression correlation and spectral analysis with PAST Software which identifies maximum periodicity cycles and minimums under the formula of the Lomb periodgram. In relation to part of the results obtained, up to eleven maximums and minimums considered contemporary between the incidence records and the particles have been identified taking into account the visual comparison. The spectral analyses that have been performed on the incidence and the PM2.5 have returned a series of similar maximum periods in both registers, which are at a maximum during a period of one year and another every 25 days (0.9 and 0.07 years). The bivariate analysis has managed to characterize the variable "Daily Vehicular Flow" in the ninth position of importance of a total of 55 variables. However, the statistical correlation has not obtained a favorable result, having obtained a low value of the R2 coefficient. The series of analyses conducted has demonstrated the importance of the influence of pollutants such as PM2.5 in the development of childhood asthma in Cartagena. The quantification of the influence of the variables has been able to determine that there is a 56% probability of dependence between PM2.5 and childhood respiratory asthma in Cartagena. Considering this justification, the study could be completed through the application of the BenMap Software, throwing a series of spatial results of interpolated values of the pollutant contamination records that exceeded the established legal limits (represented by homogeneous units up to the neighborhood level) and results of the impact on the exacerbation of pediatric asthma. As a final result, an economic estimate (in Colombian Pesos) of the monthly and individual savings derived from the percentage reduction of the influence of pollutants in relation to visits to the Hospital Emergency Room due to asthma exacerbation in pediatric patients has been granted.

Keywords: Asthma Incidence, BenMap, PM2.5, Statistical Analysis

Procedia PDF Downloads 96

Authors: Semachew M. Kassa, Africa M Geremew, Tezera F. Azmatch, Nandyala Darga Kumar

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Frequency ratio (FR) and analytical hierarchy process (AHP) methods are developed based on past landslide failure points to identify the landslide susceptibility mapping because landslides can seriously harm both the environment and society. However, it is still difficult to select the most efficient method and correctly identify the main driving factors for particular regions. In this study, we used fourteen landslide conditioning factors (LCFs) and five soft computing algorithms, including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Artificial Neural Network (ANN), and Naïve Bayes (NB), to predict the landslide susceptibility at 12.5 m spatial scale. The performance of the RF (F1-score: 0.88, AUC: 0.94), ANN (F1-score: 0.85, AUC: 0.92), and SVM (F1-score: 0.82, AUC: 0.86) methods was significantly better than the LR (F1-score: 0.75, AUC: 0.76) and NB (F1-score: 0.73, AUC: 0.75) method, according to the classification results based on inventory landslide points. The findings also showed that around 35% of the study region was made up of places with high and very high landslide risk (susceptibility greater than 0.5). The very high-risk locations were primarily found in the western and southeastern regions, and all five models showed good agreement and similar geographic distribution patterns in landslide susceptibility. The towns with the highest landslide risk include Amhara Saint Town's western part, the Northern part, and St. Gebreal Church villages, with mean susceptibility values greater than 0.5. However, rainfall, distance to road, and slope were typically among the top leading factors for most villages. The primary contributing factors to landslide vulnerability were slightly varied for the five models. Decision-makers and policy planners can use the information from our study to make informed decisions and establish policies. It also suggests that various places should take different safeguards to reduce or prevent serious damage from landslide events.

Keywords: artificial neural network, logistic regression, landslide susceptibility, naïve Bayes, random forest, support vector machine

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Authors: Saba Ebrahimi, Saeed Ahmadian, Hedie Ashrafi

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Selecting appropriate patients for surgery is one of the main issues in thoracic surgery (TS). Both short-term and long-term risks and benefits of surgery must be considered in the patient selection criteria. There are some limitations in the existing datasets of TS patients because of missing values of attributes and imbalanced distribution of survival classes. In this study, a novel ensemble architecture of deep learning networks is proposed based on stacking different linear and non-linear layers to deal with imbalance datasets. The categorical and numerical features are split using different layers with ability to shrink the unnecessary features. Then, after extracting the insight from the raw features, a novel biased-kernel layer is applied to reinforce the gradient of the minority class and cause the network to be trained better comparing the current methods. Finally, the performance and advantages of our proposed model over the existing models are examined for predicting patient survival after thoracic surgery using a real-life clinical data for lung cancer patients.

