Search results for: predictions

Authors: Jiaxin Shi, Kaifeng Hao, Qingfan An, Zeng Peng

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Due to a lack of data sources and data processing techniques, it has always been difficult to quantify public space quality, which includes urban construction quality and how it is perceived by people, especially in large urban areas. This study proposes a quantitative research method based on the consideration of emotional health and physical health of the built environment. It highlights the low quality of public areas in Tianjin, China, where there are many negative elements. Deep learning technology is then used to measure how effectively people perceive urban areas. First, this work suggests a deep learning model that might simulate how people can perceive the quality of urban construction. Second, we perform semantic segmentation on street images to identify visual elements influencing scene perception. Finally, this study correlated the scene perception score with the proportion of visual elements to determine the surrounding environmental elements that influence scene perception. Using a small-scale labeled Tianjin street view data set based on transfer learning, this study trains five negative spatial discriminant models in order to explore the negative space distribution and quality improvement of urban streets. Then it uses all Tianjin street-level imagery to make predictions and calculate the proportion of negative space. Visualizing the spatial distribution of negative space along the Tianjin Inner Ring Road reveals that the negative elements are mainly found close to the five key districts. The map of Tianjin was combined with the experimental data to perform the visual analysis. Based on the emotional assessment, the distribution of negative materials, and the direction of street guidelines, we suggest guidance content and design strategy points of the negative phenomena in Tianjin street space in the two dimensions of perception and substance. This work demonstrates the utilization of deep learning techniques to understand how people appreciate high-quality urban construction, and it complements both theory and practice in urban planning. It illustrates the connection between human perception and the actual physical public space environment, allowing researchers to make urban interventions.

Keywords: human perception, public space quality, deep learning, negative elements, street images

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Authors: Madhvi Bhayani

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Introduction: Due to the rapid pace of urbanization, the urban health issues have become one of the significant threats to future development in India. It also poses serious repercussions on the citizen’s health. As urbanization in India is increasing at an unprecedented rate and it has generated the urban health crisis among the city dwellers especially the urban poor. The increasing proportion of the urban poor and vulnerable to the health indicators worse than the rural counterparts, they face social and financial barriers in accessing healthcare services and these conditions make human health at risk. The Local as well as the State and National governments are alike tackling with the challenges of urbanization as it has become very essential for the government to provide the basic necessities and better infrastructure that make life in cities safe and healthy. Thus, the paper argues that if no major realistic steps are taken with immediate effect, the citizens will face a huge burden of health hazards. Aim: This paper attempts to analyze the current infrastructure, government planning, and its future policy, it also discusses the challenges and outcomes of urbanization on health and its impact on it and it will also predict the future trend with regard to disease burden in the urban areas. Methods: The paper analyzes on the basis of the secondary data by taking into consideration the connection between the Rapid Urbanization and Public Health Challenges, health and health care system and its services delivery to the citizens especially to the urban poor. Extensive analyses of government census reports, health information and policy, the government health-related schemes, urban development and based on the past trends, the future status of urban infrastructure and health outcomes are predicted. The social-economic and political dimensions are also taken into consideration from regional, national and global perspectives, which are incorporated in the paper to make realistic predictions for the future. Findings and Conclusion: The findings of the paper show that India suffers a lot due to the double burden of rapidly increasing in diseases and also growing health inequalities and disparities in health outcomes. Existing tools of governance of urban health are falling short to provide the better health care services. They need to strengthen the collaboration and communication among the state, national and local governments and also with the non-governmental partners. Based on the findings the policy implications are then described and areas for future research are defined.

Keywords: health care, urbanization, urban health, service delivery

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Authors: A. F. Momin, D. V. Khakhar

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Granular materials consist of particulate particles found in nature and various industries that, due to gravity flow, behave macroscopically like liquids. A fundamental industrial unit operation is a hopper with inclined walls or a converging channel in which material flows downward under gravity and exits the storage bin through the bottom outlet. The simplest form of the flow corresponds to a wedge-shaped, quasi-two-dimensional geometry with smooth walls and radially directed gravitational force toward the apex of the wedge. These flows were examined using the Mohr-Coulomb criterion in the classic work of Savage (1965), while Ravi Prakash and Rao used the critical state theory (1988). The smooth-wall radial gravity (SWRG) wedge-shaped hopper is simulated using the discrete element method (DEM) to test existing theories. DEM simulations involve the solution of Newton's equations, taking particle-particle interactions into account to compute stress and velocity fields for the flow in the SWRG system. Our computational results are consistent with the predictions of Savage (1965) and Ravi Prakash and Rao (1988), except for the region near the exit, where both viscous and frictional effects are present. To further comprehend this behaviour, a parametric analysis is carried out to analyze the rheology of wedge-shaped hoppers by varying the orifice diameter, wedge angle, friction coefficient, and stiffness. The conclusion is that velocity increases as the flow rate increases but decreases as the wedge angle and friction coefficient increase. We observed no substantial changes in velocity due to varying stiffness. It is anticipated that stresses at the exit result from the transfer of momentum during particle collisions; for this reason, relationships between viscosity and shear rate are shown, and all data are collapsed into a single curve. In addition, it is demonstrated that viscosity and volume fraction exhibit power law correlations with the inertial number and that all the data collapse into a single curve. A continuum model for determining granular flows is presented using empirical correlations.

Keywords: discrete element method, gravity flow, smooth-wall, wedge-shaped hoppers

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Authors: Natalia Rudeli, Elisabeth Viles, Adrian Santilli

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Delays in the construction industry are a global phenomenon. Many construction projects experience extensive delays exceeding the initially estimated completion time. The main purpose of this study is to identify construction projects typical behaviors in order to develop a prognosis and management tool. Being able to know a construction projects schedule tendency will enable evidence-based decision-making to allow resolutions to be made before delays occur. This study presents an innovative approach that uses Cluster Analysis Method to support predictions during Earned Value Analyses. A clustering analysis was used to predict future scheduling, Earned Value Management (EVM), and Earned Schedule (ES) principal Indexes behaviors in construction projects. The analysis was made using a database with 90 different construction projects. It was validated with additional data extracted from literature and with another 15 contrasting projects. For all projects, planned and executed schedules were collected and the EVM and ES principal indexes were calculated. A complete linkage classification method was used. In this way, the cluster analysis made considers that the distance (or similarity) between two clusters must be measured by its most disparate elements, i.e. that the distance is given by the maximum span among its components. Finally, through the use of EVM and ES Indexes and Tukey and Fisher Pairwise Comparisons, the statistical dissimilarity was verified and four clusters were obtained. It can be said that construction projects show an average delay of 35% of its planned completion time. Furthermore, four typical behaviors were found and for each of the obtained clusters, the interim milestones and the necessary rhythms of construction were identified. In general, detected typical behaviors are: (1) Projects that perform a 5% of work advance in the first two tenths and maintain a constant rhythm until completion (greater than 10% for each remaining tenth), being able to finish on the initially estimated time. (2) Projects that start with an adequate construction rate but suffer minor delays culminating with a total delay of almost 27% of the planned time. (3) Projects which start with a performance below the planned rate and end up with an average delay of 64%, and (4) projects that begin with a poor performance, suffer great delays and end up with an average delay of a 120% of the planned completion time. The obtained clusters compose a tool to identify the behavior of new construction projects by comparing their current work performance to the validated database, thus allowing the correction of initial estimations towards more accurate completion schedules.

