Search results for: wheat yield prediction

Authors: Shani Brathwaite, Deborah Villarroel-Lamb

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Wave run-up may be defined as the time-varying position of the landward extent of the water’s edge, measured vertically from the mean water level position. The hydrodynamics of the swash zone and the accurate prediction of maximum wave run-up, play a critical role in the study of coastal engineering. The understanding of these processes is necessary for the modeling of sediment transport, beach recovery and the design and maintenance of coastal engineering structures. However, due to the complex nature of the swash zone, there remains a lack of detailed knowledge in this area. Particularly, there has been found to be insufficient consideration of bed porosity and ultimately infiltration/exfiltration processes, in the development of wave run-up models. Theoretically, there should be an inverse relationship between maximum wave run-up and beach porosity. The greater the rate of infiltration during an event, associated with a larger bed porosity, the lower the magnitude of the maximum wave run-up. Additionally, most models have been developed using data collected on North American or Australian beaches and may have limitations when used for operational forecasting in Trinidad. This paper aims to assess the influence and significance of infiltration and exfiltration processes on wave run-up magnitudes within the swash zone. It also seeks to pay particular attention to how well various empirical formulae can predict maximum run-up on contrasting beaches in Trinidad. Traditional surveying techniques will be used to collect wave run-up and cross-sectional data on various beaches. Wave data from wave gauges and wave models will be used as well as porosity measurements collected using a double ring infiltrometer. The relationship between maximum wave run-up and differing physical parameters will be investigated using correlation analyses. These physical parameters comprise wave and beach characteristics such as wave height, wave direction, period, beach slope, the magnitude of wave setup, and beach porosity. Most parameterizations to determine the maximum wave run-up are described using differing parameters and do not always have a good predictive capability. This study seeks to improve the formulation of wave run-up by using the aforementioned parameters to generate a formulation with a special focus on the influence of infiltration/exfiltration processes. This will further contribute to the improvement of the prediction of sediment transport, beach recovery and design of coastal engineering structures in Trinidad.

Keywords: beach porosity, empirical models, infiltration, swash, wave run-up

Procedia PDF Downloads 355

Authors: S. Mousavian, D. Ashouri, F. Mousavian, V. Nikkhah Rashidabad, N. Ghazinia

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PH, temperature, and time of extraction of each stage, agitation speed, and delay time between stages effect on efficiency of zinc extraction from concentrate. In this research, efficiency of zinc extraction was predicted as a function of mentioned variable by artificial neural networks (ANN). ANN with different layer was employed and the result show that the networks with 8 neurons in hidden layer has good agreement with experimental data.

Keywords: zinc extraction, efficiency, neural networks, operating condition

Procedia PDF Downloads 544

Authors: Khushi Kashyap, Pratibha Singh

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In the recent years increasing interest in vegetarian diets has been observed, a major problem in this type of diet is to provide the appropriate amount of protein .Value addition of food is current most talked topic because of increasing nutritional awareness among consumers today. An investigation was conducted to develop protein rich multi-millet hemp seed khakhra. The seeds of cannabis sativa L. have been a significant source of food for thousand of year. In recent years, hemp has not been thoroughly explored for its nutritional potential due to the mistaken belief regarding the cannabis plants. Methodology- two variations was prepared referencing standard recipe. Variation 1 was prepared using 25g ragi, 25g bajra,40g whole wheat flour with 10g hemp seed powder, variation 2(RF-25g,BF25g,WWF-35g,HS-15g). The product was subjected to sensory evolution by semi trained panel members using 9 point hedonic on 50 panelists. Result- result of the sensory evaluation revealed that the product incorporated with 15g of hemp seed were similar to control I texture, taste and overall quality and was more acceptable by the panelist and was selected as final product seed. On estimation of the nutrient content 30g of khakhra provides 107kcal of energy,12g protein,75g carbohydrate, and 9.6g of fats with shelf life of 3 months. Conclusion- khakhras can be eaten as a snack at any time of the day. hemp seed powder incorporated in it enhances its nutritive value and makes it more nutritious. It is suitable for consumption of all the age group.

