Search results for: crack growth prediction

Authors: Shobha U. Jadhav, R. S. Saler

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Onion is most Important Vegetable crop grown throughout the world. Onion suffers from pest and fungal diseases but the fungicides cause pollution and disturb microbial balance of soil. Under integrated fungal disease management programme cost effective and eco- friendly component like plant extract are used to control plant pathogens. Alternaria porri, Fusarium oxysporium, Stemphylium vesicarium are soil borne pathogens of onion. Effect of three different plant extract (Datura metel, Pongamia pinnata, Ipomoea palmata) at five different concentration Viz, 10,25,50,75 and 100 percentage on these pathogens was studied by food poisoning techniquie. Detura metal gave 94.73% growth of Alternaria porri at 10% extract concentraton and 26.31% growth in 100% extract concentration. As compared to Fusarium oxysporium, and Stemphylium vesicarium, Alternaria porri give good inhibitory response. In Pongamia pinnata L. at 10% extract concentration 84.21% growth and at 100% extract concentration 36.84% growth of Stemphylium vesicarium was observed. Stemphylium vesicarium give good in inhibitory response as compared to Alternaria porri and Fusarium oxysporium. Ipomoea palmata in 10% extract concentration 92% growth and in 100% extract concentration 40% growth of Fusarium oxysporium was recorded. Fusarium oxysporium give good inhibitory response as compared to Alternaria porri and, Stemphylium vesicarium.

Keywords: pathogen, onion, plant extract, Allium cepa L.

Procedia PDF Downloads 446

Authors: H. Barati, M. Wu, A. Kharicha, A. Ludwig

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A transient model for nozzle clogging has been developed and successfully validated against a laboratory experiment. Key steps of clogging are considered: transport of particles by turbulent flow towards the nozzle wall; interactions between fluid flow and nozzle wall, and the adhesion of the particle on the wall; the growth of the clog layer and its interaction with the flow. The current paper is to investigate the mesh (size and type) sensitivity of the model in both two and three dimensions. It is found that the algorithm for clog growth alone excluding the flow effect is insensitive to the mesh type and size, but the calculation including flow becomes sensitive to the mesh quality. The use of 2D meshes leads to overestimation of the clog growth because the 3D nature of flow in the boundary layer cannot be properly solved by 2D calculation. 3D simulation with tetrahedron mesh can also lead to an error estimation of the clog growth. A mesh-independent result can be achieved with hexahedral mesh, or at least with triangular prism (inflation layer) for near-wall regions.

Keywords: clogging, continuous casting, inclusion, simulation, submerged entry nozzle

Procedia PDF Downloads 266

Authors: S. B. Q. Tran

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Drop condensation is the phenomenon that the tiny drops form when the oversaturated vapour present in the environment condenses on a substrate and makes the droplet growth. Recently, this subject has received much attention due to its applications in many fields such as thin film growth, heat transfer, recovery of atmospheric water and polymer templating. In literature, many papers investigated theoretically and experimentally in macro droplet growth with the size of millimeter scale of radius. However few papers about nanodroplet condensation are found in the literature especially theoretical work. In order to understand the droplet growth in nanoscale, we perform the numerical simulation work to study nanodroplet growth. We investigate and discuss the role of the droplet shape and monomer diffusion on drop growth and their effect on growth law. The effect of droplet shape is studied by doing parametric studies of contact angle and disjoining pressure magnitude. Besides, the effect of pinning and de-pinning behaviours is also studied. We investigate the axisymmetric homogeneous growth of 10–100 nm single water nanodroplet on a substrate surface. The main mechanism of droplet growth is attributed to the accumulation of laterally diffusing water monomers, formed by the absorption of water vapour in the environment onto the substrate. Under assumptions of quasi-steady thermodynamic equilibrium, the nanodroplet evolves according to the augmented Young–Laplace equation. Using continuum theory, we model the dynamics of nanodroplet growth including the coupled effects of disjoining pressure, contact angle and monomer diffusion with the assumption of constant flux of water monomers at the far field. The simulation result is validated by comparing with the published experimental result. For the case of nanodroplet growth with constant contact angle, our numerical results show that the initial droplet growth is transient by monomer diffusion. When the flux at the far field is small, at the beginning, the droplet grows by the diffusion of initially available water monomers on the substrate and after that by the flux at the far field. In the steady late growth rate of droplet radius and droplet height follow a power law of 1/3, which is unaffected by the substrate disjoining pressure and contact angle. However, it is found that the droplet grows faster in radial direction than high direction when disjoining pressure and contact angle increase. The simulation also shows the information of computational domain effect in the transient growth period. When the computational domain size is larger, the mass coming in the free substrate domain is higher. So the mass coming in the droplet is also higher. The droplet grows and reaches the steady state faster. For the case of pinning and de-pinning droplet growth, the simulation shows that the disjoining pressure does not affect the droplet radius growth law 1/3 in steady state. However the disjoining pressure modifies the growth rate of the droplet height, which then follows a power law of 1/4. We demonstrate how spatial depletion of monomers could lead to a growth arrest of the nanodroplet, as observed experimentally.

