Search results for: accidents predictions

Authors: Eugen Rusu

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The objective of the work proposed is to perform an analysis of the wave conditions in the Black Sea basin. This is especially focused on the spatial and temporal occurrences and on the dynamics of the most extreme storms in the context of the climate changes. A numerical modelling system, based on the spectral phase averaged wave model SWAN, has been implemented and validated against both in situ measurements and remotely sensed data, all along the sea. Moreover, a successive correction method for the assimilation of the satellite data has been associated with the wave modelling system. This is based on the optimal interpolation of the satellite data. Previous studies show that the process of data assimilation improves considerably the reliability of the results provided by the modelling system. This especially concerns the most sensitive cases from the point of view of the accuracy of the wave predictions, as the extreme storm situations are. Following this numerical approach, it has to be highlighted that the results provided by the wave modelling system above described are in general in line with those provided by some similar wave prediction systems implemented in enclosed or semi-enclosed sea basins. Simulations of this wave modelling system with data assimilation have been performed for the 30-year period 1987-2016. Considering this database, the next step was to analyze the intensity and the dynamics of the higher storms encountered in this period. According to the data resulted from the model simulations, the western side of the sea is considerably more energetic than the rest of the basin. In this western region, regular strong storms provide usually significant wave heights greater than 8m. This may lead to maximum wave heights even greater than 15m. Such regular strong storms may occur several times in one year, usually in the wintertime, or in late autumn, and it can be noticed that their frequency becomes higher in the last decade. As regards the case of the most extreme storms, significant wave heights greater than 10m and maximum wave heights close to 20m (and even greater) may occur. Such extreme storms, which in the past were noticed only once in four or five years, are more recent to be faced almost every year in the Black Sea, and this seems to be a consequence of the climate changes. The analysis performed included also the dynamics of the monthly and annual significant wave height maxima as well as the identification of the most probable spatial and temporal occurrences of the extreme storm events. Finally, it can be concluded that the present work provides valuable information related to the characteristics of the storm conditions and on their dynamics in the Black Sea. This environment is currently subjected to high navigation traffic and intense offshore and nearshore activities and the strong storms that systematically occur may produce accidents with very serious consequences.

Keywords: Black Sea, extreme storms, SWAN simulations, waves

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Authors: Mohamed Elghorab, Vendra C. Madhav Rao, Jennifer X. Wen

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Turbulence modelling is still evolving, and efforts are on to improve and develop numerical methods to simulate the real turbulence structures by using the empirical and experimental information. The monotonically integrated large eddy simulation (MILES) is an attractive approach for modelling turbulence in high Re flows, which is based on the solving of the unfiltered flow equations with no explicit sub-grid scale (SGS) model. In the current work, this approach has been used, and the action of the SGS model has been included implicitly by intrinsic nonlinear high-frequency filters built into the convection discretization schemes. The MILES solver is developed using the opensource CFD OpenFOAM libraries. The role of flux limiters schemes namely, Gamma, superBee, van-Albada and van-Leer, is studied in predicting turbulent statistical quantities for a fully developed channel flow with a friction Reynolds number, Re_T = 180, and compared the numerical predictions with the well-established Direct Numerical Simulation (DNS) results for studying the wall generated turbulence. It is inferred from the numerical predictions that Gamma, van-Leer and van-Albada limiters produced more diffusion and overpredicted the velocity profiles, while superBee scheme reproduced velocity profiles and turbulence statistical quantities in good agreement with the reference DNS data in the streamwise direction although it deviated slightly in the spanwise and normal to the wall directions. The simulation results are further discussed in terms of the turbulence intensities and Reynolds stresses averaged in time and space to draw conclusion on the flux limiter schemes performance in OpenFOAM context.

Keywords: flux limiters, implicit SGS, MILES, OpenFOAM, turbulence statistics

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Authors: Zouari Ezzeddine, Ghraieb Ikram

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From the results of the academic literature which evokes the limitations of previous studies, this article shows the reasons for multidimensionality Prediction of financial bubbles. A new framework for modeling study predicting financial bubbles by linking a set of variable presented on several dimensions dictating its multidimensional character. It takes into account the preferences of financial actors. A multicriteria anticipation of the appearance of bubbles in international financial markets helps to fight against a possible crisis.

Keywords: classical measures, predictions, financial bubbles, multidimensional, artificial neural networks

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Authors: Somyoung Shin, Donghun Jeong, Yeoil Yun

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South Korea continuously installed tunnels in consideration of the safety and operation efficiency, and the number of installed tunnels has doubled over the past ten years. The tunnel section is designed based on the guidelines, but the tunnel entrance becomes narrow due to dark adaptation and pressure. In fact, around 13% of traffic in expressways of Japan happens at the entrance, leading to congestion and rear-ends collision accidents. Therefore, this study aims to analyze the stability from the expansion of the shoulder width in the tunnel entrance by applying a virtual reality driving simulator in order to reduce the accidents that happen in the tunnel entrance. To compare the driving stability based on the changes in the width of the right shoulder under the same condition, a virtual reality driving simulator is used to conduct an experiment on 30 subjects in their 20s to 60s and to provide a more practical virtual reality driving environment, and an experiment map is designed based on actual roads as the background to conduct the experiment. The right shoulder is classified into 2.5m and 3.0m based on the design guidelines of the expressways and the road structure installation regulations. The experimenters' experiment order is decided randomly. As a result of analyzing the average speed, it was displayed as 100.73km/h when the shoulder width was 2.5m and 101.69km/h when the shoulder width was 3.0m and as a result of conducting t-test analysis, the p-value appeared as more than 0.05 in the significance level of 95%, so it was statistically insignificant. Also, as a result of analyzing the speed deviation between the average driving speed of the analyzed interval and the average driving speed upon entering the tunnel, it was displayed as 3.06km/h when the shoulder width was 2.5m and 1.87km/h when the shoulder width was 3.0m and as a result of conducting t-test analysis, the p-value appeared as less than 0.05 in the significance level of 95%, so it was statistically significant. This means that when the shoulder width is 3.0m, there is stability in terms of the driving stability compared to when it is 2.5m. Therefore, it is considered that when new roads are constructed in Korea, the right shoulder width should be installed as 3.0m to enhance the driving stability.

