Search results for: crack growth prediction

Authors: Qunying Yuan, Manjula Bomma, Adrian Rhoden, Zhigang Xiao

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Selenium is an essential micronutrient for all mammals and plays an important role in maintaining human physiological functions. The potential applications of selenium as food supplements, cancer-prevention, antimicrobial and anti-inflammatory agents have been investigated in biomedicine and food sciences. Nanoscale of selenium is of particular interest due to its better biocompatibility, higher bioavailability, lower toxicity, more homogeneous distribution, and presumptive controlled release of substances. The objective of this study is to explore whether selenium nanoparticle (SeNP) has the potential to be used as a food preservative to reduce food spoilage. SeNPs were synthesized through ascorbic acid reduction of sodium selenite using the bovine serum albumin (BSA) as capping and stabilizing agent. The chemically synthesized SeNPs had a spherical conformation and a size of 22.8 ± 4.7 nm. FTIR analysis confirmed that the nanoparticles were covered with BSA. We further tested the antimicrobial activity of these SeNPs against common food-borne bacteria. Colony forming unit assay showed that SeNPs exhibited good inhibition on the growth of Listeria Monocytogens (ATCC15313), Staphylococcus epidermidis (ATCC 700583) starting at 0.5µg/mL, but only a moderate inhibitory effect on the growth of Staphylococcus aureus (ATCC12600) and Vibrio alginolyticus (ATCC 33787) at a concentration higher than 10µg/mL and 2.5µg/mL, respectively. There was a mild effect against the growth Salmonella enterica (ATCC19585) when the concentration reached 15µg/mL. No inhibition was observed in the growth of Enterococcus faecalis (ATCC 19433). Surprisingly, SeNPs appeared to promote the growth of Vibrio parahaemolyticus (ATCC43996) and Salmonella enterica (ATCC49284) at 30 µg/mL and above. Our preliminary data suggested that the chemically synthesized SeNPs may be able to inhibit some food-borne bacteria, and SeNP as a food preservative should be used with caution. We will explore the mechanisms of the inhibitory action of chemically synthesized SeNPs on bacterial growth and whether the SeNPs are able to inhibit the development of biofilm and antibiotic resistance.

Keywords: antimicrobial, food-borne bacteria, nanoparticles, selenium

Procedia PDF Downloads 75

Authors: Faheema Khan

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We have investigated the effects of salt stress on the stability of plant growth, water relations, photosynthetic variables, lipid peroxidation and antioxidant system in salt-tolerant (PK-327) and salt-sensitive (PK-471) soybean genotypes. Ten-day-old salt-tolerant and salt-sensitive soybean plants were subjected to 0-150 mM NaCl for 15 days. While the growth of genotype PK-327 was not affected significantly up to 75 mM NaCl treatment, the growth of the PK-471 was reduced significantly beyond 25 mM NaCl treatments. Salt stress caused severe impairments in photosynthetic variables like photosynthetic rate, chlorophyll fluorescence and chlorophyll content, being more pronounced in salt-sensitive genotype than in salt-tolerant.The activities of antioxidant enzymes (superoxide dismutase, catalase, ascorbate peroxidase and glutathione reductase) were higher in PK-327 than in PK-471 at various levels of salt treatments.It is concluded that tolerance capacity of PK-327 against salinity can be associated with the ability of this genotype in keeping an active photosynthetic system and strong antioxidant defence system.

Keywords: salt stress, soybean, antioxidant, photosynthesis

Procedia PDF Downloads 365

Authors: Sonya Barzgar, J. Patrick, A. Hettiaratchi

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Methane (CH4) is reputed as the second largest contributor to greenhouse effect with a global warming potential (GWP) of 34 related to carbon dioxide (CO2) over the 100-year horizon, so there is a growing interest in reducing the emissions of this gas. Methane biofiltration (MBF) is a cost effective technology for reducing low volume point source emissions of methane. In this technique, microbial oxidation of methane is carried out by methane-oxidizing bacteria (methanotrophs) which use methane as carbon and energy source. MBF uses a granular medium, such as soil or compost, to support the growth of methanotrophic bacteria responsible for converting methane to carbon dioxide (CO₂) and water (H₂O). Even though the biofiltration technique has been shown to be an efficient, practical and viable technology, the design and operational parameters, as well as the relevant microbial processes have not been investigated in depth. In particular, limited research has been done on the effects of sulphur on methane bio-oxidation. Since bacteria require a variety of nutrients for growth, to improve the performance of methane biofiltration, it is important to establish the input quantities of nutrients to be provided to the biofilter to ensure that nutrients are available to sustain the process. The study described in this paper was conducted with the aim of determining the influence of sulphur on methane elimination in a biofilter. In this study, a set of experimental measurements has been carried out to explore how the conversion of elemental sulphur could affect methane oxidation in terms of methanotrophs growth and system pH. Batch experiments with different concentrations of sulphur were performed while keeping the other parameters i.e. moisture content, methane concentration, oxygen level and also compost at their optimum level. The study revealed the tolerable limit of sulphur without any interference to the methane oxidation as well as the particular sulphur concentration leading to the greatest methane elimination capacity. Due to the sulphur oxidation, pH varies in a transient way which affects the microbial growth behavior. All methanotrophs are incapable of growth at pH values below 5.0 and thus apparently are unable to oxidize methane. Herein, the certain pH for the optimal growth of methanotrophic bacteria is obtained. Finally, monitoring methane concentration over time in the presence of sulphur is also presented for laboratory scale biofilters.