Keywords: deep learning, ensemble models, imbalanced classification, lung cancer, TS patient selection

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Authors: Saba Rahman, Arvind K. Jain, S. D. Bharti, T. K. Datta

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Chimneys are generally tall and slender structures with circular cross-sections, due to which they are highly prone to wind forces. Wind exerts pressure on the wall of the chimneys, which produces unwanted forces. Vortex-induced oscillation is one of such excitations which can lead to the failure of the chimneys. Therefore, vortex-induced oscillation of chimneys is of great concern to researchers and practitioners since many failures of chimneys due to vortex shedding have occurred in the past. As a consequence, extensive research has taken place on the subject over decades. Many laboratory experiments have been performed to verify the theoretical models proposed to predict vortex-induced forces, including aero-elastic effects. Comparatively, very few proto-type measurement data have been recorded to verify the proposed theoretical models. Because of this reason, the theoretical models developed with the help of experimental laboratory data are utilized for analyzing the chimneys for vortex-induced forces. This calls for reliability analysis of the predictions of the responses of the chimneys produced due to vortex shedding phenomena. Although several works of literature exist on the vortex-induced oscillation of chimneys, including code provisions, the reliability analysis of chimneys against failure caused due to vortex shedding is scanty. In the present study, the reliability analysis of chimneys against vortex shedding failure is presented, assuming the uncertainty in vortex shedding phenomena to be significantly more than other uncertainties, and hence, the latter is ignored. The vortex shedding is modeled as a stationary random process and is represented by a power spectral density function (PSDF). It is assumed that the vortex shedding forces are perfectly correlated and act over the top one-third height of the chimney. The PSDF of the tip displacement of the chimney is obtained by performing a frequency domain spectral analysis using a matrix approach. For this purpose, both chimney and random wind forces are discretized over a number of points along with the height of the chimney. The method of analysis duly accounts for the aero-elastic effects. The double barrier threshold crossing level, as proposed by Vanmarcke, is used for determining the probability of crossing different threshold levels of the tip displacement of the chimney. Assuming the annual distribution of the mean wind velocity to be a Gumbel type-I distribution, the fragility curve denoting the variation of the annual probability of threshold crossing against different threshold levels of the tip displacement of the chimney is determined. The reliability estimate is derived from the fragility curve. A 210m tall concrete chimney with a base diameter of 35m, top diameter as 21m, and thickness as 0.3m has been taken as an illustrative example. The terrain condition is assumed to be that corresponding to the city center. The expression for the PSDF of the vortex shedding force is taken to be used by Vickery and Basu. The results of the study show that the threshold crossing reliability of the tip displacement of the chimney is significantly influenced by the assumed structural damping and the Gumbel distribution parameters. Further, the aero-elastic effect influences the reliability estimate to a great extent for small structural damping.

Keywords: chimney, fragility curve, reliability analysis, vortex-induced vibration

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Authors: Radomir Perzina, Jaroslav Ramik

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The Analytic Hierarchy Process is frequently used approach for solving decision making problems. There exists wide range of software programs utilizing that approach. Their main disadvantage is that they are relatively expensive and missing intermediate calculations. This work introduces a Microsoft Excel add-in called DAME – Decision Analysis Module for Excel. Comparing to other computer programs DAME is free, can work with scenarios or multiple decision makers and displays intermediate calculations. Users can structure their decision models into three levels – scenarios/users, criteria and variants. Items on all levels can be evaluated either by weights or pair-wise comparisons. There are provided three different methods for the evaluation of the weights of criteria, the variants as well as the scenarios – Saaty’s Method, Geometric Mean Method and Fuller’s Triangle Method. Multiplicative and additive syntheses are supported. The proposed software package is demonstrated on couple of illustrating examples of real life decision problems.