Keywords: augmented young-laplace equation, contact angle, disjoining pressure, nanodroplet growth

Procedia PDF Downloads 253

Authors: Ahmed Sakr Ashour, Hoda Saad AboRemila

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An attempt was made to fill the gap in the macro analysis of corruption by suggesting a conceptual framework that differentiates four types of macro corruption: state capture, political, bureaucratic and financial/corporate. The economic consequences or forward linkages (growth, inclusiveness and sustainability of development) and macro institutional determinants constituting the backward linkages of each type were delineated. The research implications of the macro perspective and proposed framework were discussed. Implications of the findings for theory, research and reform policies addressing macro corruption issues were discussed.

Keywords: economic growth, inclusive growth, macro corruption, sustainable development

Procedia PDF Downloads 161

Authors: Farzad Doostishoar, Abdolreza Hasanzadeh, Seyed Amin Ayatolahi Mousavi

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Introduction and Goals: Deuterium has different action from its isotopes hydrogen in chemical reactions and biochemical processes. It is not a significant difference in heavier atoms between the behavior of heavier isotope and the lighter One but for very lighter atoms it is significant . According to that most of the weight of all creatures body is water natural rate can be significant. In this article we want to study the effect of reduced deuterium on the fungus cell. If we saw the dependence of deuterium concentration of environment on the cells growth we can test this in invivo models too. Methods: First we measured deuterium concentration of the distillated water this analyze was operated by Arak’s heavy water company. Then the deuterium was diluted to ½ ¼ 1/8 1/16 by adding water free of deuterium for making media. In tree of samples the deuterium concentration was increased by adding D2O up to 10,50,100 times more concentrated. For candida albicans growth we used sabor medium and for aspergillus fomigatis growth we used sabor medium containing chloramphenicol. After culturing the funguses species we put the mediums for each species in the shaker incubator for 10 days in 25 centigrade. In different days and times the plates were studied morphologically and some microscopic characteristics were studied too. This experiments and cultures were repeated 3 times. Results: Statistical analyzes by paired-sample T test showed that aspergilus fomigatoos growth was decreased in concentration of 72 ppm( half deuterium concentration of negative control) significantly. In deuterium concentration reduction the growth reduce into the negative control significantly. The project results showed that candida albicans was sensitive to reduce and decrease of the deuterium in all concentrations.

Keywords: deuterium, cancer cell, growth, candida albicans

Procedia PDF Downloads 385

Authors: Amirreza Attarzadeh

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This paper aims to investigate the relationship between economic variables, e.g., inflation rate, interest rate, trade openness and the growth rate of GDP, with domestic investment. The present study also draws on conceptual economy related theories to verify the negative effect of interest rates on domestic investment. However, trade openness and growth rate had a positive correlation, and the inflation rate may have a positive or negative impact on domestic investment.

Keywords: inflation rate, growth rate of GDP, interest rate and trade openness, domestic investment

Procedia PDF Downloads 390

Authors: Caihong Sun, Xizi Zhang

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Rating prediction is an important problem for recommender systems. The task is to predict the rating for an item that a user would give. Most of the existing algorithms for the task ignore the effect of negative ratings rated by users on items, but the negative ratings have a significant impact on users’ purchasing decisions in practice. In this paper, we present a rating prediction algorithm based on factorization machines that consider the effect of negative ratings inspired by Loss Aversion theory. The aim of this paper is to develop a concave and a convex negative disgust function to evaluate the negative ratings respectively. Experiments are conducted on MovieLens dataset. The experimental results demonstrate the effectiveness of the proposed methods by comparing with other four the state-of-the-art approaches. The negative ratings showed much importance in the accuracy of ratings predictions.

Keywords: factorization machines, feature engineering, negative ratings, recommendation systems

Procedia PDF Downloads 222

Authors: Sabuj Kanti Mazumder, Mazlan Abd Ghaffar, Simon Kumar Das

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In this study, we analyzed the effects of water temperature and diet on the growth properties and gastric emptying period of juvenile Malabar blood snapper (Lutjanus malabaricus) over a 30day experimental period. Fish were collected from a local hatchery of Pulau Ketam, Selangor, Malaysia and immediately transferred to flow-through sea water system and subjected to four different temperatures (22, 26, 30, and 34 °C) and two diets (formulated pellet and shrimp). Body weight gain, food consumption, food conversion ratio, food consumption efficiency, specific growth rate, relative growth rate, daily growth rate, and gastric emptying period were significantly influenced by temperature and diet (P<0.05). The best food conversion ratio was with the shrimp group recorded at 30°C (1.33±0.08). The highest growth rate was observed in the shrimp group at 30°C (3.97±0.57% day-1), and the lowest was observed in the formulated pellet group at 22°C (1.63±0.29% day-1). No significant difference was observed between the groups subjected to temperatures of 26 and 30°C. Similarly, the lowest gastric emptying period was detected in the shrimp group at 30°C (16h), where the proportion of meal residues in the stomach decreased from 100% to less than 8% after 12h of starvation. A significantly longer gastric emptying period was observed in the formulated pellet group at 22°C (28h). Overall, the best results were observed on shrimp group subjected to a 30°C temperature. The data obtained from this study suggest that a shrimp diet fed on L. malabaricus at 30°C will optimize the commercial production of this commercially important fish species.