Keywords: driving stability, shoulder width, tunnel, virtual reality driving simulator

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Authors: Venkat S. Somayajula

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Ground surface temperature history prediction model plays a vital role in determining standards for international nuclear waste management. International standards for borehole based nuclear waste disposal require paleoclimate cycle predictions on scale of a million forward years for the place of waste disposal. This research focuses on developing a paleoclimate cycle prediction model using Bayesian long-short term memory (LSTM) neural architecture operated on accumulated borehole temperature history data. Bayesian models have been previously used for paleoclimate cycle prediction based on Monte-Carlo weight method, but due to limitations pertaining model coupling with certain other prediction networks, Bayesian models in past couldn’t accommodate prediction cycle’s over 1000 years. LSTM has provided frontier to couple developed models with other prediction networks with ease. Paleoclimate cycle developed using this process will be trained on existing borehole data and then will be coupled to surface temperature history prediction networks which give endpoints for backpropagation of LSTM network and optimize the cycle of prediction for larger prediction time scales. Trained LSTM will be tested on past data for validation and then propagated for forward prediction of temperatures at borehole locations. This research will be beneficial for study pertaining to nuclear waste management, anthropological cycle predictions and geophysical features

Keywords: Bayesian long-short term memory neural network, borehole temperature, ground surface temperature history, paleoclimate cycle

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Authors: Animish Jain

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This study deals with using Multiplicative Weight Update within artificial intelligence and machine learning to create models that can diagnose skin cancer using microscopic images of cancer samples. In this study, the multiplicative weight update method is used to take the predictions of multiple models to try and acquire more accurate results. Logistic Regression, Convolutional Neural Network (CNN), and Support Vector Machine Classifier (SVMC) models are employed within the Multiplicative Weight Update system. These models are trained on pictures of skin cancer from the ISIC-Archive, to look for patterns to label unseen scans as either benign or malignant. These models are utilized in a multiplicative weight update algorithm which takes into account the precision and accuracy of each model through each successive guess to apply weights to their guess. These guesses and weights are then analyzed together to try and obtain the correct predictions. The research hypothesis for this study stated that there would be a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The SVMC model had an accuracy of 77.88%. The CNN model had an accuracy of 85.30%. The Logistic Regression model had an accuracy of 79.09%. Using Multiplicative Weight Update, the algorithm received an accuracy of 72.27%. The final conclusion that was drawn was that there was a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The conclusion was made that using a CNN model would be the best option for this problem rather than a Multiplicative Weight Update system. This is due to the possibility that Multiplicative Weight Update is not effective in a binary setting where there are only two possible classifications. In a categorical setting with multiple classes and groupings, a Multiplicative Weight Update system might become more proficient as it takes into account the strengths of multiple different models to classify images into multiple categories rather than only two categories, as shown in this study. This experimentation and computer science project can help to create better algorithms and models for the future of artificial intelligence in the medical imaging field.

Keywords: artificial intelligence, machine learning, multiplicative weight update, skin cancer

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Authors: Hiroki Kikuchi, Atsushi Fukuda, Hirokazu Akahane, Satoru Kobayakawa, Tuenjai Fukuda, Takeru Miyokawa

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According to WHO reports, fatalities by road traffic accidents in many countries of South-East Asia region especially Thailand and Malaysia are increasing year by year. In order to overcome these serious problems, both governments are focusing on road safety measures. In response, the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) of Japan and Japan International Cooperation Agency (JICA) have begun active support based on the experiences to reduce the number of fatalities in road accidents in Japan in the past. However, even if the successful road safety measures in Japan is adopted in South-East Asian countries, it is not sure whether it will work well or not. So, it is necessary to clarify the issues and systematize the process for the implementation of road safety measures in South-East Asia. On the basis of the above, this study examined the applicability of "information sharing traffic safety measure" which is one of the successful road safety measures in Japan to the social implementation of road safety measures in South-East Asian countries. The "Information sharing traffic safety measure" is carried out traffic safety measures by stakeholders such as residents, administration, and experts jointly. In this study, we extracted the issues of implementation of road safety measures under local context firstly. This is clarifying the particular issues with its implementation in South-East Asian cities. Secondly, we considered how to implement road safety measures for solving particular issues based on the method of "information sharing traffic safety measure". In the implementation method, the location of the occurrence of a dangerous event was extracted based on the “HIYARI-HATTO” data which were obtained from the residents. This is because it is considered that the implementation of the information sharing traffic safety measure focusing on the location where the dangerous event occurs leads to the reduction of traffic accidents. Also, the target locations for the implementation of measures differ for each city. In Penang, we targeted the intersections　in the downtown, while in Suphan Buri, we targeted mainly traffic control on the intercity highway. Finally, we proposed a method for implementing traffic safety measures. For Penang, we proposed a measure to improve the signal phase and showed the effect of the measure on the micro traffic simulation. For Suphan Buri, we proposed the suitable measures for the danger points extracted by collecting the “HIYARI-HATTO” data of residents to the administration. In conclusion, in order to successfully implement the road safety measure based on the "information sharing traffic safety measure", the process for social implementation of the road safety measures should be consistent and carried out repeatedly. In particular, by clarifying specific issues based on local context in South-East Asian countries, the stakeholders, not only such as government sectors but also local citizens can share information regarding road safety and select appropriate countermeasures. Finally, we could propose this approach to the administration that had the authority.

Keywords: information sharing road safety measure, social implementation, South-East Asia, HIYARI-HATTO

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Authors: A. Abdul-Ameer

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The equations governing the distance, pressure- volume flow relationships for the pipeline transportation of gaseous mixtures, are considered. A derivation based on differential calculus, for an element of this system model, is addressed. Solutions, yielding the input- output response following pressure changes, are reviewed. The technical problems associated with these analytical results are identified. Procedures resolving these difficulties providing thereby an attractive, simple, analysis route are outlined. Computed responses, validating thereby calculated predictions, are presented.