Keywords: analytic hierarchy process, multi-criteria decision making, pair-wise comparisons, Microsoft Excel, scenarios

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Authors: Marielcio Lacerda, Angelo Paulino, Elcio Shiguemori, Alvaro Damiao, Lamartine Guimaraes, Camila Anjos

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Accurate information about the terrain is extremely important in disaster management activities or conflict. This paper proposes the use of the Unmanned Aircraft Systems (UAS) at the identification of Airdrop Zones (AZs) and Helicopter Landing Zones (HLZs). In this paper we consider the AZs the zones where troops or supplies are dropped by parachute, and HLZs areas where victims can be rescued. The use of digital image processing enables the automatic generation of an orthorectified mosaic and an actual Digital Surface Model (DSM). This methodology allows obtaining this fundamental information to the terrain’s comprehension post-disaster in a short amount of time and with good accuracy. In order to get the identification and classification of AZs and HLZs images from DJI drone, model Phantom 4 have been used. The images were obtained with the knowledge and authorization of the responsible sectors and were duly registered in the control agencies. The flight was performed on May 24, 2017, and approximately 1,300 images were obtained during approximately 1 hour of flight. Afterward, new attributes were generated by Feature Extraction (FE) from the original images. The use of multispectral images and complementary attributes generated independently from them increases the accuracy of classification. The attributes of this work include the Declivity Map and Principal Component Analysis (PCA). For the classification four distinct classes were considered: HLZ 1 – small size (18m x 18m); HLZ 2 – medium size (23m x 23m); HLZ 3 – large size (28m x 28m); AZ (100m x 100m). The Decision Tree method Random Forest (RF) was used in this work. RF is a classification method that uses a large collection of de-correlated decision trees. Different random sets of samples are used as sampled objects. The results of classification from each tree and for each object is called a class vote. The resulting classification is decided by a majority of class votes. In this case, we used 200 trees for the execution of RF in the software WEKA 3.8. The classification result was visualized on QGIS Desktop 2.12.3. Through the methodology used, it was possible to classify in the study area: 6 areas as HLZ 1, 6 areas as HLZ 2, 4 areas as HLZ 3; and 2 areas as AZ. It should be noted that an area classified as AZ covers the classifications of the other classes, and may be used as AZ, HLZ of large size (HLZ3), medium size (HLZ2) and small size helicopters (HLZ1). Likewise, an area classified as HLZ for large rotary wing aircraft (HLZ3) covers the smaller area classifications, and so on. It was concluded that images obtained through small UAV are of great use in calamity situations since they can provide data with high accuracy, with low cost, low risk and ease and agility in obtaining aerial photographs. This allows the generation, in a short time, of information about the features of the terrain in order to serve as an important decision support tool.

Keywords: disaster management, unmanned aircraft systems, helicopter landing zones, airdrop zones, random forest

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Authors: Jiahe Liu, HongyangYu, Feng Qian, Miao Luo

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The aim of this study was to improve the effects of human body 3D reconstruction. The MvP algorithm was adopted to obtain key point information from multiple perspectives. This algorithm allowed the capture of human posture and joint positions from multiple angles, providing more comprehensive and accurate data. The study also incorporated the SMPL-X model, which has been widely used for human body modeling, to achieve more accurate 3D reconstruction results. The use of the MvP algorithm made it possible to observe the reconstructed object from multiple angles, thus reducing the problems of blind spots and missing information. This algorithm was able to effectively capture key point information, including the position and rotation angle of limbs, providing key data for subsequent 3D reconstruction. Compared with traditional single-view methods, the method of multi-view fusion significantly improved the accuracy and stability of reconstruction. By combining the MvP algorithm with the SMPL-X model, we successfully achieved better human body 3D reconstruction effects. The SMPL-X model is highly scalable and can generate highly realistic 3D human body models, thus providing more detail and shape information.

Keywords: 3D human reconstruction, multi-view, joint point, SMPL-X

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Authors: Fariba Azizi, Firoozeh Haghighi, Viliam Makis

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In this paper, we propose a method to model the relationship between failure time and degradation for a simple step stress test where underlying degradation path is linear and different causes of failure are possible. It is assumed that the intensity function depends only on the degradation value. No assumptions are made about the distribution of the failure times. A simple step-stress test is used to shorten failure time of products and a tampered failure rate (TFR) model is proposed to describe the effect of the changing stress on the intensities. We assume that some of the products that fail during the test have a cause of failure that is only known to belong to a certain subset of all possible failures. This case is known as masking. In the presence of masking, the maximum likelihood estimates (MLEs) of the model parameters are obtained through an expectation-maximization (EM) algorithm by treating the causes of failure as missing values. The effect of incomplete information on the estimation of parameters is studied through a Monte-Carlo simulation. Finally, a real example is analyzed to illustrate the application of the proposed methods.