Keywords: aquaculture, diet, digestion rate, growth, Malabar blood snapper

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Authors: Jacqueline T. Madaure, Phatu W. Mashela

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Cucurbitacin-containing phytonematicides consistently suppress root-knot (Meloidogyne species) nematode population densities. However, the impact of these products on nematode biocontrol agents is not documented. The objective of this study was to determine the influence of Nemarioc-AL and Nemafric-BL phytonematicides on growth of Trichoderma harzianum under in vitro conditions. The two phytonematicides were separately prepared to concentrations of 3% and used in poison plate assays. After exposure at different times from 0 to 72 h, there was 100% mycelial growth of T. harzianum. In conclusion, at the recommended concentrations of phytonematicides used in managing nematode population densities, there was no evidence of suppressive effects on growth of T. harzianum by the two phytonematicides.

Keywords: botanicals, crude extracts, cucumis africanus, cucumis myriocarpus, cucurbitacin a, cucurbitacin b, ethnomedicinal plants

Procedia PDF Downloads 196

Authors: Mohammed T. Hayajneh

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Machining of Metal Matrix Composites (MMCs) is very significant process and has been a main problem that draws many researchers to investigate the characteristics of MMCs during different machining process. The poor machining properties of hard particles reinforced MMCs make drilling process a rather interesting task. Unlike drilling of conventional materials, many problems can be seriously encountered during drilling of MMCs, such as tool wear and cutting forces. Cutting tool wear is a very significant concern in industries. Cutting tool wear not only influences the quality of the drilled hole, but also affects the cutting tool life. Prediction the cutting tool life during drilling is essential for optimizing the cutting conditions. However, the relationship between tool life and cutting conditions, tool geometrical factors and workpiece material properties has not yet been established by any machining theory. In this research work, fuzzy subtractive clustering system has been used to model the cutting tool life in drilling of Al₂O₃ particle reinforced aluminum alloy composite to investigate of the effect of cutting conditions on cutting tool life. This investigation can help in controlling and optimizing of cutting conditions when the process parameters are adjusted. The built model for prediction the tool life is identified by using drill diameter, cutting speed, and cutting feed rate as input data. The validity of the model was confirmed by the examinations under various cutting conditions. Experimental results have shown the efficiency of the model to predict cutting tool life.

Keywords: composite, fuzzy, tool life, wear

Procedia PDF Downloads 274

Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

Procedia PDF Downloads 24

Authors: R. Burahee, B. Chassinat, T. de Laclos, A. Dépée, A. Sastim

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The purpose of this paper is to propose a solution to detect, predict and reconstitute rain drops in real time – during the night – using an embedded material with an infrared camera. To prevent the system from needing too high hardware resources, simple models are considered in a powerful image treatment algorithm reducing considerably calculation time in OpenCV software. Using a smart model – drops will be matched thanks to a process running through two consecutive pictures for implementing a sophisticated tracking system. With this system drops computed trajectory gives information for predicting their future location. Thanks to this technique, treatment part can be reduced. The hardware system composed by a Raspberry Pi is optimized to host efficiently this code for real time execution.

Keywords: reconstitution, prediction, detection, rain drop, real time, raspberry, infrared

Procedia PDF Downloads 394

Authors: Retno Ambarwati Sigit Lestari

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Microalga is one of the organisms that can be considered ideal and potential for raw material of bioenergy production, because the content of lipids in microalga is relatively high. Microalga is an aquatic organism that produces complex organic compounds from inorganic molecules using carbon dioxide as a carbon source, and sunlight for energy supply. Microalga-CO₂ fixation has potential advantages over other carbon captures and storage approaches, such as wide distribution, high photosynthetic rate, good environmental adaptability, and ease of operation. The rates of growth and CO₂ capture of microalga are influenced by CO₂ concentration and light intensity. This study quantitatively investigates the effects of CO₂ concentration on the rates of growth and CO₂ capture of a type of microalga, cultivated in bioreactors. The works include laboratory experiments as well as mathematical modelling. The mathematical models were solved numerically and the accuracy of the model was tested by the experimental data. It turned out that the mathematical model proposed can well quantitatively describe the growth and CO₂ capture of microalga, in which the effects of CO₂ concentration can be observed.

Keywords: Microalga, CO2 concentration, photobioreactor, mathematical model

Procedia PDF Downloads 110

Authors: Marianna Maiaru, Gregory M. Odegard

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During the processing of high-performance thermoset polymer matrix composites, chemical reactions occur during elevated pressure and temperature cycles, causing the constituent monomers to crosslink and form a molecular network that gradually can sustain stress. As the crosslinking process progresses, the material naturally experiences a gradual shrinkage due to the increase in covalent bonds in the network. Once the cured composite completes the cure cycle and is brought to room temperature, the thermal expansion mismatch of the fibers and matrix cause additional residual stresses to form. These compounded residual stresses can compromise the reliability of the composite material and affect the composite strength. Composite process modeling is greatly complicated by the multiscale nature of the composite architecture. At the molecular level, the degree of cure controls the local shrinkage and thermal-mechanical properties of the thermoset. At the microscopic level, the local fiber architecture and packing affect the magnitudes and locations of residual stress concentrations. At the macroscopic level, the layup sequence controls the nature of crack initiation and propagation due to residual stresses. The goal of this research is use molecular dynamics (MD) and finite element analysis (FEA) to predict the residual stresses in composite laminates and the corresponding effect on composite failure. MD is used to predict the polymer shrinkage and thermomechanical properties as a function of degree of cure. This information is used as input into FEA to predict the residual stresses on the microscopic level resulting from the complete cure process. Virtual testing is subsequently conducted to predict strength allowables. Experimental characterization is used to validate the modeling.