Keywords: cannabis sativa, hemp, protein, seed

Procedia PDF Downloads 88

Authors: Katerina H. Takova, Ivan N. Minkov, Gergana G. Zahmanova

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Molecular farming is the production of recombinant proteins in plants with the aim to use the protein as a purified product, crude extract or directly in the planta. Plants gain more attention as expression systems compared to other ones due to the cost effective production of pharmaceutically important proteins, appropriate post-translational modifications, assembly of complex proteins, absence of human pathogens to name a few. In addition, transient expression in plant leaves enables production of recombinant proteins within few weeks. Hepatitis E virus (HEV) is a causative agent of acute hepatitis. HEV causes epidemics in developing countries and is primarily transmitted through the fecal-oral route. Presently, all efforts for development of Hepatitis E vaccine are focused on the Open Read Frame 2 (ORF2) capsid protein as it contains epitopes that can induce neutralizing antibodies. For our purpose, we used the CMPV-based vector-pEAQ-HT for transient expression of HEV ORF2 in Nicotiana benthamina. Different molecular analysis (Western blot and ELISA) showed that HEV ORF2 capsid protein was expressed in plant tissue in high-yield up to 1g/kg of fresh leaf tissue. Electron microscopy showed that the capsid protein spontaneously assembled in low abundance virus-like particles (VLPs), which are highly immunogenic structures and suitable for vaccine development. The expressed protein was recognized by both human and swine HEV positive sera and can be used as a diagnostic reagent for the detection of HEV infection. Production of HEV capsid protein in plants is a promising technology for further HEV vaccine investigations. Here, we reported for a rapid high-yield transient expression of a recombinant protein in plants suitable for vaccine production as well as a diagnostic reagent. Acknowledgments -The authors’ research on HEV is supported with grants from the Project PlantaSYST under the Widening Program, H2020 as well as under the UK Biotechnological and Biological Sciences Research Council (BBSRC) Institute Strategic Programme Grant ‘Understanding and Exploiting Plant and Microbial Secondary Metabolism’ (BB/J004596/1). The authors want to thank Prof. George Lomonossoff (JIC, Norwich, UK) for his contribution.

Keywords: hepatitis E virus, plant molecular farming, transient expression, vaccines

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Authors: Mohammad R. Jalali, Mohammad M. Jalali

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The earliest theories of sloshing waves and solitary waves based on potential theory idealisations and irrotational flow have been extended to be applicable to more realistic domains. To this end, the computational fluid dynamics (CFD) methods are widely used. Three-dimensional CFD methods such as Navier-Stokes solvers with volume of fluid treatment of the free surface and Navier-Stokes solvers with mappings of the free surface inherently impose high computational expense; therefore, considerable effort has gone into developing depth-averaged approaches. Examples of such approaches include Green–Naghdi (GN) equations. In Cartesian system, GN velocity profile depends on horizontal directions, x-direction and y-direction. The effect of vertical direction (z-direction) is also taken into consideration by applying weighting function in approximation. GN theory considers the effect of vertical acceleration and the consequent non-hydrostatic pressure. Moreover, in GN theory, the flow is rotational. The present study illustrates the application of GN equations to propagation of sloshing waves and solitary waves. For this purpose, GN equations solver is verified for the benchmark tests of Gaussian hump sloshing and solitary wave propagation in shallow basins. Analysis of the free surface sloshing of even harmonic components of an initial Gaussian hump demonstrates that the GN model gives predictions in satisfactory agreement with the linear analytical solutions. Discrepancies between the GN predictions and the linear analytical solutions arise from the effect of wave nonlinearities arising from the wave amplitude itself and wave-wave interactions. Numerically predicted solitary wave propagation indicates that the GN model produces simulations in good agreement with the analytical solution of the linearised wave theory. Comparison between the GN model numerical prediction and the result from perturbation analysis confirms that nonlinear interaction between solitary wave and a solid wall is satisfactorilly modelled. Moreover, solitary wave propagation at an angle to the x-axis and the interaction of solitary waves with each other are conducted to validate the developed model.