Keywords: pressure, distance, flow, dissipation, models

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Authors: G. Ravindranath, S. Savitha

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This paper presents heat transfer analysis of single horizontal bare tube and heat transfer analysis of staggered arrangement of bare tube bundles bare tube bundles in gas-solid (air-solid) fluidized bed and predictions are done by using Artificial Neural Network (ANN) based on experimental data. Fluidized bed provide nearly isothermal environment with high heat transfer rate to submerged objects i.e. due to through mixing and large contact area between the gas and the particle, a fully fluidized bed has little temperature variation and gas leaves at a temperature which is close to that of the bed. Measurement of average heat transfer coefficient was made by local thermal simulation technique in a cold bubbling air-fluidized bed of size 0.305 m. x 0.305 m. Studies were conducted for single horizontal Bare Tube of length 305mm and 28.6mm outer diameter and for bare tube bundles of staggered arrangement using beds of large (average particle diameter greater than 1 mm) particle (raagi and mustard). Within the range of experimental conditions influence of bed particle diameter ( Dp), Fluidizing Velocity (U) were studied, which are significant parameters affecting heat transfer. Artificial Neural Networks (ANNs) have been receiving an increasing attention for simulating engineering systems due to some interesting characteristics such as learning capability, fault tolerance, and non-linearity. Here, feed-forward architecture and trained by back-propagation technique is adopted to predict heat transfer analysis found from experimental results. The ANN is designed to suit the present system which has 3 inputs and 2 out puts. The network predictions are found to be in very good agreement with the experimental observed values of bare heat transfer coefficient (hb) and nusselt number of bare tube (Nub).

Keywords: fluidized bed, large particles, particle diameter, ANN

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Authors: N. Lakshmipathy, Ch. S. N. Murthy, M. Aruna

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The design of lighting systems for surface mines is not an easy task because of the unique environment and work procedures encountered in the mines. The primary objective of this paper is to identify the major problems encountered in mine lighting application and to provide guidance in the solution of these problems. In the surface mining reflectance of surrounding surfaces is one of the important factors, which improve the vision, in the night hours. But due to typical working nature in the mines it is very difficult to fulfill these requirements, and also the orientation of the light at work site is a challenging task. Due to this reason machine operator and other workers in a mine need to be able to orient themselves in a difficult visual environment. The haul roads always keep on changing to tune with the mining activity. Other critical area such as dumpyards, stackyards etc. also change their phase with time, and it is difficult to illuminate such areas. Mining is a hazardous occupation, with workers exposed to adverse conditions; apart from the need for hard physical labor, there is exposure to stress and environmental pollutants like dust, noise, heat, vibration, poor illumination, radiation, etc. Visibility is restricted when operating load haul dumper and Heavy Earth Moving Machinery (HEMM) vehicles resulting in a number of serious accidents. one of the leading causes of these accidents is the inability of the equipment operator to see clearly people, objects or hazards around the machine. Results indicate blind spots are caused primarily by posts, the back of the operator's cab, and by lights and light brackets. The careful designed and implemented, lighting systems provide mine workers improved visibility and contribute to improved safety, productivity and morale. Properly designed lighting systems can improve visibility and safety during working in the opencast mines.

Keywords: contrast, efficacy, illuminance, illumination, light, luminaire, luminance, reflectance, visibility

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Authors: Aurélia Jandot

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With the oil and economic crises which began in the 1970’s, it has progressively appeared necessary to convince the French “general public“ that a use of new energy sources was essential. In this field, nuclear energy represented the future and concentrated lots of hopes. However, the discourse about nuclear energy has progressively seen negative arguments growing in the French media. The gradual changes in the perception of nuclear energy will be studied here through the arguments given in the main French weekly newsmagazines, which had a great impact on the readers, thus on the “general public“, from the 1970’s to the end of the 1980’s. Indeed, to understand better these changes will be taken into account the major international events, the reorientations of the French domestic policy, and the evolutions of the nuclear technology. As this represents a considerable amount of copies and thus of information, will be selected here the main articles which emphasize the “mental images“ aiming to direct the thought of the readers, and which have led the public awareness and acceptance to evolve. From the 1970’s to the end of the 1980’s, two dichotomous trends are in confrontation : one is promoting the perception of the nuclear energy, the other is discrediting it. Moreover, these two trends are organized in two axes. The first axis is about the engineerings evolutions, such as the main French media represent them, with its approximations, its exaggerations, its fictions sometimes. Is added the will to make accessible to the “general public“ some concepts which are quite difficult to understand for the largest number. The second axis rests on the way the major accidents of the period are approached, including those of Three Mile Island and Chernobyl. Thanks to these accidents and because of the international relations evolutions, the ecologist movements and their impacts have progressively grown, with evident consequences on the public perception of nuclear energy and on the way the successive governments can implement new power plants in France. Then, in both cases, over the period considered, the language has changed, as the perceptible objectives of the communication, allowing to discern the deepest intentions of the newsmagazines editing. This is all these changes that will be emphasized, over a period where the nuclear energy technology, to there a field for specialists, bearing mystery and secret, has become a social issue seemingly open to all.

Keywords: social issues, public acceptance, mediatization, discourse changes

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Authors: Go Iwashita

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The paper describes how the frequencies of heatstroke occurring in indoor built environment are related to the outdoor thermal environment with big statistical data. As the statistical accident data of heatstroke, the nationwide accident data were obtained from the National Agency for the Advancement of Sports and Health (NAASH) . The meteorological database of the Japanese Meteorological Agency supplied data about 1-hour average temperature, humidity, wind speed, solar radiation, and so forth. Each heatstroke data point from the NAASH database was linked to the meteorological data point acquired from the nearest meteorological station where the accident of heatstroke occurred. This analysis was performed for a 10-year period (2005–2014). During the 10-year period, 3,819 cases of heatstroke were reported in the NAASH database for the investigated secondary/high schools of the nine Japanese representative cities. Heatstroke most commonly occurred in the outdoor schoolyard at a wet-bulb globe temperature (WBGT) of 31°C and in the indoor gymnasium during athletic club activities at a WBGT > 31°C. The determined accident ratio (number of accidents during each club activity divided by the club’s population) in the gymnasium during the female badminton club activities was the highest. Although badminton is played in a gymnasium, these WBGT results show that the risk level during badminton under hot and humid conditions is equal to that of baseball or rugby played in the schoolyard. Except sports, the high risk of heatstroke was observed in schools houses during cultural activities. The risk level for indoor environment under hot and humid condition would be equal to that for outdoor environment based on the above results of WBGT. Therefore control measures against hot and humid indoor condition were needed as installing air conditions not only schools but also residences.