Keywords: cluster analysis, construction management, earned value, schedule

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Authors: Mohammad Ghahramani, Fahimeh Saei Manesh

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Winning a soccer game is based on thorough and deep analysis of the ongoing match. On the other hand, giant gambling companies are in vital need of such analysis to reduce their loss against their customers. In this research work, we perform deep, real-time analysis on every soccer match around the world that distinguishes our work from others by focusing on particular seasons, teams and partial analytics. Our contributions are presented in the platform called “Analyst Masters.” First, we introduce various sources of information available for soccer analysis for teams around the world that helped us record live statistical data and information from more than 50,000 soccer matches a year. Our second and main contribution is to introduce our proposed in-play performance evaluation. The third contribution is developing new features from stable soccer matches. The statistics of soccer matches and their odds before and in-play are considered in the image format versus time including the halftime. Local Binary patterns, (LBP) is then employed to extract features from the image. Our analyses reveal incredibly interesting features and rules if a soccer match has reached enough stability. For example, our “8-minute rule” implies if 'Team A' scores a goal and can maintain the result for at least 8 minutes then the match would end in their favor in a stable match. We could also make accurate predictions before the match of scoring less/more than 2.5 goals. We benefit from the Gradient Boosting Trees, GBT, to extract highly related features. Once the features are selected from this pool of data, the Decision trees decide if the match is stable. A stable match is then passed to a post-processing stage to check its properties such as betters’ and punters’ behavior and its statistical data to issue the prediction. The proposed method was trained using 140,000 soccer matches and tested on more than 100,000 samples achieving 98% accuracy to select stable matches. Our database from 240,000 matches shows that one can get over 20% betting profit per month using Analyst Masters. Such consistent profit outperforms human experts and shows the inefficiency of the betting market. Top soccer tipsters achieve 50% accuracy and 8% monthly profit in average only on regional matches. Both our collected database of more than 240,000 soccer matches from 2012 and our algorithm would greatly benefit coaches and punters to get accurate analysis.

Keywords: soccer, analytics, machine learning, database

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Authors: Hsiu-Tsu Cho

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Collective efficacy refers to a group’s sense of its capacity to complete a task successfully or to reach objectives. Little effort has been expended on investigating the relationship between the emotion expression of a leader and collective efficacy. In this study, we examined the impact of the different emotions and emotion expression of a group leader on collective efficacy and explored whether the emotion–expressive effects differed under conditions of negative and positive emotions. A total of 240 undergraduate and graduate students recruited using Facebook and posters at a university participated in this research. The participants were separated randomly into 80 groups of four persons consisting of three participants and a confederate. They were randomly assigned to one of five conditions in a 2 (pride vs. guilt) × 2 (emotion expression of group leader vs. no emotion expression of group leader) factorial design and a control condition. Each four-person group was instructed to get the reward in a group competition of solving the five-disk Tower of Hanoi puzzle and making decisions on an investment case. We surveyed the participants by employing the emotional measure revised from previous researchers and collective efficacy questionnaire on a 5-point scale. To induce an emotion of pride (or guilt), the experimenter announced whether the group performance was good enough to have a chance of getting the reward (ranking the top or bottom 20% among all groups) after group task. The leader (confederate) could either express or not express a feeling of pride (or guilt) following the instruction according to the assigned condition. To check manipulation of emotion, we added a control condition under which the experimenter revealed no results regarding group performance in maintaining a neutral emotion. One-way ANOVAs and post hoc pairwise comparisons among the three emotion conditions (pride, guilt, and control condition) involved assigning pride and guilt scores (pride: F(1,75) = 32.41, p < .001; guilt: F(1,75) = 6.75, p < .05). The results indicated that manipulations of emotion were successful. A two-way between-measures ANOVA was conducted to examine the predictions of the main effects of emotion types and emotion expression as well as the interaction effect of these two variables on collective efficacy. The experimental findings suggest that pride did not affect collective efficacy (F(1,60) = 1.90, ns.) more than guilt did and that the group leader did not motivate collective efficacy regardless of whether he or she expressed emotion (F(1,60) = .89, ns.). However, the interaction effect of emotion types and emotion expression was statistically significant (F(1,60) = 4.27, p < .05, ω2 = .066); the effects accounted for 6.6% of the variance. Additional results revealed that, under the pride condition, the leader enhanced group efficacy when expressing emotion, whereas, under the guilt condition, an expression of emotion could reduce collective efficacy. Overall, these findings challenge the assumption that the effect of expression emotion are the same on all emotions and suggest that a leader should be cautious when expressing negative emotions toward a group to avoid reducing group effectiveness.

Keywords: collective efficacy, group leader, emotion expression, pride, guilty

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Authors: Bilel Chalghaf, Mathieu Varin

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Forest characterization in Quebec, Canada, is usually assessed based on photo-interpretation at the stand level. For species identification, this often results in a lack of precision. Very high spatial resolution imagery, such as DigitalGlobe, and Light Detection and Ranging (LiDAR), have the potential to overcome the limitations of aerial imagery. To date, few studies have used that data to map a large number of species at the tree level using machine learning techniques. The main objective of this study is to map 11 individual high tree species ( > 17m) at the tree level using an object-based approach in the broadleaf forest of Kenauk Nature, Quebec. For the individual tree crown segmentation, three canopy-height models (CHMs) from LiDAR data were assessed: 1) the original, 2) a filtered, and 3) a corrected model. The corrected CHM gave the best accuracy and was then coupled with imagery to refine tree species crown identification. When compared with photo-interpretation, 90% of the objects represented a single species. For modeling, 313 variables were derived from 16-band WorldView-3 imagery and LiDAR data, using radiance, reflectance, pixel, and object-based calculation techniques. Variable selection procedures were employed to reduce their number from 313 to 16, using only 11 bands to aid reproducibility. For classification, a global approach using all 11 species was compared to a semi-hierarchical hybrid classification approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were used: (1) support vector machine (SVM), (2) classification and regression tree (CART), (3) random forest (RF), (4) k-nearest neighbors (k-NN), and (5) linear discriminant analysis (LDA). Each model was tuned separately for all approaches and levels. For the global approach, the best model was the SVM using eight variables (overall accuracy (OA): 80%, Kappa: 0.77). With the semi-hierarchical hybrid approach, at the tree type level, the best model was the k-NN using six variables (OA: 100% and Kappa: 1.00). At the level of identifying broadleaf and conifer species, the best model was the SVM, with OA of 80% and 97% and Kappa values of 0.74 and 0.97, respectively, using seven variables for both models. This paper demonstrates that a hybrid classification approach gives better results and that using 16-band WorldView-3 with LiDAR data leads to more precise predictions for tree segmentation and classification, especially when the number of tree species is large.