Keywords: Green–Naghdi equations, nonlinearity, numerical prediction, sloshing waves, solitary waves

Procedia PDF Downloads 283

Authors: A. Belmokhtar, A. Yahiaoui, A. Benyoucef, M. Belbachir

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We reported the synthesis and characterization of nanocomposite poly (4,4’ methylenedianiline) via chemical polymerization of monomers 4,4’ methylenedianiline by ammonium persulfate (APS) at room temperature catalyzed by Maghnite-H+. A facile method was demonstrated to grow poly (4,4’ methylenedianiline) nanocomposite, which was carried out by mixing Ammonium Persulfate (APS) aqueous and 4,4’ methylenedianiline solution in the presence of Maghnite-H+ at room temperature The effect of amount of catalyst and time on the polymerization yield of the polymers was studied. Structure was confirmed by elemental analysis, UV vis, RMN-1H, and voltammetry cyclique.

Keywords: charaterization, maghnite-h+, polymerization, poly (4, 4’ methylenedianiline)

Procedia PDF Downloads 287

Authors: Man-Yun Liu, Emily Chia-Yu Su

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Alzheimer's disease (AD) is the public health crisis of the 21st century. AD is a degenerative brain disease and the most common cause of dementia, a costly disease on the healthcare system. Unfortunately, the cause of AD is poorly understood, furthermore; the treatments of AD so far can only alleviate symptoms rather cure or stop the progress of the disease. Currently, there are several ways to diagnose AD; medical imaging can be used to distinguish between AD, other dementias, and early onset AD, and cerebrospinal fluid (CSF). Compared with other diagnostic tools, blood (plasma) test has advantages as an approach to population-based disease screening because it is simpler, less invasive also cost effective. In our study, we used blood biomarkers dataset of The Alzheimer’s disease Neuroimaging Initiative (ADNI) which was funded by National Institutes of Health (NIH) to do data analysis and develop a prediction model. We used independent analysis of datasets to identify plasma protein biomarkers predicting early onset AD. Firstly, to compare the basic demographic statistics between the cohorts, we used SAS Enterprise Guide to do data preprocessing and statistical analysis. Secondly, we used logistic regression, neural network, decision tree to validate biomarkers by SAS Enterprise Miner. This study generated data from ADNI, contained 146 blood biomarkers from 566 participants. Participants include cognitive normal (healthy), mild cognitive impairment (MCI), and patient suffered Alzheimer’s disease (AD). Participants’ samples were separated into two groups, healthy and MCI, healthy and AD, respectively. We used the two groups to compare important biomarkers of AD and MCI. In preprocessing, we used a t-test to filter 41/47 features between the two groups (healthy and AD, healthy and MCI) before using machine learning algorithms. Then we have built model with 4 machine learning methods, the best AUC of two groups separately are 0.991/0.709. We want to stress the importance that the simple, less invasive, common blood (plasma) test may also early diagnose AD. As our opinion, the result will provide evidence that blood-based biomarkers might be an alternative diagnostics tool before further examination with CSF and medical imaging. A comprehensive study on the differences in blood-based biomarkers between AD patients and healthy subjects is warranted. Early detection of AD progression will allow physicians the opportunity for early intervention and treatment.

Keywords: Alzheimer's disease, blood-based biomarkers, diagnostics, early detection, machine learning

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Authors: C. S. Saini

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The composite flour blend consisting of corn, pearl millet, black gram and wheat bran in the ratio of 80:5:10:5 was taken to prepare the extruded product and their effect on physical properties of extrudate was studied. The extrusion process was conducted in laboratory by using twin screw extruder. The physical characteristics evaluated include lateral expansion, bulk density, water absorption index, water solubility index, rehydration ratio and moisture retention. The Central Composite Rotatable Design (CCRD) was used to decide the level of processing variables i.e. feed moisture content (%), screw speed (rpm), and barrel temperature (oC) for the experiment. The data obtained after extrusion process were analyzed by using response surface methodology. A second order polynomial model for the dependent variables was established to fit the experimental data. The numerical optimization studies resulted in 127°C of barrel temperature, 246 rpm of screw speed, and 14.5% of feed moisture as optimum variables to produce acceptable extruded product. The responses predicted by the software for the optimum process condition resulted in lateral expansion 126 %, bulk density 0.28 g/cm3, water absorption index 4.10 g/g, water solubility index 39.90 %, rehydration ratio 544 % and moisture retention 11.90 % with 75 % desirability.