Keywords: global warming, methane biofiltration (MBF), methane oxidation, methanotrophs, pH, sulphur

Procedia PDF Downloads 221

Authors: Anwar Ul Haque, Waqar Asrar, Erwin Sulaeman, Jaffar S. M. Ali

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Pressure distribution data of the standard airfoils is usually used for the calibration purposes in subsonic wind tunnels. Results of such experiments are quite old and obtained by using the model in the spanwise direction. In this manuscript, pressure distribution over NACA 4412 airfoil model was presented by placing the 3D model in the lateral direction. The model is made of metal with pressure ports distributed longitudinally as well as in the lateral direction. The pressure model was attached to the floor of the tunnel with the help of the base plate to give the specified angle of attack to the model. Before the start of the experiments, the pressure tubes of the respective ports of the 128 ports pressure scanner are checked for leakage, and the losses due to the length of the pipes were also incorporated in the results for the specified pressure range. Growth rate maps of the boundary layer thickness were also plotted. It was found that with the increase in the velocity, the dynamic pressure distribution was also increased for the alpha seep. Plots of pressure distribution so obtained were overlapped with those obtained by using XFLR software, a low fidelity tool. It was found that at moderate and high angles of attack, the distribution of the pressure coefficients obtained from the experiments is high when compared with the XFLR ® results obtained along with the span of the wing. This under-prediction by XFLR ® is more obvious on the windward than on the leeward side.

Keywords: subsonic flow, boundary layer, wind tunnel, pressure testing

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Authors: Huseyin Zengin, Muhammet Emre Turan, Yunus Turen, Hayrettin Ahlatci, Yavuz Sun

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This study aimed for understanding the effects of dilute Nb additions on the austenite microstructure of microalloyed steels at five different reheating temperatures from 950 °C to 1300 °C. Four microalloyed high-carbon steels having 0.8 %wt C were examined in which three of them had varying Nb concentrations from 0.005 wt% to 0.02 wt% and one of them had no Nb concentration. The quantitative metallographic techniques were used to measure the average prior austenite grain size in order to compare the grain growth pinning effects of Nb precipitates as a function of reheating temperature. Due to the higher stability of the precipitates with increasing Nb concentrations, the grain coarsening temperature that resulted in inefficient grain growth impediment and a bimodal grain distribution in the microstructure, showed an increase with increasing Nb concentration. The respective grain coarsening temperatures (T_GC) in an ascending order for the steels having 0.005 wt% Nb, 0.01 wt% Nb and 0.02 wt% Nb were 950 °C, 1050 °C and 1150 °C. According to these observed grain coarsening temperatures, an approximation was made considering the complete dissolution temperature (T_DISS) of second phase particles as T_GC=T_DISS-300. On the other hand, the plain carbon steel did not show abnormal grain growth behaviour due to the absence of second phase particles. It was also observed that the higher the Nb concentration, the smaller the average prior austenite grain size although the small increments in Nb concenration did not change the average grain size considerably.

Keywords: microalloyed steels, prior austenite grains, second phase particles, grain coarsening temperature

Procedia PDF Downloads 245

Authors: Najafi S., Heidai R., Jamei R., Tofigh F.

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In order to study the effects of magnetic field on the root system and growth of Phaseolus vulgaris, an experiment was conducted in 2012. The possible involvement of magnetic field (MF) pretreatment in physiological factors of Phaseolus vulgaris was investigated. Seeds were subjected to 10 days with 1.8 mT of magnetic field for 1h per day. MF pretreatment decreased the plant height, fresh and dry weight, length of root and length of shoot, Chlorophyll a, Chlorophyll b and carotenoid in 10 days old seedling. In addition, activity of enzymes such as Catalase and Guaiacol peroxidase was decreased due to MF exposure. Also, the total Protein and DPPH content of the treated by magnetic field was not significantly changed in compare to control groups, while the flavonoid, Phenol and prolin content of the treated of the treated by magnetic field was significantly changed in compare to control groups. Lateral branches of roots and secondary roots increased with MF. The results suggest that pretreatment of this MF plays important roles in changes in crop productivity. In all cases there was observed a slight stimulating effect of the factors examined. The growth dynamics were weakened. The plants were shorter. Moreover, the effect of a magnetic field on the crop of Phaseolus vulgaris and its structure was small.

Keywords: carotenoid, Chlorophyll a, Chlorophyll b, DPPH, enzymes, flavonoid, germination, growth, phenol, proline, protein, magnetic field, phaseolus vulgaris

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Authors: Sekena H. Abd El-Aziem, Heba A. M. Abd El-Kader, Sally S. Alam, Othman E. Othman

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Growth hormone (GH) is an anabolic hormone synthesized and secreted by the somatotroph cells of the anterior lobe of the pituitary gland in a circadian and pulsatile manner, the pattern of which plays an important role in postnatal longitudinal growth and development, tissue growth, lactation, reproduction as well as protein, lipid and carbohydrate metabolism. The aim of this study was to detect the genetic polymorphism of GH gene in five camel breeds reared in Egypt; Sudany, Somali, Mowaled, Maghrabi and Falahy, using PCR-RFLP technique. Also this work aimed to identify the single nucleotide polymorphism between different genotypes detected in these camel breeds. The amplified fragment of camel GH at 613-bp was digested with the restriction enzyme MspI and the result revealed the presence of three different genotypes; CC, CT and TT in tested breeds and significant differences were recorded in the genotype frequencies between these camel breeds. The result showed that the Maghrabi breed that is classified as a dual purpose camels had higher frequency for allele C (0.75) than those in the other tested four breeds. The sequence analysis declared the presence of a SNP (C→T) at position 264 in the amplified fragment which is responsible for the destruction of the restriction site C^CGG and consequently the appearance of two different alleles C and T. The nucleotide sequences of camel GH alleles T and C were submitted to nucleotide sequences database NCBI/Bankit/GenBank and have accession numbers: KP143517 and KP143518, respectively. It is concluded that only one SNP C→T was detected in GH gene among the five tested camel breeds reared in Egypt and this nucleotide substitution can be used as a marker for the genetic biodiversity between camel breeds reared in Egypt. Also, due to the possible association between allele C and higher growth rate, we can used it in MAS for camels and enter the camels possess this allele in breeding program as a way for enhancement of growth trait in camel breeds reared in Egypt.