Keywords: accidents in schools, club activity, gymnasium, heatstroke

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Authors: Chitra Rajagopal, Indra Deo Kumar, Ila Chauhan, Ruchi Joshi, Binoy Bhargavan

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Reliability and safety are essential for the success of mission-critical and safety-critical defense systems. Humans are part of the entire life cycle of defense systems development and deployment. The majority of industrial accidents or disasters are attributed to human errors. Therefore, considerations of human performance and human reliability are critical in all complex systems, including defense systems. Defense systems are operating from the ground, naval and aerial platforms in diverse conditions impose unique physical and psychological challenges to the human operators. Some of the safety and mission-critical defense systems with human-machine interactions are fighter planes, submarines, warships, combat vehicles, aerial and naval platforms based missiles, etc. Human roles and responsibilities are also going through a transition due to the infusion of artificial intelligence and cyber technologies. Human operators, not accustomed to such challenges, are more likely to commit errors, which may lead to accidents or loss events. In such a scenario, it is imperative to understand the human factors in defense systems for better systems performance, safety, and cost-effectiveness. A case study using Task Analysis (TA) based methodology for assessment and reduction of human errors in the Air and Missile Defense System in the context of emerging technologies were presented. Action-oriented task analysis techniques such as Hierarchical Task Analysis (HTA) and Operator Action Event Tree (OAET) along with Critical Action and Decision Event Tree (CADET) for cognitive task analysis was used. Human factors assessment based on the task analysis helps in realizing safe and reliable defense systems. These techniques helped in the identification of human errors during different phases of Air and Missile Defence operations, leading to meet the requirement of a safe, reliable and cost-effective mission.

Keywords: defence systems, reliability, risk, safety

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Authors: Bikis Muhammed, Sehra Sedigh Sarvestani, Ali R. Hurson, Lasanthi Gamage

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Vehicular traffic events have overly complex spatial correlations and temporal interdependencies and are also influenced by environmental events such as weather conditions. To capture these spatial and temporal interdependencies and make more realistic vehicular traffic predictions, graph neural networks (GNN) based traffic prediction models have been extensively utilized due to their capability of capturing non-Euclidean spatial correlation very effectively. However, most of the already existing GNN-based traffic prediction models have some limitations during learning complex and dynamic spatial and temporal patterns due to the following missing factors. First, most GNN-based traffic prediction models have used static distance or sometimes haversine distance mechanisms between spatially separated traffic observations to estimate spatial correlation. Secondly, most GNN-based traffic prediction models have not incorporated environmental events that have a major impact on the normal traffic states. Finally, most of the GNN-based models did not use an attention mechanism to focus on only important traffic observations. The objective of this paper is to study and make real-time vehicular traffic predictions while incorporating the effect of weather conditions. To fill the previously mentioned gaps, our prediction model uses a real-time driving distance between sensors to build a distance matrix or spatial adjacency matrix and capture spatial correlation. In addition, our prediction model considers the effect of six types of weather conditions and has an attention mechanism in both spatial and temporal data aggregation. Our prediction model efficiently captures the spatial and temporal correlation between traffic events, and it relies on the graph attention network (GAT) and Bidirectional bidirectional long short-term memory (Bi-LSTM) plus attention layers and is called GAT-BILSTMA.

Keywords: deep learning, real time prediction, GAT, Bi-LSTM, attention

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Authors: Ginger Egberts, Marianne Schaaphok, Fred Vermolen, Paul van Zuijlen

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A few years ago, a promising morphoelastic model was developed for the simulation of contraction formation after burn injuries. Contraction can lead to a serious reduction in physical mobility, like a reduction in the range-of-motion of joints. If this is the case in a healing burn wound, then this is referred to as a contracture that needs medical intervention. The morphoelastic model consists of a set of partial differential equations describing both a chemical part and a mechanical part in dermal wound healing. These equations are solved with the numerical finite element method (FEM). In this method, many calculations are required on each of the chosen elements. In general, the more elements, the more accurate the solution. However, the number of elements increases rapidly if simulations are performed in 2D and 3D. In that case, it not only takes longer before a prediction is available, the computation also becomes more expensive. It is therefore important to investigate alternative possibilities to generate the same results, based on the input parameters only. In this study, a surrogate neural network has been designed to mimic the results of the one-dimensional morphoelastic model. The neural network generates predictions quickly, is easy to implement, and there is freedom in the choice of input and output. Because a neural network requires extensive training and a data set, it is ideal that the one-dimensional FEM code generates output quickly. These feed-forward-type neural network results are very promising. Not only can the network give faster predictions, but it also has a performance of over 99%. It reports on the relative surface area of the wound/scar, the total strain energy density, and the evolutions of the densities of the chemicals and mechanics. It is, therefore, interesting to investigate the applicability of a neural network for the two- and three-dimensional morphoelastic model for contraction after burn injuries.

Keywords: biomechanics, burns, feasibility, feed-forward NN, morphoelasticity, neural network, relative surface area wound

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Authors: Jhe-Hao Huang, Kun-Da Wu, Wei-Cheng Shih, Jui-Pin Hung

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This study presented the investigation on the dynamic characteristics of a spindle tool system by experimental and finite element modeling approaches. As well known facts, the machining stability is greatly determined by the dynamic characteristics of the spindle tool system. Therefore, understanding the factors affecting dynamic behavior of a spindle tooling system is a prerequisite in dominating the final machining performance of machine tool system. To this purpose, a physical spindle unit was employed to assess the dynamic characteristics by vibration tests. Then, a three-dimensional finite element model of a high-speed spindle system integrated with tool holder was created to simulate the dynamic behaviors. For modeling the angular contact bearings, a series of spring elements were introduced between the inner and outer rings. The spring constant can be represented by the contact stiffness of the rolling bearing based on Hertz theory. The interface characteristic between spindle nose and tool holder taper can be quantified from the comparison of the measurements and predictions. According to the results obtained from experiments and finite element predictions, the vibration behavior of the spindle is dominated by the bending deformation of the spindle shaft in different modes, which is further determined by the stiffness of the bearings in spindle housing. Also, the spindle unit with tool holder shows a different dynamic behavior from that of spindle without tool holder. This indicates the interface property between tool holder and spindle nose plays an dominance on the dynamic characteristics the spindle tool system. Overall, the dynamic behaviors the spindle with and without tool holder can be successfully investigated through the finite element model proposed in this study. The prediction accuracy is determined by the modeling of the rolling interface of ball bearings in spindles and the interface characteristics between tool holder and spindle nose. Besides, identifications of the interface characteristics of a ball bearing and spindle tool holder are important for the refinement of the spindle tooling system to achieve the optimum machining performance.