Keywords: black gram, corn flour, extrusion, physical characteristics

Procedia PDF Downloads 477

Authors: Indu Bala Sethi, Meena Sewhag, Rakesh Kumar, Parveen Kumar

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Field experiment was conducted at Pulse Research Area of CCS Haryana Agricultural University, Hisar during rabi 2012-13 to study the economics of chickpea cultivars as influenced by sowing time and seed rate on sandy loam soils under irrigated conditions. The factorial experiment consisting of 24 treatment combinations with two sowing time (1st fortnight of November and 1st fortnight of December.) and four cultivars (H09-23, H08-18, C-235 and HC-1) kept in main plots while three seed rates viz. 40 kg ha-1, 50 kg ha-1 and 60 kg ha-1 was laid out in split plot design with three replications. The crop was sown with common row spacing of 30 cm as per the dates of sowing. The fertilizer was applied in the form of di- ammonium phosphate. The soil of the experimental site was deep sandy loam having pH of 7.9, EC of 0.13 dS/m and low in organic carbon (0.34%), low in available N status (193.36 kg ha-1), medium in available P2O5 (32.18 kg ha-1) and high in available K2O (249.67 kg ha-1). The crop was irrigated as and when required so as to maintain adequate soil moisture in the root zone The crop was sprayed with monocrotophos (1.25 l/ha) at initiation of flowering and at pod filling stage to protect the crop from pod borer attack. The yield was measured at the time of harvest. The cost of field preparation, sowing of seeds, thinning, weeding, plant protection, harvesting and cleaning contributed to fixed cost. The experiment was laid out in a split plot design with two sowing time (1st fortnight of November and 1st fortnight of December.) and four cultivars (H09-23, H08-18, C-235 and HC-1) kept in main plots while three seed rates viz. 40 kg ha-1, 50 kg ha-1 and 60 kg ha-1 were kept in subplots and replicated thrice. Results revealed that 1st fortnight of November sowing recorded significantly higher gross (Rs.1, 01,254 ha-1), net returns (Rs. 68,504 ha-1) and BC (3.09) ratio as compared to delayed crop of chickpea. Highest gross (Rs.91826 ha-1), net returns (Rs. 59076ha-1) and BC ratio (2.81) was recorded with H08-18. Higher value of cost of cultivation of chickpea was observed in higher seed rate than the lower ones. However no significant variation in net and gross returns was observed due to seed rates. Highest BC (2.72) ratio was recorded with 50 kg ha-1 which differs significantly from 60 kg ha-1 but was at par with 40 kg ha-1. This is because of higher grain yield obtained with 50 kg ha-1 seed rate. Net profit for farmers growing chickpea with seed rate of 50 kg ha-1 was higher than the farmers growing chickpea with seed rate of 40 and 60 kg ha.

Keywords: chickpea, cultivars, seed rate, sowing time

Procedia PDF Downloads 441

Authors: Shehu S. Janguza

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The need for national security cannot be over Emphasis, which should be pursued by any means. Thus the need for effective management of education through effective school community Relationship/participation. In preparing and implementing only effort to promote community involvement in manning Education, it is importance to understand the whole picture of community participation, how it works, what forms are used, what benefit it can yield and what we should expect in the process of carrying out the efforts finally emphasis will be made on how effective school community relationship/participation and lead to national security.

Keywords: community participation, managing, school community, national security

Procedia PDF Downloads 592

Authors: Karima Boukhadia

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In the present study, the subsonic flow in an asymmetrical diffuser was simulated numerically using code CFX 11.0 and its generator of grid ICEM CFD. Two models of turbulence were tested: K- ε and K- ω SST. The results obtained showed that the K- ε model singularly over-estimates the speed value close to the wall and that the K- ω SST model is qualitatively in good agreement with the experimental results of Buice and Eaton 1997. They also showed that the separation and reattachment of the fluid on the tilted wall strongly depends on its angle of inclination and that the length of the zone of separation increases with the angle of inclination of the lower wall of the diffuser.