Keywords: camel breeds in Egypt, GH, PCR-RFLP, SNPs

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Authors: Omar Asad Ahmad, Mohammed Awwad

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Concrete is one of the essential elements that used in different types of construction these days, but it has many problems when interacts with environmental elements such as water, air, temperature, dust, and humidity. Also concrete made with Portland cement has certain characteristics: it is relatively strong in compression but weak in tension and tends to be brittle. These disadvantages make concrete limited to use in certain conditions. The most common problems appears on concrete are manifested by tearing, cracking, corrosion and spalling, which will lead to do some defect in concrete then in the whole construction, The fundamental objective of this research was to provide information about the hardened properties of concrete achieved by using easily available local raw materials in Jordan to support the practical work with partners in assessing the practicability of the mixes with polypropylene, and to facilitate the introduction of polypropylene fiber concrete (PFC) technology into general construction practice. Investigate the effect of the polypropylene fibers in PCC mixtures and on materials properties such as compressive strength, and tensile strength. Also to investigate the use of polypropylene fibers in plain cubes and cylindrical concrete to improve its compressive and tensile strengths to reduce early cracking and inhibit later crack growth. Increasing the hardness of concrete in this research is the main purpose to measure the deference of compressive strength and tensile strength between plain concrete and concrete mixture with polypropylene fibers different additions and to investigate its effect on reducing the early and later cracking problem. To achieve the goals of research 225 concrete test sample were prepared to measure it’s compressive strength and tensile strength, the concrete test sample were three classes (A,B,C), sub-classified to standard , and polypropylene fibers added by the volume of concrete (5%, 10%, 15%, and 20%). The investigation of polypropylene fibers mixture with concrete shows that the strengths of the cement are increased and the cracking decreased. The results show that for class A the recommended addition were 5% of polypropylene fibers additions for compressive strength and 10 % for tensile strength revels the best compressive strength that reach 26.67 Mpa and tensile strength that reach 2.548 Mpa records. Achieved results show that for classes B and C the recommend additions were 10 % polypropylene fibers revels the best compressive strength records where they reach 21.11 and 33.78 Mpa, records reach for tensile strength 2.707 and 2.65 Mpa respectively.

Keywords: polypropylene, effects, compressive, tensile, strengths, concrete, construction

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Authors: Bojan Najdenov, Danco Davcev

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Reduction of traffic congestions and the effects of pollution and waste of resources that come with them has been a big challenge in the past decades. Having reliable systems to facilitate the process of modeling and prediction of traffic conditions would not only reduce the environmental pollution, but will also save people time and money. Social networks play big role of people’s lives nowadays providing them means of communicating and sharing thoughts and ideas, that way generating huge knowledge bases by crowdsourcing. In addition to that, crowdsourcing as a concept provides mechanisms for fast and relatively reliable data generation and also many services are being used on regular basis because they are mainly powered by the public as main content providers. In this paper we present the Social-NETS-Traffic-Control System (SNTCS) that should serve as a facilitator in the process of modeling and prediction of traffic congestions. The main contribution of our system is to integrate data from social networks as Twitter and also implements a custom created crowdsourcing subsystem with which users report traffic conditions using an android application. Our first experience of the usage of the system confirms that the integrated approach allows easy extension of the system with other social networks and represents a very useful tool for traffic control.

Keywords: traffic, congestion reduction, crowdsource, social networks, twitter, android

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Authors: Wei-Ting Kuo, Feng-Huei Lin

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Epidermal growth factor receptor (EGFR) is often constitutively stimulated in cancer owing to the binding of ligands such as epidermal growth factor (EGF), so it is necessary to investigate the interaction between EGFR and its targeting biomolecules which were over ligands binding. This study would focus on the binding affinity and adhesion force of two targeting products anti-EGFR monoclonal antibody (mAb) and peptide A to EGFR comparing with EGF. Surface plasmon resonance (SPR) was used to obtain the equilibrium dissociation constant to evaluate the binding affinity. Atomic force microscopy (AFM) was performed to detect adhesion force. The result showed that binding affinity of mAb to EGFR was higher than that of EGF to EGFR, and peptide A to EGFR was lowest. The adhesion force between EGFR and mAb that was higher than EGF and peptide A to EGFR was lowest. From the studies, we could conclude that mAb had better adhesion force and binding affinity to EGFR than that of EGF and peptide A. SPR and AFM could confirm the interaction between receptor and targeting ligand easily and carefully. It provide a platform to screen ligands for receptor targeting and drug delivery.

Keywords: adhesion force, binding affinity, epidermal growth factor receptor, target molecule

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Authors: Amartya Hatua, Trung Nguyen, Andrew Sung

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In this paper, we study the information diffusion process on Twitter as a multivariate time series problem. Our model concerns three measures (volume, network influence, and sentiment of tweets) based on 10 features, and we collected 27 million tweets to build our information diffusion time series dataset for analysis. Then, different time series clustering techniques with Dynamic Time Warping (DTW) distance were used to identify different patterns of information diffusion. Finally, we built the information diffusion prediction models for new hashtags which comprise two phrases: The first phrase is recognizing the pattern using k-NN with DTW distance; the second phrase is building the forecasting model using the traditional Autoregressive Integrated Moving Average (ARIMA) model and the non-linear recurrent neural network of Long Short-Term Memory (LSTM). Preliminary results of performance evaluation between different forecasting models show that LSTM with clustering information notably outperforms other models. Therefore, our approach can be applied in real-world applications to analyze and predict the information diffusion characteristics of selected topics or memes (hashtags) in Twitter.

Keywords: ARIMA, DTW, information diffusion, LSTM, RNN, time series clustering, time series forecasting, Twitter

Procedia PDF Downloads 375

Authors: Ravi Soni, Irfan Pathan, Manish Pande

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The current product development process needs simultaneous consideration of different physics. The performance of the product needs to be considered under both structural and fluid loads. Examples include ducts and valves where structural behavior affects fluid motion and vice versa. Simulation of fluid-structure interaction involves modeling interaction between moving components and the fluid flow. In these scenarios, it is difficult to calculate the damping provided by fluid flow because of dynamic motions of components and the transient nature of the flow. Abaqus Explicit offers general capabilities for modeling fluid-structure interaction with the Coupled Eulerian-Lagrangian (CEL) method. The Coupled Eulerian-Lagrangian technique has been used to simulate fluid spillage through fuel valves during dynamic closure events. The technique to simulate pressure drops across Eulerian domains has been developed using stagnation pressure. Also, the fluid flow is calculated considering material flow through elements at the outlet section of the valves. The methodology has been verified on Eaton products and shows a good correlation with the test results.