Keywords: tree species, object-based, classification, multispectral, machine learning, WorldView-3, LiDAR

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Authors: Ali K. M. Al-Nasrawi, Uday A. Al-Hamdany, Sarah M. Hamylton, Brian G. Jones, Yasir M. Alyazichi

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Surface elevation dynamics have always responded to disturbance regimes. Creating Digital Elevation Models (DEMs) to detect surface dynamics has led to the development of several methods, devices and data clouds. DEMs can provide accurate and quick results with cost efficiency, in comparison to the inherited geomatics survey techniques. Nowadays, remote sensing datasets have become a primary source to create DEMs, including LiDAR point clouds with GIS analytic tools. However, these data need to be tested for error detection and correction. This paper evaluates various DEMs from different data sources over time for Apple Orchard Island, a coastal site in southeastern Australia, in order to detect surface dynamics. Subsequently, 30 chosen locations were examined in the field to test the error of the DEMs surface detection using high resolution global positioning systems (GPSs). Results show significant surface elevation changes on Apple Orchard Island. Accretion occurred on most of the island while surface elevation loss due to erosion is limited to the northern and southern parts. Concurrently, the projected differential correction and validation method aimed to identify errors in the dataset. The resultant DEMs demonstrated a small error ratio (≤ 3%) from the gathered datasets when compared with the fieldwork survey using RTK-GPS. As modern modelling approaches need to become more effective and accurate, applying several tools to create different DEMs on a multi-temporal scale would allow easy predictions in time-cost-frames with more comprehensive coverage and greater accuracy. With a DEM technique for the eco-geomorphic context, such insights about the ecosystem dynamic detection, at such a coastal intertidal system, would be valuable to assess the accuracy of the predicted eco-geomorphic risk for the conservation management sustainability. Demonstrating this framework to evaluate the historical and current anthropogenic and environmental stressors on coastal surface elevation dynamism could be profitably applied worldwide.

Keywords: DEMs, eco-geomorphic-dynamic processes, geospatial Information Science, remote sensing, surface elevation changes,

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Authors: Moustafa Osman Mohammed

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The photocurrent generations are influencing ultra-high efficiency solar cells based on self-assembled quantum dot (QD) nanostructures. Nanocrystal quantum dots (QD) provide a great enhancement toward solar cell efficiencies through the use of quantum confinement to tune absorbance across the solar spectrum enabled multi-exciton generation. Based on theoretical predictions, QDs have potential to improve systems efficiency in approximate regular electrons excitation intensity greater than 50%. In solar cell devices, an intermediate band formed by the electron levels in quantum dot systems. The spatial architecture is exploring how can solar cell integrate and produce not only high open circuit voltage (> 1.7 eV) but also large short-circuit currents due to the efficient absorption of sub-bandgap photons. In the proposed QD system, the structure allows barrier material to absorb wavelengths below 700 nm while multi-photon processes in the used quantum dots to absorb wavelengths up to 2 µm. The assembly of the electronic model is flexible to demonstrate the atoms and molecules structure and material properties to tune control energy bandgap of the barrier quantum dot to their respective optimum values. In terms of energy virtual conversion, the efficiency and cost of the electronic structure are unified outperform a pair of multi-junction solar cell that obtained in the rigorous test to quantify the errors. The milestone toward achieving the claimed high-efficiency solar cell device is controlling the edge causes of energy bandgap between the barrier material and quantum dot systems according to the media design limits. Despite this remarkable potential for high photocurrent generation, the achievable open-circuit voltage (Voc) is fundamentally limited due to non-radiative recombination processes in QD solar cells. The orientation of voltage recovery system is compared theoretically with experimental Voc variation in mediation upper–limit obtained one diode modeling form at the cells with different bandgap (Eg) as classified in the proposed spatial architecture. The opportunity for improvement Voc is valued approximately greater than 1V by using smaller QDs through QD solar cell recovery systems as confined to other micro and nano operations states.

Keywords: nanotechnology, photovoltaic solar cell, quantum systems, renewable energy, environmental modeling

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Authors: Sulemana Ibrahim

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Food security remains a paramount global challenge, with vulnerable regions grappling with issues of hunger and malnutrition. This study embarks on a comprehensive exploration of data-driven strategies aimed at ameliorating food security in such regions. Our research employs a multifaceted approach, integrating data analytics to predict crop yields, optimizing supply chains, and enhancing food distribution networks. The study unfolds as a multi-dimensional analysis, commencing with the development of robust machine learning models harnessing remote sensing data, historical crop yield records, and meteorological data to foresee crop yields. These predictive models, underpinned by convolutional and recurrent neural networks, furnish critical insights into anticipated harvests, empowering proactive measures to confront food insecurity. Subsequently, the research scrutinizes supply chain optimization to address food security challenges, capitalizing on linear programming and network optimization techniques. These strategies intend to mitigate loss and wastage while streamlining the distribution of agricultural produce from field to fork. In conjunction, the study investigates food distribution networks with a particular focus on network efficiency, accessibility, and equitable food resource allocation. Network analysis tools, complemented by data-driven simulation methodologies, unveil opportunities for augmenting the efficacy of these critical lifelines. This study also considers the ethical implications and privacy concerns associated with the extensive use of data in the realm of food security. The proposed methodology outlines guidelines for responsible data acquisition, storage, and usage. The ultimate aspiration of this research is to forge a nexus between data science and food security policy, bestowing actionable insights to mitigate the ordeal of food insecurity. The holistic approach converging data-driven crop yield forecasts, optimized supply chains, and improved distribution networks aspire to revitalize food security in the most vulnerable regions, elevating the quality of life for millions worldwide.

Keywords: data-driven strategies, crop yield prediction, supply chain optimization, food distribution networks

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Authors: Harshan Tee Pee

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Climate elements include surface temperatures, rainfall patterns, humidity, type and amount of cloudiness, air pressure and wind speed and direction. Change in one element can have an impact on the regional climate. The scientific predictions indicate that global climate change will increase the number of extreme events, leading to more frequent natural hazards. Global warming is likely to intensify the risk of drought in certain parts and also leading to increased rainfall in some other parts. Drought is a slow advancing disaster and creeping phenomenon– which accumulate slowly over a long period of time. Droughts are naturally linked with aridity. But droughts occur over most parts of the world (both wet and humid regions) and create severe impacts on agriculture, basic household welfare and ecosystems. Drought condition occurs at least every three years in India. India is one among the most vulnerable drought prone countries in the world. The economic impacts resulting from extreme environmental events and disasters are huge as a result of disruption in many economic activities. The focus of this paper is to develop a comprehensive understanding about the distributional impacts of disaster, especially impact of drought on agricultural production and income through a panel study (drought year and one year after the drought) in Raikhel village, Maharashtra, India. The major findings of the study indicate that cultivating area as well as the number of cultivating households reduced after the drought, indicating a shift in the livelihood- households moved from agriculture to non-agriculture. Decline in the gross cropped area and production of various crops depended on the negative income from these crops in the previous agriculture season. All the landholding categories of households except landlords had negative income in the drought year and also the income disparities between the households were higher in that year. In the drought year, the cost of cultivation was higher for all the landholding categories due to the increased cost for irrigation and input cost. In the drought year, agriculture products (50 per cent of the total products) were used for household consumption rather than selling in the market. It is evident from the study that livelihood which was based on natural resources became less attractive to the people to due to the risk involved in it and people were moving to less risk livelihood for their sustenance.