Keywords: asymmetric diffuser, separation, reattachment, tilt angle, separation zone

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Authors: Rouzbeh Jafari, Joe Nava

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This study includes learnings from an engineering practice normally performed on large scale biohydrogen processes. If properly scale-up is done, biohydrogen can be a reliable pathway for biowaste valorization. Most of the studies on biohydrogen process development have used model feedstock to investigate process key performance indicators (KPIs). This study does not intend to compare different technologies with model feedstock. However, it reports economic drivers and technical challenges which help in developing a road map for expanding biohydrogen economy deployment in Canada. BBA is a consulting firm responsible for the design of hydrogen production projects. Through executing these projects, activity has been performed to identify, register and mitigate technical drawbacks of large-scale hydrogen production. Those learnings, in this study, have been applied to the biohydrogen process. Through data collected by a comprehensive literature review, a base case has been considered as a reference, and several case studies have been performed. Critical parameters of the process were identified and through common engineering practice (process design, simulation, cost estimate, and life cycle assessment) impact of these parameters on the commercialization risk matrix and class 5 cost estimations were reported. The process considered in this study is food waste and woody biomass dark fermentation. To propose a reliable road map to develop a sustainable biohydrogen production process impact of critical parameters was studied on the end-to-end process. These parameters were 1) feedstock composition, 2) feedstock pre-treatment, 3) unit operation selection, and 4) multi-product concept. A couple of emerging technologies also were assessed such as photo-fermentation, integrated dark fermentation, and using ultrasound and microwave to break-down feedstock`s complex matrix and increase overall hydrogen yield. To properly report the impact of each parameter KPIs were identified as 1) Hydrogen yield, 2) energy consumption, 3) secondary waste generated, 4) CO2 footprint, 5) Product profile, 6) $/kg-H2 and 5) environmental impact. The feedstock is the main parameter defining the economic viability of biohydrogen production. Through parametric studies, it was found that biohydrogen production favors feedstock with higher carbohydrates. The feedstock composition was varied, by increasing one critical element (such as carbohydrate) and monitoring KPIs evolution. Different cases were studied with diverse feedstock, such as energy crops, wastewater slug, and lignocellulosic waste. The base case process was applied to have reference KPIs values and modifications such as pretreatment and feedstock mix-and-match were implemented to investigate KPIs changes. The complexity of the feedstock is the main bottleneck in the successful commercial deployment of the biohydrogen process as a reliable pathway for waste valorization. Hydrogen yield, reaction kinetics, and performance of key unit operations highly impacted as feedstock composition fluctuates during the lifetime of the process or from one case to another. In this case, concept of multi-product becomes more reliable. In this concept, the process is not designed to produce only one target product such as biohydrogen but will have two or multiple products (biohydrogen and biomethane or biochemicals). This new approach is being investigated by the BBA team and the results will be shared in another scientific contribution.

Keywords: biohydrogen, process scale-up, economic evaluation, commercialization uncertainties, hydrogen economy

Procedia PDF Downloads 108

Authors: Sabrina Ladjama, Aicha Rizi, Azzedine Abbaci

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In this work, we use the crossover model to formulate a comprehensive fundamental equation of state for the thermodynamic properties for several n-alkanes in the critical region that extends to the classical region. This equation of state is constructed on the basis of comparison of selected measurements of pressure-density-temperature data, isochoric and isobaric heat capacity. The model can be applied in a wide range of temperatures and densities around the critical point for n-heptane. It is found that the developed model represents most of the reliable experimental data accurately.

Keywords: crossover model, critical region, fundamental equation, n-heptane

Procedia PDF Downloads 474

Authors: Rekab Djabri Hamza, Daoud Salah

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We report first-principles calculations of structural and magnetic properties of TmAs compound in zinc blende(B3) and CsCl(B2), structures employing the density functional theory (DFT) within the local density approximation (LDA). We use the full potential linear muffin-tin orbitals (FP-LMTO) as implemented in the LMTART-MINDLAB code (Calculation). Results are given for lattice parameters (a), bulk modulus (B), and its first derivatives(B’) in the different structures NaCl (B1) and CsCl (B2). The most important result in this work is the prediction of the possibility of transition; from cubic rocksalt (NaCl)→ CsCl (B2) (32.96GPa) for TmAs. These results use the LDA approximation.