Keywords: Coupled Eulerian-Lagrangian Technique, fluid structure interaction, spillage prediction, stagnation pressure

Procedia PDF Downloads 361

Authors: J. Ismail, F. Zaïri, M. Naït-Abdelaziz, Z. Azari

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In this work, a combined approach of continuum damage mechanics (CDM) and fracture mechanics is applied to model a glass plate behavior under static indentation. A spherical indenter is used and a CDM based constitutive model with an anisotropic damage tensor was selected and implemented into a finite element code to study the damage of glass. Various regions with critical damage values were predicted in good agreement with the experimental observations in the literature. In these regions, the directions of crack propagation, including both cracks initiating on the surface as well as in the bulk, were predicted using the strain energy density factor.

Keywords: finite element modeling, continuum damage mechanics, indentation, cracks

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Authors: A. Chellil, A. Nour, S. Lecheb , H. Mechakra, A. Bouderba, H. Kebir

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The study of the rotor dynamic in transient system allowed to determine the vibratory responses due to various excitations. This work presents a coupled gyroscopic effect in the defects of a rotor under dynamic loading. Calculations of different energies and virtual work from the various elements of the rotor are developed. To treat real systems a model of finite element was developed. This model of the rotor makes it possible to extract the frequencies and modal deformed, and to calculate the stresses in the critical zone. The study of the rotor in transient system allowed to determine the vibratory responses due to the unbalances, crack and various excitations.

Keywords: rotor, defect, finite element, numerical

Procedia PDF Downloads 445

Authors: Morteza Malek Yarand, Hadi Saebi Monfared

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This study aimed at designing and developing a mechanical force gauge for the square watermelon mold for the first time. It also tried to introduce the square watermelon characteristics and its production limitations. The mechanical force gauge performance and the product itself were also described. There are three main designable gauge models: a. hydraulic gauge, b. strain gauge, and c. mechanical gauge. The advantage of the hydraulic model is that it instantly displays the pressure and thus the force exerted by the melon. However, considering the inability to measure forces at all directions, complicated development, high cost, possible hydraulic fluid leak into the fruit chamber and the possible influence of increased ambient temperature on the fluid pressure, the development of this gauge was overruled. The second choice was to calculate pressure using the direct force a strain gauge. The main advantage of these strain gauges over spring types is their high precision in measurements; but with regard to the lack of conformity of strain gauge working range with water melon growth, calculations were faced with problems. Finally the mechanical pressure gauge has advantages, including the ability to measured forces and pressures on the mold surface during melon growth; the ability to display the peak forces; the ability to produce melon growth graph thanks to its continuous force measurements; the conformity of its manufacturing materials with the required physical conditions of melon growth; high air conditioning capability; the ability to permit sunlight reaches the melon rind (no yellowish skin and quality loss); fast and straightforward calibration; no damages to the product during assembling and disassembling; visual check capability of the product within the mold; applicable to all growth environments (field, greenhouses, etc.); simple process; low costs and so forth.

Keywords: mechanical force gauge, mold, reshaped fruit, square watermelon

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Authors: Yuhang Wang, Jorg Schluter, Sergiy Shelyag

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The high cost associated with high-resolution computational fluid dynamics (CFD) is one of the main challenges that inhibit the design, development, and optimisation of new combustion systems adapted for renewable fuels. In this study, we propose a physics-guided CNN-based model to predict turbulence evolution and mixing without requiring a traditional CFD solver. The model architecture is built upon U-Net and the inception module, while a physics-guided loss function is designed by introducing two additional physical constraints to allow for the conservation of both mass and pressure over the entire predicted flow fields. Then, the model is trained on the Large Eddy Simulation (LES) results of a natural turbulent mixing layer with two different Reynolds number cases (Re = 3000 and 30000). As a result, the model prediction shows an excellent agreement with the corresponding CFD solutions in terms of both spatial distributions and temporal evolution of turbulent mixing. Such promising model prediction performance opens up the possibilities of doing accurate high-resolution manifold-based combustion simulations at a low computational cost for accelerating the iterative design process of new combustion systems.

Keywords: computational fluid dynamics, turbulence, machine learning, combustion modelling

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Authors: I. L. Kim, J. Y. Lee, A. K. Tekile

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In consequence of the very high urbanization rate of Korea, the number of traffic noise damages in areas congested with population and facilities is steadily increasing. The current environmental noise levels data in major cities of the country show that the noise levels exceed the standards set for both day and night times. This research was about comparative analysis in search for optimal soundproof panel shape and design factor that can minimize sound reflection noise. In addition to the normal flat-type panel shape, the reflection noise reduction of swelling-type, combined swelling and curved-type, and screen-type were evaluated. The noise source model Nord 2000, which often provides abundant information compared to models for the similar purpose, was used in the study to determine the overall noise level. Based on vehicle categorization in Korea, the noise levels for varying frequency from different heights of the sound source (directivity heights of Harmonize model) have been calculated for simulation. Each simulation has been made using the ray-tracing method. The noise level has also been calculated using the noise prediction program called SoundPlan 7.2, for comparison. The noise level prediction was made at 15m (R1), 30 m (R2) and at middle of the road, 2m (R3) receiving the point. By designing the noise barriers by shape and running the prediction program by inserting the noise source on the 2nd lane to the noise barrier side, among the 6 lanes considered, the reflection noise slightly decreased or increased in all noise barriers. At R1, especially in the cases of the screen-type noise barriers, there was no reduction effect predicted in all conditions. However, the swelling-type showed a decrease of 0.7~1.2 dB at R1, performing the best reduction effect among the tested noise barriers. Compared to other forms of noise barriers, the swelling-type was thought to be the most suitable for reducing the reflection noise; however, since a slight increase was predicted at R2, further research based on a more sophisticated categorization of related design factors is necessary. Moreover, as swellings are difficult to produce and the size of the modules are smaller than other panels, it is challenging to install swelling-type noise barriers. If these problems are solved, its applicable region will not be limited to other types of noise barriers. Hence, when a swelling-type noise barrier is installed at a downtown region where the amount of traffic is increasing every day, it will both secure visibility through the transparent walls and diminish any noise pollution due to the reflection. Moreover, when decorated with shapes and design, noise barriers will achieve a visual attraction than a flat-type one and thus will alleviate any psychological hardships related to noise, other than the unique physical soundproofing functions of the soundproof panels.