Keywords: climate change, drought, agriculture economics, disaster impact

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Authors: M. J. Baum, B. Gibbes, A. Grinham, S. Albert, D. Gale, P. Fisher

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Alongside the rapid expansion of Seawater Reverse Osmosis technologies there is a concurrent increase in the production of hypersaline brine by-products. To minimize environmental impact, these by-products are commonly disposed into open-coastal environments via submerged diffuser systems as inclined dense jet outfalls. Despite the widespread implementation of this process, diffuser designs are typically based on small-scale laboratory experiments under idealistic quiescent conditions. Studies concerning diffuser performance in the field are limited. A set of experiments were conducted to assess the near field characteristics of brine disposal at the Gold Coast Desalination Plant offshore multiport diffuser. The aim of the field experiments was to determine the trajectory and dilution characteristics of the plume under various discharge configurations with production ranging 66 – 100% of plant operative capacity. The field monitoring system employed an unprecedented static array of temperature and electrical conductivity sensors in a three-dimensional grid surrounding a single diffuser port. Complimenting these measurements, Acoustic Doppler Current Profilers were also deployed to record current variability over the depth of the water column and wave characteristics. Recorded data suggested the open-coastal environment was highly active over the experimental duration with ambient velocities ranging 0.0 – 0.5 m∙s^-1, with considerable variability over the depth of the water column observed. Variations in background electrical conductivity corresponding to salinity fluctuations of ± 1.7 g∙kg^-1 were also observed. Increases in salinity were detected during plant operation and appeared to be most pronounced 10 – 30 m from the diffuser, consistent with trajectory predictions described by existing literature. Plume trajectories and respective dilutions extrapolated from salinity data are compared with empirical scaling arguments. Discharge properties were found to adequately correlate with modelling projections. Temporal and spatial variation of background processes and their subsequent influence upon discharge outcomes are discussed with a view to incorporating the influence of waves and ambient currents in the design of brine outfalls into the future.

Keywords: brine disposal, desalination, field study, negatively buoyant discharge

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Authors: Binger Lu

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

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

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Authors: Dmitriy V. Chulkov

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Escalation of commitment is defined as continuation of a project after receiving negative information about it. While literature in management and psychology identified various factors contributing to escalation behavior, this phenomenon has received little analysis in economics, potentially due to the apparent irrationality of escalation. In this study, we present an economic model of escalation with asymmetric information in a principal-agent setup where the agents are responsible for a project selection decision and discover the outcome of the project before the principal. Our theoretical model complements the existing literature on several accounts. First, we link the incentive to escalate commitment to a project with the turnover decision by the manager. When a manager learns the outcome of the project and stops it that reveals that a mistake was made. There is an incentive to continue failing projects and avoid admitting the mistake. This incentive is enhanced when the agent may voluntarily resign from the firm before the outcome of the failing project is revealed, and thus not bear the full extent of reputation damage due to project failure. As long as some successful managers leave the firm for extraneous reasons, outside firms find it difficult to link failing projects with certainty to managers that left a firm. Second, we demonstrate that non-CEO managers have reputation concerns separate from those of the CEO, and thus may escalate commitment to projects they oversee, when such escalation can attenuate damage to reputation from impending project failure. Such incentive for escalation will be present for non-CEO managers if the CEO delegates responsibility for a project to a non-CEO executive. If reputation matters for promotion to the CEO, the incentive for a rising executive to escalate in order to protect reputation is distinct from that of a CEO. Third, our theoretical model is supported by empirical analysis of changes in the firm’s operations measured by the presence of discontinued operations at the time of turnover among the top four members of the top management team. Discontinued operations are indicative of termination of failing projects at a firm. The empirical results demonstrate that in a large dataset of over three thousand publicly traded U.S. firms for a period from 1993 to 2014 turnover by top executives significantly increases the likelihood that the firm discontinues operations. Furthermore, the type of turnover matters as this effect is strongest when at least one non-CEO member of the top management team leaves the firm and when the CEO departure is due to a voluntary resignation and not to a retirement or illness. Empirical results are consistent with the predictions of the theoretical model and suggest that escalation of commitment is primarily observed in decisions by non-CEO members of the top management team.

Keywords: discontinued operations, escalation of commitment, executive turnover, top management teams

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Authors: Hesam Gholami

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Elevated suspended sediment loads in riverine systems resulting from accelerated erosion due to human activities are a serious threat to the sustainable management of watersheds and ecosystem services therein worldwide. Therefore, mitigation of deleterious sediment effects as a distributed or non-point pollution source in the catchments requires reliable provenance information. Sediment tracing or sediment fingerprinting, as a combined process consisting of sampling, laboratory measurements, different statistical tests, and the application of mixing or unmixing models, is a useful technique for discriminating the sources of sediments. From 1996 to the present, different aspects of this technique, such as grouping the sources (spatial and individual sources), discriminating the potential sources by different statistical techniques, and modification of mixing and unmixing models, have been introduced and modified by many researchers worldwide, and have been applied to identify the provenance of fine materials in agricultural, rural, mountainous, and coastal catchments, and in large catchments with numerous lakes and reservoirs. In the last two decades, efforts exploring the uncertainties associated with sediment fingerprinting results have attracted increasing attention. The frameworks used to quantify the uncertainty associated with fingerprinting estimates can be divided into three groups comprising Monte Carlo simulation, Bayesian approaches and generalized likelihood uncertainty estimation (GLUE). Given the above background, the primary goal of this study was to apply geochemical fingerprinting within the GLUE framework in the estimation of sub-basin spatial sediment source contributions in the arid Mehran River catchment in southern Iran, which drains into the Persian Gulf. The accuracy of GLUE predictions generated using four different sets of statistical tests for discriminating three sub-basin spatial sources was evaluated using 10 virtual sediments (VS) samples with known source contributions using the root mean square error (RMSE) and mean absolute error (MAE). Based on the results, the contributions modeled by GLUE for the western, central and eastern sub-basins are 1-42% (overall mean 20%), 0.5-30% (overall mean 12%) and 55-84% (overall mean 68%), respectively. According to the mean absolute fit (MAF; ≥ 95% for all target sediment samples) and goodness-of-fit (GOF; ≥ 99% for all samples), our suggested modeling approach is an accurate technique to quantify the source of sediments in the catchments. Overall, the estimated source proportions can help watershed engineers plan the targeting of conservation programs for soil and water resources.

Keywords: sediment source tracing, generalized likelihood uncertainty estimation, virtual sediment mixtures, Iran

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Authors: Mohammad R. Jalali

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Movement of liquid with a free surface in a container is known as slosh. For instance, slosh occurs when water in a closed tank is set in motion by a free surface displacement, or when liquid natural gas in a container is vibrated by an external driving force, such as an earthquake or movement induced by transport. Slosh is also derived from resonant switching of a natural basin. During sloshing, different types of motion are produced by energy exchange between the liquid and its container. In present study, a numerical model is developed to simulate the nonlinear even harmonic oscillations of free surface sloshing of an initial disturbance to the free surface of a liquid in a closed square basin. The response of the liquid free surface is affected by amplitude and motion frequencies of its container; therefore, sloshing involves complex fluid-structure interactions. In the present study, nonlinear interaction of free surface sloshing of an initial Gaussian hump with its uneven container is predicted numerically. For this purpose, Green-Naghdi (GN) equations are applied as governing equation of fluid field to produce nonlinear second-order and higher-order wave interactions. These equations reduce the dimensions from three to two, yielding equations that can be solved efficiently. The GN approach assumes a particular flow kinematic structure in the vertical direction for shallow and deep-water problems. The fluid velocity profile is finite sum of coefficients depending on space and time multiplied by a weighting function. It should be noted that in GN theory, the flow is rotational. In this study, GN numerical simulations of initial Gaussian hump are compared with Fourier series semi-analytical solutions of the linearized shallow water equations. The comparison reveals that satisfactory agreement exists between the numerical simulation and the analytical solution of the overall free surface sloshing patterns. The resonant free surface motions driven by an initial Gaussian disturbance are obtained by Fast Fourier Transform (FFT) of the free surface elevation time history components. Numerically predicted velocity vectors and magnitude contours for the free surface patterns indicate that interaction of Gaussian hump with its container has localized effect. The result of this sloshing is applicable to the design of stable liquefied oil containers in tankers and offshore platforms.