Keywords: contact stiffness, dynamic characteristics, spindle, tool holder interface

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Authors: Hela Ghali, Oumayma Ben Amor, Salwa Khefacha, Mohamed Ben Rejeb, Sirine Frigui, Meriam Tourki Dhidah, Lamine Dhidah, Houyem Said Laatiri

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Background: Blood exposure accidents are the most common problem in hospitals that threaten healthcare professionals with a high risk of infectious complications which weighs heavily on health systems worldwide. Paramedics are the highest risk group due to the nature of their daily activities. We aimed to determine knowledge and attitudes about the management of blood-exposure accidents among nurses and technicians in two pediatric departments. Materials/Methods: This is a cross-sectional descriptive study conducted on March 2017, carried out with the care staff of the pediatric ward of the Farhat Hached Teaching Hospital of Sousse and pediatric surgery of the Fattouma Bourguiba University Hospital in Monastir, using a pre- tested and self-administered questionnaire. Data entry and analysis were performed using Excel software. Results: The response rate was 85.1%. A female predominance (82.5%) was reported among respondents with a sex ratio of 0.21. 80% of the participants were under 35 years old. Seniority of less than 10 years was found in 77.5% of respondents. Only 22.5% knew the definition of a blood- exposure accident. 100% and 95% of participants reported the relative risk, respectively, to hepatitis and AIDS viruses. However, only 15% recognized the severity factors of a blood-exposure accident. Hygiene compliance was the most important dimension for almost the entire population for the prevention. On the other hand, only 12.5% knew the meaning of 'standard precautions' and ¼ considered them necessary for at-risk patients only. 40% reported being exposed at least once, among them, 87.5% used betadine, and 77.5% said that anti-infectious chemoprophylaxis is necessary regardless of the patient's serological status. However, 52.5% did not know the official reporting circuit of management of blood-exposure accident in their institutions. Conclusion: For better management of risks in hospitals and an improvement of the safety of the care, a reinforcement of the sensibilization of the caregivers with regard to the risks of blood exposure accident is necessary, while developing their knowledge to act in security.

Keywords: attitudes, blood-exposure accident, knowledge, pediatric department

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Authors: Stephen L. Green, Alexander N. Gorban, Ivan Y. Tyukin

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Within the past decade, using Convolutional Neural Networks (CNN)’s to create Deep Learning systems capable of translating Sign Language into text has been a breakthrough in breaking the communication barrier for deaf-mute people. Conventional research on this subject has been concerned with training the network to recognize the fingerspelling gestures of a given language and produce their corresponding alphanumerics. One of the problems with the current developing technology is that images are scarce, with little variations in the gestures being presented to the recognition program, often skewed towards single skin tones and hand sizes that makes a percentage of the population’s fingerspelling harder to detect. Along with this, current gesture detection programs are only trained on one finger spelling language despite there being one hundred and forty-two known variants so far. All of this presents a limitation for traditional exploitation for the state of current technologies such as CNN’s, due to their large number of required parameters. This work aims to present a technology that aims to resolve this issue by combining a pretrained legacy AI system for a generic object recognition task with a corrector method to uptrain the legacy network. This is a computationally efficient procedure that does not require large volumes of data even when covering a broad range of sign languages such as American Sign Language, British Sign Language and Chinese Sign Language (Pinyin). Implementing recent results on method concentration, namely the stochastic separation theorem, an AI system is supposed as an operate mapping an input present in the set of images u ∈ U to an output that exists in a set of predicted class labels q ∈ Q of the alphanumeric that q represents and the language it comes from. These inputs and outputs, along with the interval variables z ∈ Z represent the system’s current state which implies a mapping that assigns an element x ∈ ℝⁿ to the triple (u, z, q). As all xi are i.i.d vectors drawn from a product mean distribution, over a period of time the AI generates a large set of measurements xi called S that are grouped into two categories: the correct predictions M and the incorrect predictions Y. Once the network has made its predictions, a corrector can then be applied through centering S and Y by subtracting their means. The data is then regularized by applying the Kaiser rule to the resulting eigenmatrix and then whitened before being split into pairwise, positively correlated clusters. Each of these clusters produces a unique hyperplane and if any element x falls outside the region bounded by these lines then it is reported as an error. As a result of this methodology, a self-correcting recognition process is created that can identify fingerspelling from a variety of sign language and successfully identify the corresponding alphanumeric and what language the gesture originates from which no other neural network has been able to replicate.

Keywords: convolutional neural networks, deep learning, shallow correctors, sign language

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Authors: S. Mousavian, F. Mousavian, V. Nikkhah Rashidabad

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Cubic equations of state like Redlich–Kwong (RK) EOS have been proved to be very reliable tools in the prediction of phase behavior. Despite their good performance in compositional calculations, they usually suffer from weaknesses in the predictions of saturated liquid density. In this research, RK equation was modified. The result of this study shows that modified equation has good agreement with experimental data.

Keywords: equation of state, modification, ammonia, genetic algorithm

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Authors: Nur Syahidatul Idany Abdul Ghani, Rahizar Ramli, Jamilah Mohamad, Ahmad Saifizul, Mohamed Rehan Karim

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Increasing fatalities in road accidents in Malaysia over the last 10 years are quite alarming. Based on Malaysian Institute of Road Safety Research (Miros) latest research ‘Predicting Malaysian Road Fatalities for year 2020; it is predicted that road fatalities in Malaysia for 2015 is 8,780 and 10,716 for the year 2020 which 30 percent of fatalities were caused by accidents involving commercial vehicles. Government, related agencies and NGOs have continuously and persistently work to reduce the statistics through enforcement, educating the public, training to drivers, road safety campaigns, advertisements etc. However, the trend of casualties does not show encouraging pattern but instead, steadily growing. Thus, this comparative study reviews the literature pertaining on method of measurement used to evaluate commercial drivers competency. In several studies driving competency has been assessed with different assessment based on the license procedures and requirements according to the country regulation. The assessment criteria that has been establish for commercial drivers generally focus on driving tasks and assessment e.g. theory test, medical test and road assessment rather than driving competency test or physical test. Realizing the importance of specific assessment test for drivers competency this comparative study reviews the most discussed literature related to competency assessment method to identify competency of the drivers include (1. judgement and reaction, 2. skill of drivers, 3. experiences and fatigue). The concluding analysis of this paper is a comparative table for assessment methodology to access driver’s competency. A comparative study is a further discussion reviewing past literature to provide an overview on existing assessment test and potential subject matters that can be identified for further studies to increase awareness of the drivers, passengers as well as the authorities about the importance of competent drivers in order to improve safety in commercial vehicles.