Keywords: reflection noise, shaped noise barriers, sound proof panel, traffic noise

Procedia PDF Downloads 496

Authors: Anne C. Richer-De-Forges, Dominique Arrouays, Songchao Chen, Mercedes Roman Dobarco

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Most of the digital soil mapping (DSM) products rely on machine learning (ML) prediction models and/or the use or pedotransfer functions (PTF) in which calibration data come from soil analyses performed in labs. However, many other observations (often qualitative, nominal, or ordinal) could be used as proxies of lab measurements or as input data for ML of PTF predictions. DSM and ML are briefly described with some examples taken from the literature. Then, we explore the potential of an ordinal qualitative variable, i.e., the hand-feel soil texture (HFST) estimating the mineral particle distribution (PSD): % of clay (0-2µm), silt (2-50µm) and sand (50-2000µm) in 15 classes. The PSD can also be measured by lab measurements (LAST) to determine the exact proportion of these particle-sizes. However, due to cost constraints, HFST are much more numerous and spatially dense than LAST. Soil texture (ST) is a very important soil parameter to map as it is controlling many of the soil properties and functions. Therefore, comes an essential question: is it possible to use HFST as a proxy of LAST for calibration and/or validation of DSM predictions of ST? To answer this question, the first step is to compare HFST with LAST on a representative set where both information are available. This comparison was made on ca 17,400 samples representative of a French region (34,000 km2). The accuracy of HFST was assessed, and each HFST class was characterized by a probability distribution function (PDF) of its LAST values. This enables to randomly replace HFST observations by LAST values while respecting the PDF previously calculated and results in a very large increase of observations available for the calibration or validation of PTF and ML predictions. Some preliminary results are shown. First, the comparison between HFST classes and LAST analyses showed that accuracies could be considered very good when compared to other studies. The causes of some inconsistencies were explored and most of them were well explained by other soil characteristics. Then we show some examples applying these relationships and the increase of data to several issues related to DSM. The first issue is: do the PDF functions that were established enable to use HSFT class observations to improve the LAST soil texture prediction? For this objective, we replaced all HFST for topsoil by values from the PDF 100 time replicates). Results were promising for the PTF we tested (a PTF predicting soil water holding capacity). For the question related to the ML prediction of LAST soil texture on the region, we did the same kind of replacement, but we implemented a 10-fold cross-validation using points where we had LAST values. We obtained only preliminary results but they were rather promising. Then we show another example illustrating the potential of using HFST as validation data. As in numerous countries, the HFST observations are very numerous; these promising results pave the way to an important improvement of DSM products in all the countries of the world.

Keywords: digital soil mapping, improvement of digital soil mapping predictions, potential of using hand-feel soil texture, soil texture prediction

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Authors: Supria Sarkar, Vasit Sagan, Sourav Bhadra, Meghnath Pokharel, Felix B.Fritschi

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Soybean and their derivatives are very important agricultural commodities around the world because of their wide applicability in human food, animal feed, biofuel, and industries. However, the significance of soybean production depends on the quality of the soybean seeds rather than the yield alone. Seed composition is widely dependent on plant physiological properties, aerobic and anaerobic environmental conditions, nutrient content, and plant phenological characteristics, which can be captured by high temporal resolution remote sensing datasets. Planet scope (PS) satellite images have high potential in sequential information of crop growth due to their frequent revisit throughout the world. In this study, we estimate soybean seed composition while the plants are in the field by utilizing PlanetScope (PS) satellite images and different machine learning algorithms. Several experimental fields were established with varying genotypes and different seed compositions were measured from the samples as ground truth data. The PS images were processed to extract 462 hand-crafted vegetative and textural features. Four machine learning algorithms, i.e., partial least squares (PLSR), random forest (RFR), gradient boosting machine (GBM), support vector machine (SVM), and two recurrent neural network architectures, i.e., long short-term memory (LSTM) and gated recurrent unit (GRU) were used in this study to predict oil, protein, sucrose, ash, starch, and fiber of soybean seed samples. The GRU and LSTM architectures had two separate branches, one for vegetative features and the other for textures features, which were later concatenated together to predict seed composition. The results show that sucrose, ash, protein, and oil yielded comparable prediction results. Machine learning algorithms that best predicted the six seed composition traits differed. GRU worked well for oil (R-Squared: of 0.53) and protein (R-Squared: 0.36), whereas SVR and PLSR showed the best result for sucrose (R-Squared: 0.74) and ash (R-Squared: 0.60), respectively. Although, the RFR and GBM provided comparable performance, the models tended to extremely overfit. Among the features, vegetative features were found as the most important variables compared to texture features. It is suggested to utilize many vegetation indices for machine learning training and select the best ones by using feature selection methods. Overall, the study reveals the feasibility and efficiency of PS images and machine learning for plot-level seed composition estimation. However, special care should be given while designing the plot size in the experiments to avoid mixed pixel issues.