Keywords: fluid-structure interactions, free surface sloshing, Gaussian hump, Green-Naghdi equations, numerical predictions

Procedia PDF Downloads 370

Authors: Ahmed Alghurabi, Mysara Mohyaldinn, Shiferaw Jufar, Obai Younis, Abdullah Abduljabbar

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Erosion modeling equations were typically acquired from regulated experimental trials for solid particles entrained in single-phase or multi-phase flows. Evidently, those equations were later employed to predict the erosion damage caused by the continuous impacts of solid particles entrained in streamflow. It is also well-known that the particle impact angle and velocity do not change drastically in gas-sand flow erosion prediction; hence an accurate prediction of erosion can be projected. On the contrary, high-density fluid flows, such as water flow, through complex geometries, such as sand screens, greatly affect the sand particles’ trajectories/tracks and consequently impact the erosion rate predictions. Particle tracking models and erosion equations are frequently applied simultaneously as a method to improve erosion visualization and estimation. In the present work, computational fluid dynamic (CFD)-based erosion modeling was performed using a commercially available software; ANSYS Fluent. The continuous phase (water flow) behavior was simulated using the realizable K-epsilon model, and the secondary phase (solid particles), having a 5% flow concentration, was tracked with the help of the discrete phase model (DPM). To accomplish a successful erosion modeling, three erosion equations from the literature were utilized and introduced to the ANSYS Fluent software to predict the screen wire-slot velocity surge and estimate the maximum erosion rates on the screen surface. Results of turbulent kinetic energy, turbulence intensity, dissipation rate, the total pressure on the screen, screen wall shear stress, and flow velocity vectors were presented and discussed. Moreover, the particle tracks and path-lines were also demonstrated based on their residence time, velocity magnitude, and flow turbulence. On one hand, results from the utilized erosion equations have shown similarities in screen erosion patterns, locations, and DPM concentrations. On the other hand, the model equations estimated slightly different values of maximum erosion rates of the wire-wrapped screen. This is solely based on the fact that the utilized erosion equations were developed with some assumptions that are controlled by the experimental lab conditions.

Keywords: CFD simulation, erosion rate prediction, material loss due to erosion, water-sand flow

Procedia PDF Downloads 132

Authors: Cleyton S. Stampa

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This paper addresses the perspectives of using low melting point metals (LMPMs) as phase change materials (PCMs) in latent thermal energy storage (LTES) units, through a numerical approach. This is a new class of PCMs that has been one of the most prospective alternatives to be considered in LTES, due to these materials present high thermal conductivity and elevated heat of fusion, per unit volume. The chosen type of LTES consists of several horizontal parallel slabs filled with PCM. The heat transfer fluid (HTF) circulates through the channel formed between each two consecutive slabs on a laminar regime through forced convection. The study deals with the LTES charging process (heat-storing) by using pure gallium as PCM, and it considers heat conduction in the solid phase during melting driven by natural convection in the melt. The transient heat transfer problem is analyzed in one arbitrary slab under the influence of the HTF. The mathematical model to simulate the isothermal phase change is based on a volume-averaged enthalpy method, which is successfully verified by comparing its predictions with experimental data from works available in the pertinent literature. Regarding the convective heat transfer problem in the HTF, it is assumed that the flow is thermally developing, whereas the velocity profile is already fully developed. The study aims to learn about the effect of the solid subcooling in the melting rate through comparisons with the melting process of the solid in which it starts to melt from its fusion temperature. In order to best understand this effect in a metallic compound, as it is the case of pure gallium, the study also evaluates under the same conditions established for the gallium, the melting process of commercial paraffin wax (organic compound) and of the calcium chloride hexahydrate (CaCl₂ 6H₂O-inorganic compound). In the present work, it is adopted the best options that have been established by several researchers in their parametric studies with respect to this type of LTES, which lead to high values of thermal efficiency. To do so, concerning with the geometric aspects, one considers a gap of the channel formed by two consecutive slabs, thickness and length of the slab. About the HTF, one considers the type of fluid, the mass flow rate, and inlet temperature.

Keywords: flat slab, heat storing, pure metal, solid subcooling

Procedia PDF Downloads 110

Authors: Siva K. Bathina, Sudheer Siddapureddy

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Pool fires are formed when the flammable liquid accidentally spills on the ground or water and ignites. Pool fire is a kind of buoyancy-driven and diffusion flame. There have been many pool fire accidents caused during processing, handling and storing of liquid fuels in chemical and oil industries. Such kind of accidents causes enormous damage to property as well as the loss of lives. Pool fires are complex in nature due to the strong interaction among the combustion, heat and mass transfers and pyrolysis at the fuel surface. Moreover, the experimental study of such large complex fires involves fire safety issues and difficulties in performing experiments. In the present work, large eddy simulations are performed to study such complex fire scenarios using fire dynamic simulator. A 1 m diesel pool fire is considered for the studied cases, and diesel is chosen as it is most commonly involved fuel in fire accidents. Fire simulations are performed by specifying two different boundary conditions: one the fuel is in liquid state and pyrolysis model is invoked, and the other by assuming the fuel is initially in a vapor state and thereby prescribing the mass loss rate. A domain of size 11.2 m × 11.2 m × 7.28 m with uniform structured grid is chosen for the numerical simulations. Grid sensitivity analysis is performed, and a non-dimensional grid size of 12 corresponding to 8 cm grid size is considered. Flame properties like mass burning rate, irradiance, and time-averaged axial flame temperature profile are predicted. The predicted steady-state mass burning rate is 40 g/s and is within the uncertainty limits of the previously reported experimental data (39.4 g/s). Though the profile of the irradiance at a distance from the fire along the height is somewhat in line with the experimental data and the location of the maximum value of irradiance is shifted to a higher location. This may be due to the lack of sophisticated models for the species transportation along with combustion and radiation in the continuous zone. Furthermore, the axial temperatures are not predicted well (for any of the boundary conditions) in any of the zones. The present study shows that the existing models are not sufficient enough for modeling blended fuels like diesel. The predictions are strongly dependent on the experimental values of the soot yield. Future experiments are necessary for generalizing the soot yield for different fires.