Keywords: cause of failure, linear degradation path, reliability function, expectation-maximization algorithm, intensity, masked data

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Authors: Tsafi Timor

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The research focuses on the exploration of the unique journey that novice teachers experience in their first year of teaching, among graduates of re-training programs into teaching. The study explores the experiences of success and failure and the factors that underpin positive experiences, as well as the journey (process) of this year with reference to the comparison between novice teachers and new immigrants. The content analysis that was adopted in the study was conducted on texts that were written by the teachers and detailed their first year of teaching. The findings indicate that experiences of success are featured by personal satisfaction, constant need of feedback, high motivation in challenging situations, and emotions. Failure experiences are featured by frustration, helplessness, sense of humiliation, feeling of rejection, and lack of efficacy. Factors that promote and inhibit positive experiences relate to personal, personality, professional and organizational levels. Most teachers reported feeling like new immigrants, and demonstrated different models of the process of the first year of teaching. Further research is recommended on the factors that promote and inhibit positive experiences, and on 'The Missing Link' of the relationship between Teacher Education Programs and the practices in schools.

Keywords: first-year teaching, novice teachers, school practice, teacher education programs

Procedia PDF Downloads 277

Authors: Pooja Pandey

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The role of communication plays an integral part in the management of any disaster, whether natural or human-induced, both require effective and strategic delivery of information. The way any information is conveyed carries the most weight while dealing with the disaster. Hence, integrating communication strategies in disaster risk management (DRM) are extensively acknowledged however, these integration and planning are missing from the practical books. Researchers are continuously exploring integrated DRM and have established substantial vents between research and implementation of the strategies (gaps between science and policy). For this reason, this paper reviews the communication barriers that obstruct effective management of the disaster. Communication between first responders (government agencies, police, medical services) and the public (people directly affected by the disaster) is most critical and lacks proper delivery during a disaster. And these challenges can only be resolved if the foundation of the problem is properly dealt with, which is resolving the issues within the organizations. Through this study, it was found that it is necessary to build the communication gap between the organizations themselves as most of the hindrances occur during the mitigation, preparedness, response and recovery phase of the disaster. The study is concluded with the main aim to review the communication barriers within and at the organizational, technological, and social levels that impact effective DRM. In the end, some suggestions are made to strengthen the knowledge for future improvement in communication between the responders and their organizations.

Keywords: communication, organization, barriers, first responders, disaster risk management

Procedia PDF Downloads 275

Authors: Mark Bezner, Shani Deri, Tom Trigano, Kfir Ben-Harush

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Intermediate filament (IF) proteins-based fibers are among the toughest fibers in nature, as was shown by native hagfish slime threads and by synthetic fibers that are based on type V IF-proteins, the nuclear lamins. It is assumed that their mechanical performance stems from two major factors: (1) the transition from elastic -helices to stiff-sheets during tensile load; and (2) the specific organization of the coiled-coil proteins into a hierarchical network of nano-filaments. Here, we investigated the interrelationship between these two factors by using wet-spun fibers based on C. elegans (Ce) lamin. We found that Ce-lamin fibers, whether assembled in aqueous or alcoholic solutions, had the same nonlinear mechanical behavior, with the elastic region ending at ~5%. The pattern of the transition was, however, different: the ratio between -helices and -sheets/random coils was relatively constant until a 20% strain for fibers assembled in an aqueous solution, whereas for fibers assembled in 70% ethanol, the transition ended at a 6% strain. This structural phenomenon in alcoholic solution probably occurred through the transition between compacted and extended conformation of the random coil, and not between -helix and -sheets, as cycle analyses had suggested. The different transition pattern can also be explained by the different higher order organization of Ce-lamins in aqueous or alcoholic solutions, as demonstrated by introducing a point mutation in conserved residue in Ce-lamin gene that alter the structure of the Ce-lamins’ nano-fibrils. In addition, biomimicking the layered structure of silk and hair fibers by coating the Ce-lamin fiber with a hydrophobic layer enhanced fiber toughness and lead to a reversible transition between -helix and the extended conformation. This work suggests that different hierarchical structures, which are formed by specific assembly conditions, lead to diverse secondary structure transitions patterns, which in turn affect the fibers’ mechanical properties.