Keywords: molecular dynamics, finite element analysis, processing modeling, multiscale modeling

Procedia PDF Downloads 77

Authors: J. K. Alhassan, B. Attah, S. Misra

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Human beings have the ability to make logical decisions. Although human decision - making is often optimal, it is insufficient when huge amount of data is to be classified. medical dataset is a vital ingredient used in predicting patients health condition. In other to have the best prediction, there calls for most suitable machine learning algorithms. This work compared the performance of Artificial Neural Network (ANN) and Decision Tree Algorithms (DTA) as regards to some performance metrics using diabetes data. The evaluations was done using weka software and found out that DTA performed better than ANN. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) were the two algorithms used for ANN, while RegTree and LADTree algorithms were the DTA models used. The Root Mean Squared Error (RMSE) of MLP is 0.3913,that of RBF is 0.3625, that of RepTree is 0.3174 and that of LADTree is 0.3206 respectively.

Keywords: artificial neural network, classification, decision tree algorithms, diabetes mellitus

Procedia PDF Downloads 389

Authors: Ioannis P. Panapakidis, Marios N. Moschakis

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The Greek Energy Market is structured as a mandatory pool where the producers make their bid offers in day-ahead basis. The System Operator solves an optimization routine aiming at the minimization of the cost of produced electricity. The solution of the optimization problem leads to the calculation of the System Marginal Price (SMP). Accurate forecasts of the SMP can lead to increased profits and more efficient portfolio management from the producer`s perspective. Aim of this study is to provide a comparative analysis of various machine learning models such as artificial neural networks and neuro-fuzzy models for the prediction of the SMP of the Greek market. Machine learning algorithms are favored in predictions problems since they can capture and simulate the volatilities of complex time series.

Keywords: deregulated energy market, forecasting, machine learning, system marginal price

Procedia PDF Downloads 190

Authors: Doaa H. I. Mahmoud, El-Said M. Elsharkawy, Saad Z. Soliman, Soher E. Mustfa

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The opening of new promising foreign markets is one of the objectives of Egypt’s foreign trade policies, especially for agricultural exports. This study aims at the examination of the commodity structure of the Egyptian agricultural imports and exports with the COMESA countries. In addition, estimation of the surplus/deficit of the Egyptian commodities and agricultural balance with these countries is made. Time series data covering the period 2004-2016 is used. Estimation of the growth function along with the derivation of the annual growth rates of the study’s variables is made. Some of the results of the study period display the following: (1) The average total Egyptian exports to the COMESA (Common Market for Eastern and Southern Africa) countries is estimated at 1,491 million dollars, with an annual growth rate of 14.4% (214.7 million dollars). (2) The average annual Egyptian agricultural exports to these economies is estimated at 555 million dollars, with an annual growth rate of 19.4% (107.7 million dollars). (3) The average annual value of agricultural imports from the COMESA countries is set at 289 Million Dollars, with an annual growth rate of 14.4% (41.6 million dollars). (4) The study shows that there is a continuous surplus in the agricultural balance with these economies, whilst having a deficit in the raw-materials agricultural balance, as well as the balance of input requirements with these countries.

Keywords: COMESA, Egypt, growth rates, trade balance

Procedia PDF Downloads 185

Authors: Olabisi Badmos, Daniel Callo-Concha, Babatunde Agbola, Andreas Rienow, Klaus Greve, Carsten Jurgens

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Slums are important components of city development planning, especially in Africa where slum growth is on par with urban growth. Purposefully, our knowledge on the residential choice of slum dwellers, which contributes to population growth in slums, is limited. This is the case in Lagos, a megacity reportedly dominated by slum dwellers. Thus, this study aims to disclose the factors influencing the residential choices and causes of people to remain in Lagos slums. Data was collected through questionnaire administration and focus group discussions. Descriptive statistics were used to analyze and describe the factors influencing residential location choice; logistic regression was utilized to determine the extent to which the neighborhood and household attributes, influence slum dwellers decisions to remain in the slums. Results showed that movement to Lagos was the main cause of population growth in slums; most of the migrants were from closer geopolitical zones (in Nigeria). Further, the movement patterns observed support two theories of human mobility in slums: slum as a sink, and as a final destination. Also, the factors that brought most of the slum dwellers to the slums (cheap housing, proximity to work etc.) differs from the ones that made them stay (Gender, employment status, housing status etc.). This study concludes that residential choice and intention to stay are the major contributors to population growth in a slum. It is therefore important for Lagos state Government to incorporate these elements of residential choices of slum dwellers in their slum management policies if the city aims to be free of slums by 2030

Keywords: Lagos, population growth, residential decision choices, slum

Procedia PDF Downloads 150

Authors: Ismail Mahdi, Nidal Fahsi, Mohamed Hafidi, Abdelmounaim Allaoui, Latefa Biskri