Keywords: burning rate, fire accidents, fire dynamic simulator, pyrolysis

Procedia PDF Downloads 169

Authors: Paul E. Jose

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A new self-report measure named the General Emotion Regulation Measure (GERM) assesses four key goals for experiencing broad valenced groups of emotions: 1) trying to experience positive emotions (e.g., joy, pride, liking a person); 2) trying to avoid experiencing positive emotions; 3) trying to experience negative emotions (e.g., anger, anxiety, contempt); and 4) trying to avoid experiencing negative emotions. Although individual differences in GERM motives have been identified, evidence of validity with common mood outcomes is lacking. In the present study, whether GERM motives predict self-reported subjective happiness and depressive symptoms (CES-D) was tested with a community sample of 833 young adults. It was predicted that the GERM motive of trying to experience positive emotions would positively predict subjective happiness, and analogously trying to experience negative emotions would predict depressive symptoms. An initial path model was constructed in which the four GERM motives predicted both subjective happiness and depressive symptoms. The fully saturated model included three non-significant paths, which were subsequently pruned, and a good fitting model was obtained (CFI = 1.00; RMR = .007). Two GERM motives significantly predicted subjective happiness: 1) trying to experience positive emotions ( = .38, p < .001) and 2) trying to avoid experiencing positive emotions ( = -.48, p <.001). Thus, individuals who reported high levels of trying to experience positive emotions reported high levels of happiness, and individuals who reported low levels of trying to avoid experiencing positive emotions also reported high levels of happiness. Three GERM motives significantly predicted depressive symptoms: 1) trying to avoid experiencing positive emotions ( = .20, p <.001); 2) trying to experience negative emotions ( = .15, p <.001); and 3) trying to experience positive emotions (= -.07, p <.001). In agreement with predictions, trying to experience positive emotions was positively associated with subjective happiness and trying to experience negative emotions was positively associated with depressive symptoms. In essence, these two valenced mood states seem to be sustained by trying to experience similarly valenced emotions. However, the three other significant paths in the model indicated that emotional motives play a complicated role in supporting both positive and negative mood states. For subjective happiness, the GERM motive of not trying to avoid positive emotions, i.e., not avoiding happiness, was also a strong predictor of happiness. Thus, people who report being the happiest are those individuals who not only strive to experience positive emotions but also are not ambivalent about them. The pattern for depressive symptoms was more nuanced. Individuals who reported higher depressive symptoms also reported higher levels of avoiding positive emotions and trying to experience negative emotions. The strongest predictor for depressed mood was avoiding positive emotions, which would suggest that happiness aversion or fear of happiness is an important motive for dysphoric people. Future work should determine whether these patterns of association are similar among clinically depressed people, and longitudinal data are needed to determine temporal relationships between motives and mood states.

Keywords: emotions motives, depression, subjective happiness, path model

Procedia PDF Downloads 169

Authors: Sanjay K. Behera, Debasish Saha, Paramesh Gadige, Ranjini Bandyopadhyay

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The zero shear viscosity (η₀) of a suspension of hard sphere colloids characterized by a significant polydispersity (≈10%) increases with increase in volume fraction (ϕ) and shows a dramatic increase at ϕ=ϕg with the system entering a colloidal glassy state. Fragility which is the measure of the rapidity of approach of these suspensions towards the glassy state is sensitive to its size polydispersity and stiffness of the particles. Soft poly(N-isopropylacrylamide) (PNIPAM) particles deform in the presence of neighboring particles at volume fraction above the random close packing volume fraction of undeformed monodisperse spheres. Softness, therefore, enhances the packing efficiency of these particles. In this study PNIPAM particles of a nearly constant swelling ratio and with polydispersities varying over a wide range (7.4%-48.9%) are synthesized to study the effects of polydispersity on the dynamics of suspensions of soft PNIPAM colloidal particles. The size and polydispersity of these particles are characterized using dynamic light scattering (DLS) and scanning electron microscopy (SEM). As these particles are deformable, their packing in aqueous suspensions is quantified in terms of effective volume fraction (ϕeff). The zero shear viscosity (η₀) data of these colloidal suspensions, estimated from rheometric experiments as a function of the effective volume fraction ϕeff of the suspensions, increases with increase in ϕeff and shows a dramatic increase at ϕeff = ϕ₀. The data for η₀ as a function of ϕeff fits well to the Vogel-Fulcher-Tammann equation. It is observed that increasing polydispersity results in increasingly fragile supercooled liquid-like behavior, with the parameter ϕ₀, extracted from the fits to the VFT equation shifting towards higher ϕeff. The observed increase in fragility is attributed to the prevalence of dynamical heterogeneities (DHs) in these polydisperse suspensions, while the simultaneous shift in ϕ₀ is ascribed to the decoupling of the dynamics of the smallest and largest particles. Finally, it is observed that the intrinsic nonlinearity of these suspensions, estimated at the third harmonic near ϕ₀ in Fourier transform oscillatory rheological experiments, increases with increase in polydispersity. These results are in agreement with theoretical predictions and simulation results for polydisperse hard sphere colloidal glasses and clearly demonstrate that jammed suspensions of polydisperse colloidal particles can be effectively fluidized with increasing polydispersity. Suspensions of these particles are therefore excellent candidates for detailed experimental studies of the effects of polydispersity on the dynamics of glass formation.

Keywords: dynamical heterogeneity, effective volume fraction, fragility, intrinsic nonlinearity

Procedia PDF Downloads 139

Authors: Laura Micheli, Majd Hijazi, Mahmoud Faytarouni

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The damage reported by oil and gas industrial facilities revealed the utmost vulnerability of steel liquid storage tanks to seismic events. The failure of steel storage tanks may yield devastating and long-lasting consequences on built and natural environments, including the release of hazardous substances, uncontrolled fires, and soil contamination with hazardous materials. It is, therefore, fundamental to reliably predict the damage that steel liquid storage tanks will likely experience under future seismic hazard events. The seismic performance of steel liquid storage tanks is usually assessed using vulnerability curves obtained from the numerical simulation of a tank under different hazard scenarios. However, the computational demand of high-fidelity numerical simulation models, such as finite element models, makes the vulnerability assessment of liquid storage tanks time-consuming and often impractical. As a solution, this paper presents a surrogate model-based strategy for predicting seismic-induced damage in steel liquid storage tanks. In the proposed strategy, the surrogate model is leveraged to reduce the computational demand of time-consuming numerical simulations. To create the data set for training the surrogate model, field damage data from past earthquakes reconnaissance surveys and reports are collected. Features representative of steel liquid storage tank characteristics (e.g., diameter, height, liquid level, yielding stress) and seismic excitation parameters (e.g., peak ground acceleration, magnitude) are extracted from the field damage data. The collected data are then utilized to train a surrogate model that maps the relationship between tank characteristics, seismic hazard parameters, and seismic-induced damage via a data-driven surrogate model. Different types of surrogate algorithms, including naïve Bayes, k-nearest neighbors, decision tree, and random forest, are investigated, and results in terms of accuracy are reported. The model that yields the most accurate predictions is employed to predict future damage as a function of tank characteristics and seismic hazard intensity level. Results show that the proposed approach can be used to estimate the extent of damage in steel liquid storage tanks, where the use of data-driven surrogates represents a viable alternative to computationally expensive numerical simulation models.

Keywords: damage prediction , data-driven model, seismic performance, steel liquid storage tanks, surrogate model

Procedia PDF Downloads 123

Authors: W. Meron Mebrahtu, R. Absi

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Velocity distribution in turbulent open-channel flows is organized in a complex manner. This is due to the large spatial and temporal variability of fluid motion resulting from the free-surface turbulent flow condition. This phenomenon is complicated further due to the complex geometry of channels and the presence of solids transported. Thus, several efforts were made to understand the phenomenon and obtain accurate mathematical models that are suitable for engineering applications. However, predictions are inaccurate because oversimplified assumptions are involved in modeling this complex phenomenon. Therefore, the aim of this work is to study velocity distribution profiles and obtain simple, more accurate, and predictive mathematical models. Particular focus will be made on the acceptable simplification of the general transport equations and an accurate representation of eddy viscosity. Wide rectangular open-channel seems suitable to begin the study; other assumptions are smooth-wall, and sediment-free flow under steady and uniform flow conditions. These assumptions will allow examining the effect of the bottom wall and the free surface only, which is a necessary step before dealing with more complex flow scenarios. For this flow condition, two ordinary differential equations are obtained for velocity profiles; from the Reynolds-averaged Navier-Stokes (RANS) equation and equilibrium consideration between turbulent kinetic energy (TKE) production and dissipation. Then different analytic models for eddy viscosity, TKE, and mixing length were assessed. Computation results for velocity profiles were compared to experimental data for different flow conditions and the well-known linear, log, and log-wake laws. Results show that the model based on the RANS equation provides more accurate velocity profiles. In the viscous sublayer and buffer layer, the method based on Prandtl’s eddy viscosity model and Van Driest mixing length give a more precise result. For the log layer and outer region, a mixing length equation derived from Von Karman’s similarity hypothesis provides the best agreement with measured data except near the free surface where an additional correction based on a damping function for eddy viscosity is used. This method allows more accurate velocity profiles with the same value of the damping coefficient that is valid under different flow conditions. This work continues with investigating narrow channels, complex geometries, and the effect of solids transported in sewers.