Keywords: protein-based fibers, intermediate filaments (IF) assembly, toughness, structure-property relationships

Procedia PDF Downloads 91

Authors: Shubham Bhonde, Nekzad Doctor, Shashwat Gawande

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For the use and the import of Keys and ID Transmitter as well as Body Control Modules with radio transmission in a lot of countries, homologation is required. Final deliverables in homologation of the product are certificates. In considering the world of homologation, there are approximately 200 certificates per product, with most of the certificates in local languages. It is challenging to manually investigate each certificate and extract relevant data from the certificate, such as expiry date, approval date, etc. It is most important to get accurate data from the certificate as inaccuracy may lead to missing re-homologation of certificates that will result in an incompliance situation. There is a scope of automation in reading the certificate data in the field of homologation. We are using deep learning as a tool for automation. We have first trained a model using machine learning by providing all country's basic data. We have trained this model only once. We trained the model by feeding pdf and jpg files using the ETL process. Eventually, that trained model will give more accurate results later. As an outcome, we will get the expiry date and approval date of the certificate with a single click. This will eventually help to implement automation features on a broader level in the database where certificates are stored. This automation will help to minimize human error to almost negligible.

Keywords: homologation, re-homologation, data science, deep learning, machine learning, ETL (extract transform loading)

Procedia PDF Downloads 144

Authors: Mobina Bagherianlemraski, Parastoo Namdar

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Background: The ongoing coronavirus disease has affected most countries. This virus has high transmission power. Due to the closure of most dental clinics, millions of orthodontic patients missed their appointments during the COVID-19 pandemic. Methods: A questionnaire was developed and sent to patients receiving orthodontic treatment at a public or private clinic. Results: A total of 200 responses were analyzed: These included 153 women (76.5%) and 47 men (23.5%). The mean and standard deviation of their age was 18.92±7.23 years, with an age range of 8 to 40 years. One hundred eighty-nine patients (94.5%) had fixed appliances, and 11 patients (5.5%) had removable appliances. Of all participants, 35% (70) missed their appointments. The highest and lowest reasons for stopping appointments were concerned about the spread of COVID-19 with 28 cases (40%) and the closure of the clinic with 15 cases (21.4%). Of the 53 patients who contacted their orthodontists, 86.8 % (46) communicated via office phone and 5.7% (3) through social media. Conclusion: This study determined that the coronavirus pandemic and quarantine have had an important impact on orthodontic treatments. The greatest concern of orthodontic patients was increasing in treatment duration. Patients who used fixed appliances reported missing dental appointments more than others. Therefore, during COVID 19 Pandemic, orthodontists should prepare patients to solve their problems linked to orthodontic appliances when possible.

Keywords: orthodontic patients, coronavirus pandemic, appointments, COVID-19

Procedia PDF Downloads 119

Authors: Henrik Prinzhorn, Peter Nyhuis, Johannes Wagner, Peter Burggräf, Torben Schmitz, Christina Reuter

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Assembling large-scale products, such as airplanes, locomotives, or wind turbines, involves frequent process interruptions induced by e.g. delayed material deliveries or missing availability of resources. This leads to a negative impact on the logistical performance of a producer of xxl-products. In industrial practice, in case of interruptions, the identification, evaluation and eventually the selection of an alternative order of assembly activities (‘assembly alternative’) leads to an enormous challenge, especially if an optimized logistical decision should be reached. Therefore, in this paper, an innovative, optimization model for the identification of assembly alternatives that addresses the given problem is presented. It describes make-to-order, large-scale product assembly processes as a resource constrained project scheduling (RCPS) problem which follows given restrictions in practice. For the evaluation of the assembly alternative, a cost-based definition of the logistical objectives (delivery reliability, inventory, make-span and workload) is presented.