Keywords: commercial vehicles, driver’s competency, specific assessment

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Authors: Jose Daniel Hoyos Giraldo, Jesus Hernan Jimenez Giraldo, Juan Pablo Alvarado Perilla

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Range and endurance are the main limitations of electric aircraft due to the nature of its source of power. The improvement of efficiency on this kind of systems is extremely meaningful to encourage the aircraft operation with less environmental impact. The propeller efficiency highly affects the overall efficiency of the propulsion system; hence its optimization can have an outstanding effect on the aircraft performance. An optimization method is applied to an aircraft propeller in order to maximize its range and endurance by estimating the best combination of geometrical parameters such as diameter and airfoil, chord and pitch distribution for a specific aircraft design at a certain cruise speed, then the rotational speed at which the propeller operates at minimum current consumption is estimated. The optimization is based on the Blade Element Momentum (BEM) method, additionally corrected to account for tip and hub losses, Mach number and rotational effects; furthermore an airfoil lift and drag coefficients approximation is implemented from Computational Fluid Dynamics (CFD) simulations supported by preliminary studies of grid independence and suitability of different turbulence models, to feed the BEM method, with the aim of achieve more reliable results. Additionally, Vortex Theory is employed to find the optimum pitch and chord distribution to achieve a minimum induced loss propeller design. Moreover, the optimization takes into account the well-known brushless motor model, thrust constraints for take-off runway limitations, maximum allowable propeller diameter due to aircraft height and maximum motor power. The BEM-CFD method is validated by comparing its predictions for a known APC propeller with both available experimental tests and APC reported performance curves which are based on Vortex Theory fed with the NASA Transonic Airfoil code, showing a adequate fitting with experimental data even more than reported APC data. Optimal propeller predictions are validated by wind tunnel tests, CFD propeller simulations and a study of how the propeller will perform if it replaces the one of on known aircraft. Some tendency charts relating a wide range of parameters such as diameter, voltage, pitch, rotational speed, current, propeller and electric efficiencies are obtained and discussed. The implementation of CFD tools shows an improvement in the accuracy of BEM predictions. Results also showed how a propeller has higher efficiency peaks when it operates at high rotational speed due to the higher Reynolds at which airfoils present lower drag. On the other hand, the behavior of the current consumption related to the propulsive efficiency shows counterintuitive results, the best range and endurance is not necessary achieved in an efficiency peak.

Keywords: BEM, blade design, CFD, electric aircraft, endurance, optimization, range

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Authors: Shao-Huang Wu, Ai-Yun Wu, Meng-Chen Wu, Chun-Liang Wu, Kai-Ping Shaw, Hsiao-Ting Chen

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Objective: To analyze the forensic autopsy data of known passengers and compare it with the National database of the autopsy report in 2017, and obtain the special patterned injuries, which can be used as the reference for the reconstruction of hit-and-run motor vehicle accidents. Methods: Analyze the items of the Motor Vehicle Accident Report, including Date of accident, Time occurred, Day, Acc. severity, Acc. Location, Acc. Class, Collision with Vehicle, Motorcyclists Codes, Safety equipment use, etc. Analyzed the items of the Autopsy Report included, including General Description, Clothing and Valuables, External Examination, Head and Neck Trauma, Trunk Trauma, Other Injuries, Internal Examination, Associated Items, Autopsy Determinations, etc. Materials: Case 1. The process of injury formation: the car was chased forward and collided with the scooter. The passenger wearing the helmet fell to the ground. The helmet crashed under the bottom of the sedan, and the bottom of the sedan was raised. Additionally, the sedan was hit on the left by the other sedan behind, resulting in the front sedan turning 180 degrees on the spot. The passenger’s head was rotated, and the cervical spine was fractured. Injuries: 1. Fracture of atlantoaxial joint 2. Fracture of the left clavicle, scapula, and proximal humerus 3. Fracture of the 1-10 left ribs and 2-7 right ribs with lung contusion and hemothorax 4. Fracture of the transverse process of 2-5 lumbar vertebras 5. Comminuted fracture of the right femur 6. Suspected subarachnoid space and subdural hemorrhage 7. Laceration of the spleen. Case 2. The process of injury formation: The motorcyclist wearing the helmet fell to the left by himself, and his chest was crushed by the car going straight. Only his upper body was under the car and the helmet finally fell off. Injuries: 1. Dislocation of atlantoaxial joint 2. Laceration on the left posterior occipital 3. Laceration on the left frontal 4. Laceration on the left side of the chin 5. Strip bruising on the anterior neck 6. Open rib fracture of the right chest wall 7. Comminuted fracture of both 1-12 ribs 8. Fracture of the sternum 9. Rupture of the left lung 10. Rupture of the left and right atria, heart tip and several large vessels 11. The aortic root is nearly transected 12. Severe rupture of the liver. Results: The common features of the two cases were the fracture or dislocation of the atlantoaxial joint and both helmets that were crashed. There were no atlantoaxial fractures or dislocations in 27 pedestrians (without wearing a helmet) versus motor vehicle accidents in 2017 the National database of an autopsy report, but there were two atlantoaxial fracture or dislocation cases in the database, both of which were cases of falling from height. Conclusion: The cervical spine fracture injury of the motorcyclist, who was wearing a helmet, is very likely to be a patterned injury caused by his/her fall and rollover under the sedan. It could provide a reference for forensic peers.

Keywords: patterned injuries, atlantoaxial fracture or dislocation, accident reconstruction, motorcycle accident with helmet, forensic autopsy data

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Authors: Naina Mahajan, Bikram Pal Kaur

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The purpose of traffic accident analysis is to find the possible causes of an accident. Road accidents cannot be totally prevented but by suitable traffic engineering and management the accident rate can be reduced to a certain extent. This paper discusses the classification techniques C4.5 and ID3 using the WEKA Data mining tool. These techniques use on the NH (National highway) dataset. With the C4.5 and ID3 technique it gives best results and high accuracy with less computation time and error rate.