Keywords: agriculture, computer vision, data science, geospatial technology

Procedia PDF Downloads 114

Authors: Yu-Jia Jian, Emily Chia-Yu Su, Hui-Ling Hsu, Jian-Jhih Chen

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Coronary artery is the major supplier of myocardial blood flow. When fat and cholesterol are deposit in the coronary arterial wall, narrowing and stenosis of the artery occurs, which may lead to myocardial ischemia and eventually infarction. According to the World Health Organization (WHO), estimated 740 million people have died of coronary heart disease in 2015. According to Statistics from Ministry of Health and Welfare in Taiwan, heart disease (except for hypertensive diseases) ranked the second among the top 10 causes of death from 2013 to 2016, and it still shows a growing trend. According to American Heart Association (AHA), the risk factors for coronary heart disease including: age (> 65 years), sex (men to women with 2:1 ratio), obesity, diabetes, hypertension, hyperlipidemia, smoking, family history, lack of exercise and more. We have collected a dataset of 421 patients from a hospital located in northern Taiwan who received coronary computed tomography (CT) angiography. There were 300 males (71.26%) and 121 females (28.74%), with age ranging from 24 to 92 years, and a mean age of 56.3 years. Prior to coronary CT angiography, basic data of the patients, including age, gender, obesity index (BMI), diastolic blood pressure, systolic blood pressure, diabetes, hypertension, hyperlipidemia, smoking, family history of coronary heart disease and exercise habits, were collected and used as input variables. The output variable of the prediction module is the degree of coronary artery stenosis. The output variable of the prediction module is the narrow constriction of the coronary artery. In this study, the dataset was randomly divided into 80% as training set and 20% as test set. Four machine learning algorithms, including logistic regression, stepwise regression, neural network and decision tree, were incorporated to generate prediction results. We used area under curve (AUC) / accuracy (Acc.) to compare the four models, the best model is neural network, followed by stepwise logistic regression, decision tree, and logistic regression, with 0.68 / 79 %, 0.68 / 74%, 0.65 / 78%, and 0.65 / 74%, respectively. Sensitivity of neural network was 27.3%, specificity was 90.8%, stepwise Logistic regression sensitivity was 18.2%, specificity was 92.3%, decision tree sensitivity was 13.6%, specificity was 100%, logistic regression sensitivity was 27.3%, specificity 89.2%. From the result of this study, we hope to improve the accuracy by improving the module parameters or other methods in the future and we hope to solve the problem of low sensitivity by adjusting the imbalanced proportion of positive and negative data.

Keywords: decision support, computed tomography, coronary artery, machine learning

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Authors: Diego Diaz H., Cristian Larroulet

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Although a large earthquake has clear and immediate consequences such as deaths, destruction of infrastructure and displacement (at least temporary) of part of the population, scientific research about the impact of a geological disaster in economic activity is inconclusive, especially when looking beyond the very short term. Estimating the economic impact years after a disaster strike is non-trivial since there is an unavoidable difficulty in attributing the observed effect to the disaster and not to other economic shocks. Case studies are performed that determine the impact of earthquakes in Chile, Japan, and New Zealand at a regional level by applying the synthetic control method, using the natural disaster as treatment. This consisted in constructing a counterfactual from every region in the same country that is not affected (or is slightly affected) by the earthquake. The results show that the economies of Canterbury and Tohoku achieved greater levels of GDP per capita in the years after the disaster than they would have in the absence of the disaster. For the case of Chile, however, the region of Maule experiences a decline in GDP per capita because of the earthquake. All the results are robust according to the placebo tests. Also, the results suggest that national institutional quality improve the growth process after the disaster.

Keywords: earthquake, economic growth, institutional quality, synthetic control

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Authors: Bryan Emmanuel B. Marcelo, Frances Laura C. Galvez, Cyra Aleera T. Asanza, Ava Venice P. Garin

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The present study experimented with the utilization of mango (Mangifera indica) seeds as a fertilizer in the hydroponic farming of Bok Choy. The seeds were dried, mixed with EM Bokashi, and fermented for 14 days. The solution was then diluted into several ratios or concentrations: 25%: 1 part mango seed solution, 3 parts water; 50%: 2 parts mango seed solution, 2 parts water; 75%: 3 parts mango seed solution, 1 part water. 5 cups of soil with Bok Choy seeds were each planted in different containers for different concentrations of fertilizer. The fermentation of the nutrient solution lasted exactly 14 days and was directly brought to the lab for nutrient analysis and testing. In the data presented by the researchers in a span of 14 days, the study assessed varied mango seed fertilizer concentrations on Bok Choy growth. Despite an acidic pH (4.19) and moderate electrical conductivity, the 75% concentration yielded the highest growth (2.1cm) over 14 days, followed by 50%, 0, and 25%. Leaf count was consistently highest at 75%, and the leaf color remained #8CAA50 across concentrations. This emphasizes the importance of precise fertilizer application, with the 75% concentration showing optimal growth, the highest leaf count, and prevention of leaf withering until Day 14. Overall, these findings contribute to understanding bok choy’s adaptability and responses to different nutrient conditions.

Keywords: dilution ratios, organic liquid fertilizer, hydroponic farming, growth asssessment

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Authors: Deniz Peksen

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This thesis aims to predict trend movement of closing price of stock and to maximize portfolio by utilizing the predictions. In this context, the study aims to define a stock portfolio strategy from models created by using Logistic Regression, Gradient Boosting and Random Forest. Recently, predicting the trend of stock price has gained a significance role in making buy and sell decisions and generating returns with investment strategies formed by machine learning basis decisions. There are plenty of studies in the literature on the prediction of stock prices in capital markets using machine learning methods but most of them focus on closing prices instead of the direction of price trend. Our study differs from literature in terms of target definition. Ours is a classification problem which is focusing on the market trend in next 20 trading days. To predict trend direction, fourteen years of data were used for training. Following three years were used for validation. Finally, last three years were used for testing. Training data are between 2002-06-18 and 2016-12-30 Validation data are between 2017-01-02 and 2019-12-31 Testing data are between 2020-01-02 and 2022-03-17 We determine Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate as benchmarks which we should outperform. We compared our machine learning basis portfolio return on test data with return of Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate. We assessed our model performance with the help of roc-auc score and lift charts. We use logistic regression, Gradient Boosting and Random Forest with grid search approach to fine-tune hyper-parameters. As a result of the empirical study, the existence of uptrend and downtrend of five stocks could not be predicted by the models. When we use these predictions to define buy and sell decisions in order to generate model-based-portfolio, model-based-portfolio fails in test dataset. It was found that Model-based buy and sell decisions generated a stock portfolio strategy whose returns can not outperform non-model portfolio strategies on test dataset. We found that any effort for predicting the trend which is formulated on stock price is a challenge. We found same results as Random Walk Theory claims which says that stock price or price changes are unpredictable. Our model iterations failed on test dataset. Although, we built up several good models on validation dataset, we failed on test dataset. We implemented Random Forest, Gradient Boosting and Logistic Regression. We discovered that complex models did not provide advantage or additional performance while comparing them with Logistic Regression. More complexity did not lead us to reach better performance. Using a complex model is not an answer to figure out the stock-related prediction problem. Our approach was to predict the trend instead of the price. This approach converted our problem into classification. However, this label approach does not lead us to solve the stock prediction problem and deny or refute the accuracy of the Random Walk Theory for the stock price.