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To meet the worldwide demand for food, smart management of arable lands is needed. This could be achieved through sustainable approaches such as the use of plant growth-promoting microorganisms including bacteria. Phosphate (P) solubilization is one of the major mechanisms of plant growth promotion by associated bacteria. In the present study, we isolated and screened 14 strains from the rhizosphere of Chenopodium quinoa wild grown in the experimental farm of UM6P and assessed their plant growth promoting properties. Next, they were identified by using 16S rRNA and Cpn60 genes sequencing as Bacillus, Pseudomonas and Enterobacter. These strains showed dispersed capacities to solubilize P (up to 346 mg L−1) following five days of incubation in NBRIP broth. We also assessed their abilities for indole acetic acid (IAA) production (up to 795,3 µg ml−1) and in vitro salt tolerance. Three Bacillus strains QA1, QA2, and S8 tolerated high salt stress induced by NaCl with a maximum tolerable concentration of 8%. Three performant isolates, QA1, S6 and QF11, were further selected for seed germination assay because of their pronounced abilities in terms of P solubilization, IAA production and salt tolerance. The early plant growth potential of tested strains showed that inoculated quinoa seeds displayed greater germination rate and higher seedlings growth under bacterial treatments. The positive effect on seed germination traits strongly suggests that the tested strains are growth promoting, halotolerant and P solubilizing bacteria which could be exploited as biofertilizers.

Keywords: phosphate solubilizing bacteria, IAA, Seed germination, salt tolerance, quinoa

Procedia PDF Downloads 111

Authors: Venencia Albert, Arulselvi Subramanian, Hara Prasad Pati, Asok K. Mukhophadhyay

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Acute traumatic coagulopathy in an endogenous dysregulation of the intrinsic coagulation system in response to the injury, associated with three-fold risk of poor outcome, and is more amenable to corrective interventions, subsequent to early identification and management. Multiple definitions for stratification of the patients' risk for early acute coagulopathy have been proposed, with considerable variations in the defining criteria, including several trauma-scoring systems based on prehospital data. We aimed to develop a clinically relevant definition for acute coagulopathy of trauma based on conventional coagulation assays and to assess its efficacy in comparison to recently established prehospital prediction models. Methodology: Retrospective data of all trauma patients (n = 490) presented to our level I trauma center, in 2014, was extracted. Receiver operating characteristic curve analysis was done to establish cut-offs for conventional coagulation assays for identification of patients with acute traumatic coagulopathy was done. Prospectively data of (n = 100) adult trauma patients was collected and cohort was stratified by the established definition and classified as "coagulopathic" or "non-coagulopathic" and correlated with the Prediction of acute coagulopathy of trauma score and Trauma-Induced Coagulopathy Clinical Score for identifying trauma coagulopathy and subsequent risk for mortality. Results: Data of 490 trauma patients (average age 31.85±9.04; 86.7% males) was extracted. 53.3% had head injury, 26.6% had fractures, 7.5% had chest and abdominal injury. Acute traumatic coagulopathy was defined as international normalized ratio ≥ 1.19; prothrombin time ≥ 15.5 s; activated partial thromboplastin time ≥ 29 s. Of the 100 adult trauma patients (average age 36.5±14.2; 94% males), 63% had early coagulopathy based on our conventional coagulation assay definition. Overall prediction of acute coagulopathy of trauma score was 118.7±58.5 and trauma-induced coagulopathy clinical score was 3(0-8). Both the scores were higher in coagulopathic than non-coagulopathic patients (prediction of acute coagulopathy of trauma score 123.2±8.3 vs. 110.9±6.8, p-value = 0.31; trauma-induced coagulopathy clinical score 4(3-8) vs. 3(0-8), p-value = 0.89), but not statistically significant. Overall mortality was 41%. Mortality rate was significantly higher in coagulopathic than non-coagulopathic patients (75.5% vs. 54.2%, p-value = 0.04). High prediction of acute coagulopathy of trauma score also significantly associated with mortality (134.2±9.95 vs. 107.8±6.82, p-value = 0.02), whereas trauma-induced coagulopathy clinical score did not vary be survivors and non-survivors. Conclusion: Early coagulopathy was seen in 63% of trauma patients, which was significantly associated with mortality. Acute traumatic coagulopathy defined by conventional coagulation assays (international normalized ratio ≥ 1.19; prothrombin time ≥ 15.5 s; activated partial thromboplastin time ≥ 29 s) demonstrated good ability to identify coagulopathy and subsequent mortality, in comparison to the prehospital parameter-based scoring systems. Prediction of acute coagulopathy of trauma score may be more suited for predicting mortality rather than early coagulopathy. In emergency trauma situations, where immediate corrective measures need to be taken, complex multivariable scoring algorithms may cause delay, whereas coagulation parameters and conventional coagulation tests will give highly specific results.