Keywords: assembly scheduling, large-scale products, make-to-order, optimization, rescheduling

Procedia PDF Downloads 440

Authors: Vanessa R. Ralph, Leah J. Scharlott, Megan Y. Deshaye, Ryan L. Stowe

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Given the traditions of chemistry teaching, one may not question whether chemical phenomena play a prominent role. Yet, the role of chemical phenomena in an introductory chemistry course may define the extent to which the course is introductory, chemistry, and equitable. Picture, for example, the classic Ideal Gas Law problem. If one envisions a prompt wherein students are tasked with calculating a missing variable, then one envisions a prompt that relies on chemical phenomena as a context rather than as a model to understand the natural world. Consider a prompt wherein students are tasked with applying molecular models of gases to explain why the vapor pressure of a gaseous solution of water differs from that of carbon dioxide. Here, the chemical phenomenon is not only the context but also the subject of the prompt. Deliveries of general and organic chemistry were identified as ranging wildly in the integration of chemical phenomena. The more incorporated the phenomena, the more equitable the assessment task was for students of varying access to pre-college math and science preparation. How chemical phenomena are integrated may very well define whether courses are chemistry, are introductory, and are equitable. Educators of chemistry are invited colleagues to discuss the role of chemical phenomena in their courses and consider the long-lasting impacts of replicating tradition for tradition’s sake.

Keywords: equitable educational practices, chemistry curriculum, content organization, assessment design

Procedia PDF Downloads 175

Authors: Abdul-Razak Inusah

Abstract:

The study is a cross-sectional investigation of issues in Dagbanli Phonology, a Mabia language spoken in the Northern Region of Ghana. The issues investigated and assessed for the purpose of Dagbanli phonology are the status of the velar fricatives [x, ɣ] and the flap [ɾ] across Dagbanli dialects. The ethnographic approach is employed to solicit the primary data from bucolic Dagbanli speech communities. The descriptive method is engaged for the analysis of the primary data available. The investigation reveals that the dialects have the velar fricatives [x, ɣ] confined to specific segmental contexts with a particular inventory stricture. The flap[ɾ] is noticed to occur mostly in intervocalic but entirely missing in Dagbanli indigenous words in word-initial. The velar fricatives [x, ɣ] and the flap[ɾ] are observed to be non-contrastive and only suffice as dialectal allophones in the language. The paper shows evidence of coalesce of non-coronal labial /m/ and coronal fricative /s/ to produce dorsal fricative [x] in intervocalic and coalesce of stem final stop /ɡ/ and suffix onset fricative /s/ to yield the dorsal fricative [x], a finding which shows the status of the segment [x] in Dagbanli phonology. The paper concludes that the segments [x], [ɣ] and [ɾ] are positional variants of /ɡ+s/ or /m+s/, /ɡ/ and /d/.

Keywords: Dagbani, phonology, dialect, segment, fricatives, coalesce

Procedia PDF Downloads 31

Authors: Linda Smail, Zineb Azouz

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Given a Bayesian network relative to a set I of discrete random variables, we are interested in computing the probability distribution P(S) where S is a subset of I. The general idea is to write the expression of P(S) in the form of a product of factors where each factor is easy to compute. More importantly, it will be very useful to give an interpretation of each of the factors in terms of conditional probabilities. This paper considers a semantic interpretation of the factors involved in computing marginal probabilities in Bayesian networks. Establishing such a semantic interpretations is indeed interesting and relevant in the case of large Bayesian networks.

Keywords: Bayesian networks, D-Separation, level two Bayesian networks, factorization of computation

Procedia PDF Downloads 504

Authors: M. S. Gadala, Khaled Ahmed, Elasadig Mahdi

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In this paper, we introduced a gradient-based inverse solver to obtain the missing boundary conditions based on the readings of internal thermocouples. The results show that the method is very sensitive to measurement errors, and becomes unstable when small time steps are used. The artificial neural networks are shown to be capable of capturing the whole thermal history on the run-out table, but are not very effective in restoring the detailed behavior of the boundary conditions. Also, they behave poorly in nonlinear cases and where the boundary condition profile is different. GA and PSO are more effective in finding a detailed representation of the time-varying boundary conditions, as well as in nonlinear cases. However, their convergence takes longer. A variation of the basic PSO, called CRPSO, showed the best performance among the three versions. Also, PSO proved to be effective in handling noisy data, especially when its performance parameters were tuned. An increase in the self-confidence parameter was also found to be effective, as it increased the global search capabilities of the algorithm. RPSO was the most effective variation in dealing with noise, closely followed by CRPSO. The latter variation is recommended for inverse heat conduction problems, as it combines the efficiency and effectiveness required by these problems.

Keywords: inverse analysis, function specification, neural net works, particle swarm, run-out table

Procedia PDF Downloads 218