Keywords: stock prediction, portfolio optimization, data science, machine learning

Procedia PDF Downloads 62

Authors: Potaliya Pushpa, Kataria Sushma, D. S. Chowdhary, Dadhich Abhilasha

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Introduction: Stress is a nonspecific response of the body to a stressor or triggering stimulus. Chronic stress exposure contributes to various remarkable alterations o growth and development. Collective effects of stressors lead to several changes which are physical, physiological and behavioral in nature. Objective: To understand on an animal model how various chronic stress affect the somatic body growth as it can be useful for effective stress treatment and prevention of stress related illnesses. Material and Method: By selective fostering only male pup colonies were made and 102 male albino rats were studied. They were divided two groups as Control and Stressed. The experimental groups were exposed to four major types of stress as maternal deprivation, Restraint stress, electric foot shock and noise stress for affecting emotional, physical and physiological activities. Exposure was from birth to 17 week of life. Roentgenographs were taken in two planes as Dorso-ventral and Lateral and then measured for each rat. Various parameters were observed at specific intervals. Parameters recorded were Body weight and for linear growth it was summation of Cranial length, Head rump length and tail length. Behavior changes were also observed. Result: Multiple chronic stresses resulted in loss of approx. 25% of mean body weight. Maximal difference was found on 119th day (i.e. 87.81 gm) between the control and stressed group. Linear growth showed retardation which was found to be significant in stressed group on statistical analysis. Cranial Length and Head-rump Length showed maximum difference after maternal deprivation stress. After maternal deprivation (Day 21) and electric foot shock (Day 101) maximum difference i.e. 0.39 cm and 0.47 cm were found in cranial length of two groups. Electric foot shock had considerable impact on tail length. Noise Stress affected moreover behavior as compact to physical growth. Conclusion: Collective study showed that chronic stress not only resulted in reduced body weight in albino rats but also total linear size of rat thus affecting whole growth and development.

Keywords: stress, microscopic anatomy, macroscopic anatomy, chronic multiple stress, birth

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Authors: Meitham Amereh, Mohsen Akbari, Ben Nadler

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Cancer has been recognized as one of the most challenging problems in biology and medicine. Aggressive tumors are a lethal type of cancers characterized by high genomic instability, rapid progression, invasiveness, and therapeutic resistance. Their behavior involves complicated molecular biology and consequential dynamics. Although tremendous effort has been devoted to developing therapeutic approaches, there is still a huge need for new insights into the dark aspects of tumors. As one of the key requirements in better understanding the complex behavior of tumors, mathematical modeling and continuum physics, in particular, play a pivotal role. Mathematical modeling can provide a quantitative prediction on biological processes and help interpret complicated physiological interactions in tumors microenvironment. The pathophysiology of aggressive tumors is strongly affected by the extracellular cues such as stresses produced by mechanical forces between the tumor and the host tissue. During the tumor progression, the growing mass displaces the surrounding extracellular matrix (ECM), and due to the level of tissue stiffness, stress accumulates inside the tumor. The produced stress can influence the tumor by breaking adherent junctions. During this process, the tumor stops the rapid proliferation and begins to remodel its shape to preserve the homeostatic equilibrium state. To reach this, the tumor, in turn, upregulates epithelial to mesenchymal transit-inducing transcription factors (EMT-TFs). These EMT-TFs are involved in various signaling cascades, which are often associated with tumor invasiveness and malignancy. In this work, we modeled the tumor as a growing hyperplastic mass and investigated the effects of mechanical stress from surrounding ECM on tumor invasion. The invasion is modeled as volume-preserving inelastic evolution. In this framework, principal balance laws are considered for tumor mass, linear momentum, and diffusion of nutrients. Also, mechanical interactions between the tumor and ECM is modeled using Ciarlet constitutive strain energy function, and dissipation inequality is utilized to model the volumetric growth rate. System parameters, such as rate of nutrient uptake and cell proliferation, are obtained experimentally. To validate the model, human Glioblastoma multiforme (hGBM) tumor spheroids were incorporated inside Matrigel/Alginate composite hydrogel and was injected into a microfluidic chip to mimic the tumor’s natural microenvironment. The invasion structure was analyzed by imaging the spheroid over time. Also, the expression of transcriptional factors involved in invasion was measured by immune-staining the tumor. The volumetric growth, stress distribution, and inelastic evolution of tumors were predicted by the model. Results showed that the level of invasion is in direct correlation with the level of predicted stress within the tumor. Moreover, the invasion length measured by fluorescent imaging was shown to be related to the inelastic evolution of tumors obtained by the model.

Keywords: cancer, invasion, mathematical modeling, microfluidic chip, tumor spheroids

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Authors: Zhenzhen Xu, Chunfu Shao, Shengyou Wang, Chunjiao Dong

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To improve the satisfaction of residents' travel, this paper analyzes the characteristics and influencing factors of urban residents' travel behavior. First, a Multinominal Logit Model (MNL) model is built to analyze the characteristics of residents' travel behavior, reveal the influence of individual attributes, family attributes and travel characteristics on the choice of travel mode, and identify the significant factors. Then put forward suggestions for policy improvement. Finally, Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) models are introduced to evaluate the policy effect. This paper selects Futian Street in Futian District, Shenzhen City for investigation and research. The results show that gender, age, education, income, number of cars owned, travel purpose, departure time, journey time, travel distance and times all have a significant influence on residents' choice of travel mode. Based on the above results, two policy improvement suggestions are put forward from reducing public transportation and non-motor vehicle travel time, and the policy effect is evaluated. Before the evaluation, the prediction effect of MNL, SVM and MLP models was evaluated. After parameter optimization, it was found that the prediction accuracy of the three models was 72.80%, 71.42%, and 76.42%, respectively. The MLP model with the highest prediction accuracy was selected to evaluate the effect of policy improvement. The results showed that after the implementation of the policy, the proportion of public transportation in plan 1 and plan 2 increased by 14.04% and 9.86%, respectively, while the proportion of private cars decreased by 3.47% and 2.54%, respectively. The proportion of car trips decreased obviously, while the proportion of public transport trips increased. It can be considered that the measures have a positive effect on promoting green trips and improving the satisfaction of urban residents, and can provide a reference for relevant departments to formulate transportation policies.