Keywords: trauma, coagulopathy, prediction, model

Procedia PDF Downloads 165

Authors: Wade Ghribi, Abdelmoty M. Ahmed, Ahmed Said Badawy, Belgacem Bouallegue

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In higher education institutions, the most pressing priority is to improve student performance and retention. Large volumes of student data are used in Educational Data Mining techniques to find new hidden information from students' learning behavior, particularly to uncover the early symptom of at-risk pupils. On the other hand, data with noise, outliers, and irrelevant information may provide incorrect conclusions. By identifying features of students' data that have the potential to improve performance prediction results, comparing and identifying the most appropriate ensemble learning technique after preprocessing the data, and optimizing the hyperparameters, this paper aims to develop a reliable students' performance prediction model for Higher Education Institutions. Data was gathered from two different systems: a student information system and an e-learning system for undergraduate students in the College of Computer Science of a Saudi Arabian State University. The cases of 4413 students were used in this article. The process includes data collection, data integration, data preprocessing (such as cleaning, normalization, and transformation), feature selection, pattern extraction, and, finally, model optimization and assessment. Random Forest, Bagging, Stacking, Majority Vote, and two types of Boosting techniques, AdaBoost and XGBoost, are ensemble learning approaches, whereas Decision Tree, Support Vector Machine, and Artificial Neural Network are supervised learning techniques. Hyperparameters for ensemble learning systems will be fine-tuned to provide enhanced performance and optimal output. The findings imply that combining features of students' behavior from e-learning and students' information systems using Majority Vote produced better outcomes than the other ensemble techniques.

Keywords: educational data mining, student performance prediction, e-learning, classification, ensemble learning, higher education

Procedia PDF Downloads 90

Authors: O. A. Babalola, A. F Adekunle, F. Oladeji, A. T. Osungbade, O. A. Akinlaja

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Optimizing microbiological activities in an organic crop production system is crucial to the realization of optimum growth and development of the crops. Field and pot experiments were conducted to assess soil microbial activities, growth and yield of tomato varieties in response to 4 rates of composted plant and animal residues. The compost rates were 0, 5, 10 and 20 t ha-1, and improved Ibadan and Ibadan local constituted the varieties. Fungi population, microbial biomass nitrogen, cellulase and proteinase activities were significantly higher (P≤ 0.05) at the rhizosphere of the local variety than that of improved variety. This led to a significantly higher number of branches, plant height, leaf area, number of fruits and less days to maturity in the local variety. Furthermore, compost-amended soil had significantly higher microbial populations, microbial biomass N, P and C, enzyme activities, soil N, P and organic carbon than control, but amendment of 20 t ha-1 gave significantly higher values than other compost rates. Consequently, growth parameters and tissue N significantly increased in all compost treatments while dry matter yield and weight of fruits were significantly higher in soil amended with 20 t ha-1. Correlation analysis showed that microbial activities at 6 weeks after transplanting (6 WAT) were more consistently and highly correlated with growth and yield parameters. It was concluded that microbial activities could be optimized to improve the yield of the two tomato varieties in an organic production system, through the application of compost, particularly at 20 t ha-1.

Keywords: compost, microbial activities, microbial contribution, tomato growth and yield

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Authors: M. Sakthivel, A. Devaki, D. Balasubramanyam, P. Kumarasamy, A. Raja, R. Anilkumar, H. Gopi

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The genetic parameters of growth traits in the New Zealand White rabbits maintained at Sheep Breeding and Research Station, Sandynallah, The Nilgiris, India were estimated by partitioning the variance and covariance components. The (co)variance components of body weights at weaning (W42), post-weaning (W70) and marketing (W135) age and growth efficiency traits viz., average daily gain (ADG), relative growth rate (RGR) and Kleiber ratio (KR) estimated on a daily basis at different age intervals (1=42 to 70 days; 2=70 to 135 days and 3=42 to 135 days) from weaning to marketing were estimated by restricted maximum likelihood, fitting six animal models with various combinations of direct and maternal effects. Data were collected over a period of 15 years (1998 to 2012). A log-likelihood ratio test was used to select the most appropriate univariate model for each trait, which was subsequently used in bivariate analysis. Heritability estimates for W42, W70 and W135 were 0.42 ± 0.07, 0.40 ± 0.08 and 0.27 ± 0.07, respectively. Heritability estimates of growth efficiency traits were moderate to high (0.18 to 0.42). Of the total phenotypic variation, maternal genetic effect contributed 14 to 32% for early body weight traits (W42 and W70) and ADG1. The contribution of maternal permanent environmental effect varied from 6 to 18% for W42 and for all the growth efficiency traits except for KR2. Maternal permanent environmental effect on most of the growth efficiency traits was a carryover effect of maternal care during weaning. Direct maternal genetic correlations, for the traits in which maternal genetic effect was significant, were moderate to high in magnitude and negative in direction. Maternal effect declined as the age of the animal increased. The estimates of total heritability and maternal across year repeatability for growth traits were moderate and an optimum rate of genetic progress seems possible in the herd by mass selection. The estimates of genetic and phenotypic correlations among body weight traits were moderate to high and positive; among growth efficiency traits were low to high with varying directions; between body weights and growth efficiency traits were very low to high in magnitude and mostly negative in direction. Moderate to high heritability and higher genetic correlation in body weight traits promise good scope for genetic improvement provided measures are taken to keep the inbreeding at the lowest level.