Keywords: C4.5, ID3, NH(National highway), WEKA data mining tool

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Authors: Stephanie Boue, Nicolas Sierro, Julia Hoeng, Manuel C. Peitsch

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Systems biology relies on large numbers of data points and sophisticated methods to extract biologically meaningful signal and mechanistic understanding. For example, analyses of transcriptomics and proteomics data enable to gain insights into the molecular differences in tissues exposed to diverse stimuli or test items. Whereas the interpretation of endpoints specifically measuring a mechanism is relatively straightforward, the interpretation of big data is more complex and would benefit from comparing results obtained with diverse analysis methods. The sbv IMPROVER project was created to implement solutions to verify systems biology data, methods, and conclusions. Computational challenges leveraging the wisdom of the crowd allow benchmarking methods for specific tasks, such as signature extraction and/or samples classification. Four challenges have already been successfully conducted and confirmed that the aggregation of predictions often leads to better results than individual predictions and that methods perform best in specific contexts. Whenever the scientific question of interest does not have a gold standard, but may greatly benefit from the scientific community to come together and discuss their approaches and results, datathons are set up. The inaugural sbv IMPROVER datathon was held in Singapore on 23-24 September 2016. It allowed bioinformaticians and data scientists to consolidate their ideas and work on the most promising methods as teams, after having initially reflected on the problem on their own. The outcome is a set of visualization and analysis methods that will be shared with the scientific community via the Garuda platform, an open connectivity platform that provides a framework to navigate through different applications, databases and services in biology and medicine. We will present the results we obtained when analyzing data with our network-based method, and introduce a datathon that will take place in Japan to encourage the analysis of the same datasets with other methods to allow for the consolidation of conclusions.

Keywords: big data interpretation, datathon, systems toxicology, verification

Procedia PDF Downloads 278

Authors: Piyush Sharma, Laltu Chandra, P. S. Ghoshdastidar, Rajiv Shekhar

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Metals processing operations such as melting and heat treatment of metals are energy-intensive, requiring temperatures greater than 500oC. The desired temperature in these industrial furnaces is attained by circulating electrically-heated air. In most of these furnaces, electricity produced from captive coal-based thermal power plants is used. Solar thermal energy could be a viable heat source in these furnaces. A retrofitted solar convective furnace (SCF) concept, which uses solar thermal generated hot air, has been proposed. Critical to the success of a SCF is the design of an open volumetric air receiver (OVAR), which can heat air in excess of 800oC. The OVAR is placed on top of a tower and receives concentrated solar radiation from a heliostat field. Absorbers, mixer assembly, and the return air flow chamber (RAFC) are the major components of an OVAR. The absorber is a porous structure that transfers heat from concentrated solar radiation to ambient air, referred to as primary air. The mixer ensures uniform air temperature at the receiver exit. Flow of the relatively cooler return air in the RAFC ensures that the absorbers do not fail by overheating. In an earlier publication, the detailed design basis, fabrication, and characterization of a 2 kWth open volumetric air receiver (OVAR) based laboratory solar air tower simulator was presented. Development of an experimentally-validated, CFD based mathematical model which can ultimately be used for the design and scale-up of an OVAR has been the major objective of this investigation. In contrast to the published literature, where flow and heat transfer have been modeled primarily in a single absorber module, the present study has modeled the entire receiver assembly, including the RAFC. Flow and heat transfer calculations have been carried out in ANSYS using the LTNE model. The complex return air flow pattern in the RAFC requires complicated meshes and is computational and time intensive. Hence a simple, realistic 1-D mathematical model, which circumvents the need for carrying out detailed flow and heat transfer calculations, has also been proposed. Several important results have emerged from this investigation. Circumferential electrical heating of absorbers can mimic frontal heating by concentrated solar radiation reasonably well in testing and characterizing the performance of an OVAR. Circumferential heating, therefore, obviates the need for expensive high solar concentration simulators. Predictions suggest that the ratio of power on aperture (POA) and mass flow rate of air (MFR) is a normalizing parameter for characterizing the thermal performance of an OVAR. Increasing POA/MFR increases the maximum temperature of air, but decreases the thermal efficiency of an OVAR. Predictions of the 1-D mathematical are within 5% of ANSYS predictions and computation time is reduced from ~ 5 hours to a few seconds.

Keywords: absorbers, mixer assembly, open volumetric air receiver, return air flow chamber, solar thermal energy

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Authors: Ramzi Suleiman

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Recently, the author proposed an observationally-based relativity theory termed information relativity theory (IRT). The theory is simple and is based only on basic principles, with no prior axioms and no free parameters. For the case of a body of mass in uniform rectilinear motion relative to an observer, the theory transformations uncovered a matter-dark matter duality, which prescribes that the sum of the densities of the body's baryonic matter and dark matter, as measured by the observer, is equal to the body's matter density at rest. It was shown that the theory transformations were successful in predicting several important phenomena in small particle physics, quantum physics, and cosmology. This paper extends the theory transformations to the cases of rotating disks and spheres. The resulting transformations for a rotating disk are utilized to derive predictions of the radial distributions of matter and dark matter densities in rotationally supported galaxies based solely on their observed rotation curves. It is also shown that for galaxies with flattening curves, good approximations of the radial distributions of matter and dark matter and of the dark matter fraction could be obtained from one measurable scale radius. Test of the model on five galaxies, chosen randomly from the SPARC database, yielded impressive predictions. The rotation curves of all the investigated galaxies emerged as accurate traces of the predicted radial density distributions of their dark matter. This striking result raises an intriguing physical explanation of gravity in galaxies, according to which it is the proximal drag of the stars and gas in the galaxy by its rotating dark matter web. We conclude by alluding briefly to the application of the proposed model to stellar systems and black holes. This study also hints at the potential of the discovered matter-dark matter duality in fixing the standard model of elementary particles in a natural manner without the need for hypothesizing about supersymmetric particles.

Keywords: dark matter, galaxies rotation curves, SPARC, rotating disk

Procedia PDF Downloads 78

Authors: Abid Hossain Khan, Syam Hasan, M. A. R. Sarkar

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Fukushima disaster is one of the most publicly exposed accidents in a nuclear facility which has changed the outlook of people towards nuclear power. Some have used it as an example to establish nuclear energy as an unsafe source, while others have tried to find the real reasons behind this accident. Many papers have tried to shed light on the possible causes, some of which are purely based on assumptions while others rely on rigorous data analysis. To our best knowledge, none of the works can say with absolute certainty that there is a single prominent reason that has paved the way to this unexpected incident. This paper attempts to compile all the apparent reasons behind Fukushima disaster and tries to analyze and identify the most likely one.