Keywords: accuracy, eddy viscosity, sewers, velocity profile

Procedia PDF Downloads 83

Authors: Ciara Pienaar, J. W. Odendaal, J. Joubert, J. C. Smit

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Radar cross section (RCS) of dielectric objects play an important role in many applications, such as low observability technology development, drone detection, and monitoring as well as coastal surveillance. Various materials are used to construct the targets of interest such as metal, wood, composite materials, radar absorbent materials, and other dielectrics. Since simulated datasets are increasingly being used to supplement infield measurements, as it is more cost effective and a larger variety of targets can be simulated, it is important to have a high level of confidence in the predicted results. Confidence can be attained through validation. Various computational electromagnetic (CEM) methods are capable of predicting the RCS of dielectric targets. This study will extend previous studies by validating full-wave and asymptotic RCS simulations of dielectric targets with measured data. The paper will provide measured RCS data of a number of canonical dielectric targets exhibiting different material properties. As stated previously, these measurements are used to validate numerous CEM methods. The dielectric properties are accurately characterized to reduce the uncertainties in the simulations. Finally, an analysis of the sensitivity of oblique and normal incidence scattering predictions to material characteristics is also presented. In this paper, the ability of several CEM methods, including method of moments (MoM), and physical optics (PO), to calculate the RCS of dielectrics were validated with measured data. A few dielectrics, exhibiting different material properties, were selected and several canonical targets, such as flat plates and cylinders, were manufactured. The RCS of these dielectric targets were measured in a compact range at the University of Pretoria, South Africa, over a frequency range of 2 to 18 GHz and a 360° azimuth angle sweep. This study also investigated the effect of slight variations in the material properties on the calculated RCS results, by varying the material properties within a realistic tolerance range and comparing the calculated RCS results. Interesting measured and simulated results have been obtained. Large discrepancies were observed between the different methods as well as the measured data. It was also observed that the accuracy of the RCS data of the dielectrics can be frequency and angle dependent. The simulated RCS for some of these materials also exhibit high sensitivity to variations in the material properties. Comparison graphs between the measured and simulation RCS datasets will be presented and the validation thereof will be discussed. Finally, the effect that small tolerances in the material properties have on the calculated RCS results will be shown. Thus the importance of accurate dielectric material properties for validation purposes will be discussed.

Keywords: asymptotic, CEM, dielectric scattering, full-wave, measurements, radar cross section, validation

Procedia PDF Downloads 218

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

Procedia PDF Downloads 214

Authors: Roberta Martino, Viviana Ventre

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From a theoretical point of view, the relationship between the risk associated with an investment and the expected value are directly proportional, in the sense that the market allows a greater result to those who are willing to take a greater risk. However, empirical evidence proves that this relationship is distorted in the minds of investors and is perceived exactly the opposite. To deepen and understand the discrepancy between the actual actions of the investor and the theoretical predictions, this paper analyzes the essential parameters used for the valuation of financial assets with greater attention to two elements: probability and the passage of time. Although these may seem at first glance to be two distinct elements, they are closely related. In particular, the error in the theoretical description of the relationship between risk and return lies in the failure to consider the impatience that is generated in the decision-maker when events that have not yet happened occur in the decision-making context. In this context, probability loses its objective meaning and in relation to the psychological aspects of the investor, it can only be understood as the degree of confidence that the investor has in the occurrence or non-occurrence of an event. Moreover, the concept of objective probability does not consider the inter-temporality that characterizes financial activities and does not consider the condition of limited cognitive capacity of the decision maker. Cognitive psychology has made it possible to understand that the mind acts with a compromise between quality and effort when faced with very complex choices. To evaluate an event that has not yet happened, it is necessary to imagine that it happens in your head. This projection into the future requires a cognitive effort and is what differentiates choices under conditions of risk and choices under conditions of uncertainty. In fact, since the receipt of the outcome in choices under risk conditions is imminent, the mechanism of self-projection into the future is not necessary to imagine the consequence of the choice and the decision makers dwell on the objective analysis of possibilities. Financial activities, on the other hand, develop over time and the objective probability is too static to consider the anticipatory emotions that the self-projection mechanism generates in the investor. Assuming that uncertainty is inherent in valuations of events that have not yet occurred, the focus must shift from risk management to uncertainty management. Only in this way the intertemporal dimension of the decision-making environment and the haste generated by the financial market can be cautioned and considered. The work considers an extension of the prospectus theory with the temporal component with the aim of providing a description of the attitude towards risk with respect to the passage of time.

Keywords: impatience, risk aversion, subjective probability, uncertainty

Procedia PDF Downloads 82

Authors: Anuruddha Jayasuriya, Thanakorn Pheeraphan

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In this paper, a review of different mathematical models which can be used as prediction tools to assess the time to crack reinforced concrete (RC) due to corrosion is investigated. This investigation leads to an experimental study to validate a selected prediction model. Most of these mathematical models depend upon the mechanical behaviors, chemical behaviors, electrochemical behaviors or geometric aspects of the RC members during a corrosion process. The experimental program is designed to verify the accuracy of a well-selected mathematical model from a rigorous literature study. Fundamentally, the experimental program exemplifies both one-dimensional chloride diffusion using RC squared slab elements of 500 mm by 500 mm and two-dimensional chloride diffusion using RC squared column elements of 225 mm by 225 mm by 500 mm. Each set consists of three water-to-cement ratios (w/c); 0.4, 0.5, 0.6 and two cover depths; 25 mm and 50 mm. 12 mm bars are used for column elements and 16 mm bars are used for slab elements. All the samples are subjected to accelerated chloride corrosion in a chloride bath of 5% (w/w) sodium chloride (NaCl) solution. Based on a pre-screening of different models, it is clear that the well-selected mathematical model had included mechanical properties, chemical and electrochemical properties, nature of corrosion whether it is accelerated or natural, and the amount of porous area that rust products can accommodate before exerting expansive pressure on the surrounding concrete. The experimental results have shown that the selected model for both one-dimensional and two-dimensional chloride diffusion had ±20% and ±10% respective accuracies compared to the experimental output. The half-cell potential readings are also used to see the corrosion probability, and experimental results have shown that the mass loss is proportional to the negative half-cell potential readings that are obtained. Additionally, a statistical analysis is carried out in order to determine the most influential factor that affects the time to corrode the reinforcement in the concrete due to chloride diffusion. The factors considered for this analysis are w/c, bar diameter, and cover depth. The analysis is accomplished by using Minitab statistical software, and it showed that cover depth is the significant effect on the time to crack the concrete from chloride induced corrosion than other factors considered. Thus, the time predictions can be illustrated through the selected mathematical model as it covers a wide range of factors affecting the corrosion process, and it can be used to predetermine the durability concern of RC structures that are vulnerable to chloride exposure. And eventually, it is further concluded that cover thickness plays a vital role in durability in terms of chloride diffusion.