Keywords: genetic parameters, growth traits, maternal effects, rabbit genetics

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Authors: Ying Deng, Fan Yang

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Aim: To study the growth pattern of preterm infants during the first year of life and explore the association between head circumference (HC) and neurodevelopment sequences and to get a general knowledge of the incidence of anemia in preterm babies in Chengdu, Southwest China. Method: We conducted a prospective longitudinal study, neonates with gestational age < 37 weeks were enrolled this study from 2012.1.1 to 2014.7.9. Anthropometry (weight, height, HC) was obtained at birth, every month before 6 months-old and every 2 months in the next half year. All the infants’ age were corrected to 40 weeks. Growth data presented as Z-scores which was calculated by WHO Anthro software. Z-score defined as (the actual value minus the average value)/standard deviation. Neurodevelopment was assessed at 12 months-old [9-11 months corrected age (CA)] by using “Denver Development Screen Test (DDST)". The hemoglobin (Hb) was examined at 6 months for CA. Result: 445 preterm infants were followed-up 1 year, including 64 very low birth weight infants (VLBW), 246 low birth weight infants (LBW) and 135 normal birth weight infants(NBW). From full-term to 12 months after birth, catch-up growth was observed in most preterm infants. From VLBW to NBW, HCZ was -1.17 (95 % CI: -1.53,-0.80; P value < 0.0001) lower during the first12 months. WAZ was-1.12(95 % CI: -1.47,-0.76; p < 0.0001) lower. WHZ and HAZ were -1.04 (95%CI:-1.38, -0.69; P<0.0001) and -0.69 (95%CI:-1.06,-0.33; P < 0.0001) lower respectively. The peak of WAZ appeared during 0-3 months CA among preterm infants. For VLBW infants, the peak of HAZ and HCZ emerged at 8-11 months CA. However, the trend of HAZ and HCZ is the same as WAZ in LBW and NBW infants. Growth in the small for gestational age (SGA) infants was poorer than appropriate for gestational age (AGA) infants. The rate of DQ < 70 in VLBW and LBW were 29.6%, 7.7%, respectively (P < 0.0001). HCZ < -1SD at 3 months emerged as an independent predictor of DQ scores below 85 at 12 months after birth. The incidence of anemia in preterm infants was 11% at 6 months for CA. Moreover, 7 children (1.7%) diagnosed with Cerebral palsy (CP). Conclusions: The catch-up growth was observed in most preterm infants. VLBW and SGA showed poor growth. There was imbalance between WAZ and HAZ in VLBW infants. The VLBW babies had higher severe abnormal scores than LBW and NBW, especially in boys. Z score for HC at 3 months < -1SDwas a significant risk factor for abnormal DQ scores at the first year. The iron supplement reduced the morbidity of anemia in preterm infants.

Keywords: preterm infant, growth and development, DDST, Z-scores

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Authors: Celso L. Fernando, Toshio Yoshii, Takahiro Tsubota

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Understanding the causes of a road accident and predicting their occurrence is key to preventing deaths and serious injuries from road accident events. Traditional statistical methods such as the Poisson and the Logistics regressions have been used to find the association of the traffic environmental factors with the accident occurred; recently, an artificial neural network, ANN, a computational technique that learns from historical data to make a more accurate prediction, has emerged. Although the ability to make accurate predictions, the ANN has difficulty dealing with highly unbalanced attribute patterns distribution in the training dataset; in such circumstances, the ANN treats the minority group as noise. However, in the real world data, the minority group is often the group of interest; e.g., in the road traffic accident data, the events of the accident are the group of interest. This study proposes a combination of the k-means with the ANN to improve the predictive ability of the neural network model by alleviating the effect of the unbalanced distribution of the attribute patterns in the training dataset. The results show that the proposed method improves the ability of the neural network to make a prediction on a highly unbalanced distributed attribute patterns dataset; however, on an even distributed attribute patterns dataset, the proposed method performs almost like a standard neural network.

Keywords: accident risks estimation, artificial neural network, deep learning, k-mean, road safety

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Authors: Pricillia Priska Sianita

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An aesthetic facial profile is one of the goals in Orthodontics treatment. However, this is not easily achieved, especially in patients with Class II Division 1 malocclusion who have the clinical characteristics of convex profile and significant skeletal discrepancy due to mandibular growth deficiency. Malocclusion with skeletal problems require proper treatment timing for growth stimulation, and it must be done in early age and in need of good cooperation from the patient. If this is not done and the patient has passed the growth period, the ideal treatment is orthognathic surgery which is more complicated and more painful. The growth stimulation of skeletal malocclusion requires a careful cephalometric evaluation ranging from diagnosis to determine the parts that require stimulation to post-treatment evaluation to see the success achieved through changes in the measurement of the skeletal parameters shown in the cephalometric analysis. This case report aims to describe skeletal changes cephalometrically that were achieved through orthodontic treatment in growing period. Material and method: Lateral Cephalograms, pre-treatment, and post-treatment of cases of Class II Division 1 malocclusion is selected from a collection of cephalometric radiographic in a private clinic. The Cephalogram is then traced and measured for the skeletal parameters. The result is noted as skeletal condition data of pre-treatment and post-treatment. Furthermore, superimposition is done to see the changes achieved. The results show that growth stimulation through orthodontic treatment can solve the skeletal problem of Class II Division 1 malocclusion and the skeletal changes that occur can be verified through cephalometric analysis. The skeletal changes have an impact on the improvement of patient's facial profile. To sum up, the treatment timing on a skeletal malocclusion is very important to obtain satisfactory results for the improvement of the aesthetic facial profile, and skeletal changes can be verified through cephalometric evaluation of pre- and post-treatment.