Keywords: LDA, phase transition, properties, DFT

Procedia PDF Downloads 114

Authors: Adesola O. Orimoloye, Edward Gobina

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Flue gas is the most prevalent source of carbon dioxide off-gas from numerous processes globally. Among the lion's share of this flue gas is the ever-present electric power plant, primarily fuelled by coal, and then secondly, natural gas. The carbon dioxide found in coal fired power plant off gas is among the dirtiest forms of carbon dioxide, even with many of the improvements in the plants; still this will yield sulphur and nitrogen compounds; among other rather nasty compounds and elements; all let to the atmosphere. This presentation will focus on the characterization of carbon dioxide-rich flue gas sources with a view of eventual conversion to chemicals and fuels using novel membrane reactors.

Keywords: flue gas, carbon dioxide, membrane, catalyst, syngas

Procedia PDF Downloads 521

Authors: Vinod Kumar Jaysaval, Prateek Agarwal

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Performance prediction of airborne radar is a challenging and cumbersome task in clutter scenario for different types of targets. A generalized model requires to predict the performance of Radar for air targets as well as ground moving targets. In this paper, we propose a generalized model to bring out the performance of airborne radar for different Pulsed Repetition Frequency (PRF) as well as different type of targets. The model provides a platform to bring out different subsystem parameters for different applications and performance requirements under different types of clutter terrain.

Keywords: airborne radar, blind zone, clutter, probability of detection

Procedia PDF Downloads 469

Authors: Tami A. Ulhiza, Noor I. M. Puad, Azlin S. Azmi, Mohd. I. A. Malek

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High amount of chemical oxygen demand (COD) in starchy waste can be harmful to the environment. In common practice, starch processing wastewater is discharged to the river without proper treatment. However, starchy waste still contains complex sugars and organic acids. By the right pretreatment method, the complex sugar can be hydrolyzed into more readily digestible sugars which can be utilized to be converted into more valuable products. At the same time, the global demand of energy is inevitable. The continuous usage of fossil fuel as the main source of energy can lead to energy scarcity. Hydrogen is a renewable form of energy which can be an alternative energy in the future. Moreover, hydrogen is clean and carries the highest energy compared to other fuels. Biohydrogen produced from waste has significant advantages over chemical methods. One of the major problems in biohydrogen production is the raw material cost. The carbohydrate-rich starchy wastes such as tapioca, maize, wheat, potato, and sago wastes is a promising candidate to be used as a substrate in producing biohydrogen. The utilization of those wastes for biohydrogen production can provide cheap energy generation with simultaneous waste treatment. Therefore this paper aims to review variety source of starchy wastes that has been widely used to synthesize biohydrogen. The scope includes the source of waste, the performance in yielding hydrogen, the pretreatment method and the type of culture that is suitable for starchy waste.

Keywords: biohydrogen, dark fermentation, renewable energy, starchy waste

Procedia PDF Downloads 223

Authors: Moses Kolade Ogun, Ina Korner

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To improve on the sustainable resource management in the wastepaper recycling industry, studies into the valorization of wastes generated by the industry are necessary. The industry produces different residues, among which is the deinking sludge (DS). The DS is generated from the deinking process and constitutes a major fraction of the residues generated by the European pulp and paper industry. The traditional treatment of DS by incineration is capital intensive due to energy requirement for dewatering and the need for complementary fuel source due to DS low calorific value. This could be replaced by a biotechnological approach. This study, therefore, investigated the biogas potential of different DS streams (different dewatering degrees) and the influence of the high calcium carbonate content of DS on its biogas potential. Dewatered DS (solid fraction) sample from filter press and the filtrate (liquid fraction) were collected from a partner wastepaper recycling company in Germany. The solid fraction and the liquid fraction were mixed in proportion to realize DS with different water content (55–91% fresh mass). Spiked samples of DS using deionized water, cellulose and calcium carbonate were prepared to simulate DS with varying calcium carbonate content (0– 40% dry matter). Seeding sludge was collected from an existing biogas plant treating sewage sludge in Germany. Biogas potential was studied using a 1-liter batch test system under the mesophilic condition and ran for 21 days. Specific biogas potential in the range 133- 230 NL/kg-organic dry matter was observed for DS samples investigated. It was found out that an increase in the liquid fraction leads to an increase in the specific biogas potential and a reduction in the absolute biogas potential (NL-biogas/ fresh mass). By comparing the absolute biogas potential curve and the specific biogas potential curve, an optimal dewatering degree corresponding to a water content of about 70% fresh mass was identified. This degree of dewatering is a compromise when factors such as biogas yield, reactor size, energy required for dewatering and operation cost are considered. No inhibitory influence was observed in the biogas potential of DS due to the reported high calcium carbonate content of DS. This study confirms that DS is a potential bioresource for biogas production. Further optimization such as nitrogen supplementation due to DS high C/N ratio can increase biogas yield.