Keywords: accelerated corrosion, chloride diffusion, corrosion cracks, passivation layer, reinforcement corrosion

Procedia PDF Downloads 188

Authors: Mark Ashton Newman, Richard Blagrove, Jonathan Folland

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The force-velocity profile of an individual has been shown to influence success in ballistic movements, independent of the individuals' maximal power output; therefore, effective and accurate evaluation of an individual’s F-V characteristics and not solely maximal power output is important. The relatively narrow range of loads typically utilised during force-velocity profiling protocols due to the difficulty in obtaining force data at high velocities may bring into question the accuracy of the F-V slope along with predictions pertaining to the maximum force that the system can produce at a velocity of null (F₀) and the theoretical maximum velocity against no load (V₀). As such, the reliability of the slope of the force-velocity profile, as well as V₀, has been shown to be relatively poor in comparison to F₀ and maximal power, and it has been recommended to assess velocity at loads closer to both F₀ and V₀. The aim of the present study was to assess the relative and absolute reliability of an instrumented novel leg press machine which enables the assessment of force and velocity data at loads equivalent to ≤ 10% of one repetition maximum (1RM) through to 1RM during a ballistic leg press movement. The reliability of maximal and mean force, velocity, and power, as well as the respective force-velocity and power-velocity relationships and the linearity of the force-velocity relationship, were evaluated. Sixteen male strength-trained individuals (23.6 ± 4.1 years; 177.1 ± 7.0 cm; 80.0 ± 10.8 kg) attended four sessions; during the initial visit, participants were familiarised with the leg press, modified to include a mounted force plate (Type SP3949, Force Logic, Berkshire, UK) and a Micro-Epsilon WDS-2500-P96 linear positional transducer (LPT) (Micro-Epsilon, Merseyside, UK). Peak isometric force (IsoMax) and a dynamic 1RM, both from a starting position of 81% leg length, were recorded for the dominant leg. Visits two to four saw the participants carry out the leg press movement at loads equivalent to ≤ 10%, 30%, 50%, 70%, and 90% 1RM. IsoMax was recorded during each testing visit prior to dynamic F-V profiling repetitions. The novel leg press machine used in the present study appears to be a reliable tool for measuring F and V-related variables across a range of loads, including velocities closer to V₀ when compared to some of the findings within the published literature. Both linear and polynomial models demonstrated good to excellent levels of reliability for SFV and F₀ respectively, with reliability for V₀ being good using a linear model but poor using a 2nd order polynomial model. As such, a polynomial regression model may be most appropriate when using a similar unilateral leg press setup to predict maximal force production capabilities due to only a 5% difference between F₀ and obtained IsoMax values with a linear model being best suited to predict V₀.

Keywords: force-velocity, leg-press, power-velocity, profiling, reliability

Procedia PDF Downloads 25

Authors: Ahmed Borg, Elsa Aristodemou, Attia Attia

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Well control involves managing the circulating drilling fluid within the wells and avoiding kicks and blowouts as these can lead to losses in human life and drilling facilities. Current practices for good control incorporate predictions of pressure losses through computational models. Developing a realistic hydraulic model for a good control problem is a very complicated process due to the existence of a complex multiphase region, which usually contains a non-Newtonian drilling fluid and the miscibility of formation gas in drilling fluid. The current approaches assume an inaccurate flow fluid model within the well, which leads to incorrect pressure loss calculations. To overcome this problem, researchers have been considering the more complex two-phase fluid flow models. However, even these more sophisticated two-phase models are unsuitable for applications where pressure dynamics are important, such as in managed pressure drilling. This study aims to develop and implement new fluid flow models that take into consideration the miscibility of fluids as well as their non-Newtonian properties for enabling realistic kick treatment. furthermore, a corresponding numerical solution method is built with an enriched data bank. The research work considers and implements models that take into consideration the effect of two phases in kick treatment for well control in conventional drilling. In this work, a corresponding numerical solution method is built with an enriched data bank. Software STARCCM+ for the computational studies to study the important parameters to describe wellbore multiphase flow, the mass flow rate, volumetric fraction, and velocity of each phase. Results showed that based on the analysis of these simulation studies, a coarser full-scale model of the wellbore, including chemical modeling established. The focus of the investigations was put on the near drill bit section. This inflow area shows certain characteristics that are dominated by the inflow conditions of the gas as well as by the configuration of the mud stream entering the annulus. Without considering the gas solubility effect, the bottom hole pressure could be underestimated by 4.2%, while the bottom hole temperature is overestimated by 3.2%. and without considering the heat transfer effect, the bottom hole pressure could be overestimated by 11.4% under steady flow conditions. Besides, larger reservoir pressure leads to a larger gas fraction in the wellbore. However, reservoir pressure has a minor effect on the steady wellbore temperature. Also as choke pressure increases, less gas will exist in the annulus in the form of free gas.

Keywords: multiphase flow, well- control, STARCCM+, petroleum engineering and gas technology, computational fluid dynamic

Procedia PDF Downloads 91

Authors: Martin Pellon Consunji

Abstract:

Due to the rise in popularity of Bitcoin and other crypto assets as a store of wealth and speculative investment, there is an ever-growing demand for automated trading tools, such as bots, in order to gain an advantage over the market. Traditionally, trading in the stock market was done by professionals with years of training who understood patterns and exploited market opportunities in order to gain a profit. However, nowadays a larger portion of market participants are at minimum aided by market-data processing bots, which can generally generate more stable signals than the average human trader. The rise in trading bot usage can be accredited to the inherent advantages that bots have over humans in terms of processing large amounts of data, lack of emotions of fear or greed, and predicting market prices using past data and artificial intelligence, hence a growing number of approaches have been brought forward to tackle this task. However, the general limitation of these approaches can still be broken down to the fact that limited historical data doesn’t always determine the future, and that a lot of market participants are still human emotion-driven traders. Moreover, developing markets such as those of the cryptocurrency space have even less historical data to interpret than most other well-established markets. Due to this, some human traders have gone back to the tried-and-tested traditional technical analysis tools for exploiting market patterns and simplifying the broader spectrum of data that is involved in making market predictions. This paper proposes a method which uses neuro evolution techniques on both sentimental data and, the more traditionally human-consumed, technical analysis data in order to gain a more accurate forecast of future market behavior and account for the way both automated bots and human traders affect the market prices of Bitcoin and other cryptocurrencies. This study’s approach uses evolutionary algorithms to automatically develop increasingly improved populations of bots which, by using the latest inflows of market analysis and sentimental data, evolve to efficiently predict future market price movements. The effectiveness of the approach is validated by testing the system in a simulated historical trading scenario, a real Bitcoin market live trading scenario, and testing its robustness in other cryptocurrency and stock market scenarios. Experimental results during a 30-day period show that this method outperformed the buy and hold strategy by over 260% in terms of net profits, even when taking into consideration standard trading fees.

Keywords: neuro-evolution, Bitcoin, trading bots, artificial neural networks, technical analysis, evolutionary algorithms

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