Keywords: cephalometric evaluation, class II division 1 malocclusion, growth stimulation, skeletal changes, skeletal problems

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Authors: Shah Md. Asraful Islam, Shabina Yeasmin, Fatima Aktar Mousumi

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The experiment was conducted to evaluate the antagonistic potential of three commercially available Trichoderma strains viz., T. harzianum (armigera), T. harzianum (Ispahani), and T. viride against Colletotrichum musae isolates from three banana varieties viz., sagar, sobri, and katali. Mycelial growth rates of C. musae isolates were observed, the highest mycelial growth (11.62, 15.75, and 23.12 mm diameter) was observed by C. musae from sagor banana at 1, 2 and 3 days after inoculation, respectively. All the Trichoderma strains were capable of growth inhibition of C. musae isolates. After 4 days of duel culture, the highest mycelial growth reduction (10.33 mm diameter) was observed by the interaction between T. harzianum (armigera) with C. musae from sagor banana. Moreover, the highest growth inhibition (46.29%) was observed by the interaction between T. harzianum (armigera) with C. musae from the sobri banana. All the Trichoderma strains fully affected the viability of all the Colletotrichum isolates. Interestingly, both cultural filtrates and mycelial powders of all the Trichoderma strains showed a very nice inhibitory effect against C. musae isolates, where cultural filtrates were more potential than that of mycelial powders. So, all the tested Trichoderma strains may be used for the control of banana anthracnose disease.

Keywords: biological control, banana, anthracnose, Trichoderma, Colletotrichum

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Authors: Yilin Liao, Hewen Li, Paula McConvey

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Speed skaters often face a risk of concussion when they fall on the ice floor and impact crash mats during practices and competitive races. Several variables, including those related to the skater, the crash mat, and the impact position (body side/head/feet impact), are believed to influence the severity of the skater's concussion. While computer simulation modeling can be employed to analyze these accidents, the simulation process is time-consuming and does not provide rapid information for coaches and teams to assess the skater's injury risk in competitive events. This research paper promotes the exploration of the feasibility of using AI techniques for evaluating skater’s potential concussion severity, and to develop a fast concussion prediction tool using artificial neural networks to reduce the risk of treatment delays for injured skaters. The primary data is collected through virtual tests and physical experiments designed to simulate skater-mat impact. It is then analyzed to identify patterns and correlations; finally, it is used to train and fine-tune the artificial neural networks for accurate prediction. The development of the prediction tool by employing machine learning strategies contributes to the application of AI methods in sports science and has theoretical involvements for using AI techniques in predicting and preventing sports-related injuries.

Keywords: artificial neural networks, concussion, machine learning, impact, speed skater

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Authors: Mohamed M. Alsull

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In the present study, two microalgae species “Nannochloropsis sp. and Tetraselmis sp.” isolated from Penang National Park coastal waters, Malaysia; were cultivated under combined various laboratory conditions “salinity, light, nitrogen limitation and starvation”. Growth rate, dry weight, chlorophyll a content, total lipid and protein contents, were estimated at mid exponential growth phase. Both Nannochloropsis sp. and Tetraselmis sp. showed remarkable decrease in growth rate, chlorophyll a content and protein content companied with increase in lipid content under nitrogen limitation and starvation conditions. Maintaining Nannochloropsis sp. under salinity 15‰ caused only significant decrease in total protein content; while Tetraselmis sp. grown at the same salinity caused decrease in the growth rate, chlorophyll a, dry weight and total protein content only when nitrogen was available.

Keywords: biochemical composition, light, microalgae, nitrogen limitation, salinity

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Authors: A. Djoudi, R. Djibaou, H. A. Reguieg Yssaad

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Our study focuses on the study of a bacteria belonging to Pseudomonas solubilizing tricalcium phosphate. They were isolated from rhizosphere of a variety of broad bean grown in salt-affected soils (electrical conductivity between 4 and 8 mmhos/cm) of the irrigated perimeter of Mina in northwestern Algeria. Isolates which have advantageous results in the calcium phosphate solubilization index test were subjected to identification using API20 then used to re-inoculate the same soil in pots experimentation to assess the effects of inoculation on the growth of the broad bean (Vicia faba). Based on the results obtained from the in-vitro tests, two isolates P5 and P8 showed a significant effect on the solubilization of tricalcium phosphate with an index I estimated at 314% and 283% sequentially. According to the results of in-vivo tests, the inoculation of the soil with P5 and P8 were significantly and positively influencing the growth in biometric parameters of the broad bean. Inoculation with strain P5 has promoted the growth of the broad bean in stem height, stem fresh weight and stem dry weight of 108.59%, 115.28%, 104.33%, respectively. Inoculation with strain P8 has fostered the growth of the broad bean stem fresh weight of 112.47%. The effect of Pseudomonas on the development of Vicia faba is considered as an interesting process by which PGPR can increase biological production and crop protection.

Keywords: Pseudomonas, Vicia faba, promoting of plant growth, solubilization tricalcium phosphate

Procedia PDF Downloads 313