Keywords: neural network, travel characteristics analysis, transportation choice, travel sharing rate, traffic resource allocation

Procedia PDF Downloads 120

Authors: Farzad Tofigh, Saeideh Najafi, Reza Heidari, Rashid Jamei

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In order to study the effects of magnetic field on the root system and growth of Phaseolus vulgaris, an experiment was conducted in 2012. The possible involvement of magnetic field (MF) pretreatment in physiological factors of Phaseolus vulgaris was investigated. Seeds were subjected to 10 days with 1.8 mT of magnetic field for 1h per day. MF pretreatment decreased the plant height, fresh and dry weight, length of root and length of shoot, Chlorophyll a, Chlorophyll b and carotenoid in 10 days old seedling. In addition, activity of enzymes such as Catalase and Guaiacol peroxidase was decreased due to MF exposure. Also, the total Protein and DPPH content of the treated by magnetic field was not significantly changed in compare to control groups, while the flavonoid, Phenol and prolin content of the treated of the treated by magnetic field was significantly changed in compare to control groups. Lateral branches of roots and secondary roots increased with MF. The results suggest that pretreatment of this MF plays important roles in changes in crop productivity. In all cases there was observed a slight stimulating effect of the factors examined. The growth dynamics were weakened. The plants were shorter. Moreover, the effect of a magnetic field on the crop of Phaseolus vulgaris and its structure was small.

Keywords: carotenoid, chlorophyll a, chlorophyll b, DPPH, enzymes, flavonoid, germination, growth, phenol, proline, protein, magnetic field

Procedia PDF Downloads 488

Authors: Priyadarsini Samal, Rajesh Singla

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Many mobile games come with the benefits of entertainment by introducing stress to the human brain. In recognizing this mental stress, the brain-computer interface (BCI) plays an important role. It has various neuroimaging approaches which help in analyzing the brain signals. Electroencephalogram (EEG) is the most commonly used method among them as it is non-invasive, portable, and economical. Here, this paper investigates the pattern in brain signals when introduced with mental stress. Two healthy volunteers played a game whose aim was to search hidden words from the grid, and the levels were chosen randomly. The EEG signals during gameplay were recorded to investigate the impacts of stress with the changing levels from easy to medium to hard. A total of 16 features of EEG were analyzed for this experiment which includes power band features with relative powers, event-related desynchronization, along statistical features. Support vector machine was used as the classifier, which resulted in an accuracy of 93.9% for three-level stress analysis; for two levels, the accuracy of 92% and 98% are achieved. In addition to that, another game that was similar in nature was played by the volunteers. A suitable regression model was designed for prediction where the feature sets of the first and second game were used for testing and training purposes, respectively, and an accuracy of 73% was found.

Keywords: brain computer interface, electroencephalogram, regression model, stress, word search

Procedia PDF Downloads 169

Authors: Sunthari Tharapan, Chockpisit Thepsithar, Kullanart Obsuwan

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This study was aimed to investigate the effect of various organic supplements on growth and development of Dendrobium discolor’s protocorms and seedlings growth of Dendrobium Judy Rutz. Protocorms of Dendrobium discolor with 2.0 cm. in diameter and seedlings of Dendrobium Judy Rutz at the same size (0.5 cm. height) were sub-cultured on Hyponex medium supplemented with cow milk (CM), soy milk (SM), potato extract (PE) and peptone (P) for 2 months. The protocorms were developed to seedlings in all treatments after cultured for 2 months. However, the best results were found on Hyponex medium supplemented with P was the best in which the maximum fresh and dry weight and maximum shoot height were obtained in this treatment statistically different (p ≤ 0.05) to other treatments. Moreover, Hyponex medium supplemented with P also stimulated the maximum mean number of 5.7 shoots per explant which also showed statistically different (p ≤ 0.05) when compared to other treatments. The results of growth of Dendrobium Judy Rutz seedlings indicated the medium supplemented with 100 mL/L PE enhanced the maximum fresh and dry weigh per explants with significantly different (p ≤ 0.05) in fresh weight from other treatments including the control medium without any organic supplementation. However, the dry weight was not significantly different (p ≤ 0.05) from medium supplemented with SM and P. There was multiple shoots induction in all media with or without organic supplementation ranging from 2.6 to 3 shoots per explants. The maximum shoot height was also obtained in the seedlings cultured on medium supplemented with PE while the longest root length was found in medium supplemented with SM.

Keywords: fresh weight, in vitro propagation, orchid, plant height

Procedia PDF Downloads 343

Authors: Hatun Korkmaz

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Todays, the developing countries of the world widely face challenges of economic growth, political, social and human development. One of the ways to achieve economic, political and human development is good governance. Without an improvement in good governance, the objectives of human development cannot be achieved. The good governance has become a key issue over preceding two decades and it is the very important component of good economic growth and human development. This paper argues that good governance impacts positively human development with the case of Rwanda. Rwanda is a good example of this subject. In this paper, firstly we explained that what is good governance and human development and how we measure them. Then we researched the relationship between good governance and human development in case of Rwanda with the indexes of many international institutions which are researching in this topics. Rwanda has recorded the 'best progress' since the year 2000, making it the ‘most successful' about governance. Rwanda is seen as one of the top ten countries in the region in terms of relative peace, political stability and economic progress. Part of the reason for Rwanda's success is accountability, which comprises access to information, elimination of corruption and bureaucracy and transparency in public service, which variables cumulatively earned it 72.1 percent. According to this research If countries want batter growth and human development then good reforms of good governance is needed.

Keywords: human development, Rwanda, good governance, governance, development

Procedia PDF Downloads 222