Keywords: fuel meltdown, Fukushima disaster, Manmade calamity, nuclear facility, tsunami

Procedia PDF Downloads 266

Authors: Adriana Postal, Silvio C. Sampaio, Marcio A. Villas Boas, Josué P. Castro

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This study deals with the estimation of reference evapotranspiration (ET₀) in an agricultural context, focusing on efficient irrigation management to meet the growing interest in the sustainable management of water resources. Given the importance of water in agriculture and its scarcity in many regions, efficient use of this resource is essential to ensure food security and environmental sustainability. The methodology used involved the application of artificial intelligence techniques, specifically Multilayer Perceptron (MLP) Artificial Neural Networks (ANNs), to predict ET₀ in the state of Paraná, Brazil. The models were trained and validated with meteorological data from the Brazilian National Institute of Meteorology (INMET), together with data obtained from a producer's weather station in the western region of Paraná. Two optimizers (SGD and Adam) and different meteorological variables, such as temperature, humidity, solar radiation, and wind speed, were explored as inputs to the models. Nineteen configurations with different input variables were tested; amidst them, configuration 9, with 8 input variables, was identified as the most efficient of all. Configuration 10, with 4 input variables, was considered the most effective, considering the smallest number of variables. The main conclusions of this study show that MLP ANNs are capable of accurately estimating ET₀, providing a valuable tool for irrigation management in agriculture. Both configurations (9 and 10) showed promising performance in predicting ET₀. The validation of the models with cultivator data underlined the practical relevance of these tools and confirmed their generalization ability for different field conditions. The results of the statistical metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²), showed excellent agreement between the model predictions and the observed data, with MAE as low as 0.01 mm/day and 0.03 mm/day, respectively. In addition, the models achieved an R² between 0.99 and 1, indicating a satisfactory fit to the real data. This agreement was also confirmed by the Kolmogorov-Smirnov test, which evaluates the agreement of the predictions with the statistical behavior of the real data and yields values between 0.02 and 0.04 for the producer data. In addition, the results of this study suggest that the developed technique can be applied to other locations by using specific data from these sites to further improve ET₀ predictions and thus contribute to sustainable irrigation management in different agricultural regions. The study has some limitations, such as the use of a single ANN architecture and two optimizers, the validation with data from only one producer, and the possible underestimation of the influence of seasonality and local climate variability. An irrigation management application using the most efficient models from this study is already under development. Future research can explore different ANN architectures and optimization techniques, validate models with data from multiple producers and regions, and investigate the model's response to different seasonal and climatic conditions.

Keywords: agricultural technology, neural networks in agriculture, water efficiency, water use optimization

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Authors: Ismail Seçer

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The purpose of this study is to investigate the effects of anxiety sensitivity in adolescents on anxiety disorder and childhood depression. Mood disorders and anxiety disorders in children and adolescents can be given examples of important research topics in recent years. The participants of the study consist of 670 students in Erzurum and Erzincan city centers. The participants of the study were 670 secondary and high school students studying in city centers of Erzurum and Erzincan. The participants were chosen based on convenience sampling. The participants were between the ages of 13 and 18 (M=15.7, Ss= 1.35) and 355 were male and 315 were female. The data were collected through Anxiety Sensitivity Index and Anxiety and Depression Index for Children and Adolescents. For data analysis, Correlation analysis and Structural Equation Model were used. In this study, correlational descriptive survey was used. This model enables the researcher to make predictions related to different variables based on the information obtained from one or more variables. Therefore, the purpose is to make predictions considering anxiety disorder and childhood depression based on anxiety sensitivity. For this purpose, latent variable and structural equation model was used. Structural equation model is an analysis method which enables the identification of direct and indirect effects by determining the relationship between observable and latent variables and testing their effects on a single model. CFI, RMR, RMSEA and SRMR, which are commonly accepted fit indices in structural equation model, were used. The results revealed that anxiety sensitivity impacts anxiety disorder and childhood depression through direct and indirect effects in a positive way. The results are discussed in line with the relevant literature. This finding can be considered that anxiety sensitivity can be a significant risk source in terms of children's and adolescents’ anxiety disorder experience. This finding is consistent with relevant research highlighting that in case the anxiety sensitivity increases then the obsessive compulsive disorder and panic attack increase too. The adolescents’ experience of anxiety can be attributed to anxiety sensitivity.

Keywords: anxiety sensitivity, anxiety, depression, structural equation

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Authors: Virginie Hergault, Siham Chebbah, Bertrand Frere

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Following in-house objectives, Central laboratory of Paris police Prefecture conducted a general review on models and Computational Fluid Dynamics (CFD) codes used to simulate pollutant dispersion in the atmosphere. Starting from that review and considering main features of Large Eddy Simulation, Central Laboratory Of Paris Police Prefecture (LCPP) postulates that the Fire Dynamics Simulator (FDS) model, from National Institute of Standards and Technology (NIST), should be well suited for air pollutant dispersion modeling. This paper focuses on the implementation and the evaluation of FDS in the frame of the European COST ES1006 Action. This action aimed at quantifying the performance of modeling approaches. In this paper, the CUTE dataset carried out in the city of Hamburg, and its mock-up has been used. We have performed a comparison of FDS results with wind tunnel measurements from CUTE trials on the one hand, and, on the other, with the models results involved in the COST Action. The most time-consuming part of creating input data for simulations is the transfer of obstacle geometry information to the format required by SDS. Thus, we have developed Python codes to convert automatically building and topographic data to the FDS input file. In order to evaluate the predictions of FDS with observations, statistical performance measures have been used. These metrics include the fractional bias (FB), the normalized mean square error (NMSE) and the fraction of predictions within a factor of two of observations (FAC2). As well as the CFD models tested in the COST Action, FDS results demonstrate a good agreement with measured concentrations. Furthermore, the metrics assessment indicate that FB and NMSE meet the tolerance acceptable.

Keywords: numerical simulations, atmospheric dispersion, cost ES1006 action, CFD model, cute experiments, wind tunnel data, numerical results

Procedia PDF Downloads 133