Keywords: biogas, calcium carbonate, deinking sludge, dewatering, water content

Procedia PDF Downloads 180

Authors: Hana Hanaee-Ahvaz, Monika Cserjan-Puschmann, Christopher Tauer, Gerald Striedner

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Incorporation of noncanonical amino acids (ncAA) into proteins has become an interesting topic as proteins featured with ncAAs offer a wide range of different applications. Nowadays, technologies and systems exist that allow for the site-specific introduction of ncAAs in vivo, but the efficient production of proteins modified this way is still a big challenge. This is especially true for 'hard-to-express' proteins where low yields are encountered even with the native sequence. In this study, site-specific incorporation of azido-ethoxy-carbonyl-Lysin (azk) into an anti-tumor-necrosis-factor-α-Fab (FTN2) was investigated. According to well-established parameters, possible site positions for ncAA incorporation were determined, and corresponding FTN2 genes were constructed. Each of the modified FTN2 variants has one amber codon for azk incorporated either in its heavy or light chain. The expression level for all variants produced was determined by ELISA, and all azk variants could be produced with a satisfactory yield in the range of 50-70% of the original FTN2 variant. In terms of expression yield, neither the azk incorporation position nor the subunit modified (heavy or light chain) had a significant effect. We confirmed correct protein processing and azk incorporation by mass spectrometry analysis, and antigen-antibody interaction was determined by surface plasmon resonance analysis. The next step is to characterize the effect of azk incorporation on protein stability and aggregation tendency via differential scanning calorimetry and light scattering, respectively. In summary, the incorporation of ncAA into our Fab candidate FTN2 worked better than expected. The quantities produced allowed a detailed characterization of the variants in terms of their properties, and we can now turn our attention to potential applications. By using click chemistry, we can equip the Fabs with additional functionalities and make them suitable for a wide range of applications. We will now use this option in a first approach and develop an assay that will allow us to follow the degradation of the recombinant target protein in vivo. Special focus will be laid on the proteolytic activity in the periplasm and how it is influenced by cultivation/induction conditions.

Keywords: degradation, FTN2, hard-to-express protein, non-canonical amino acids

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Authors: Sandhya Devi A

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The protein content of the cranberry squash (protein: 0g) may be increased by extracting protein from the lentils (9 g), which is particularly linked to a lower risk of developing heart disease. Using the technique of alkaline extraction from the lentils flour, protein may be extracted. Alkaline extraction of protein from lentil flour was optimized utilizing response surface approach in order to maximize both protein content and yield. Cranberry squash may be taken if a protein fortification syrup is prepared and processed into the squash.

Keywords: alkaline extraction, cranberry squash, protein fortification, response surface methodology

Procedia PDF Downloads 107

Authors: Ali Kazemi

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Those within the volatile and interconnected international economic markets, appropriately predicting market trends, hold substantial fees for traders and financial establishments. Traditional device mastering strategies have made full-size strides in forecasting marketplace movements; however, monetary data's complicated and networked nature calls for extra sophisticated processes. This observation offers a groundbreaking method for monetary marketplace prediction that leverages the synergistic capability of Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks. Our suggested algorithm is meticulously designed to forecast the traits of inventory market indices and cryptocurrency costs, utilizing a comprehensive dataset spanning from January 1, 2015, to December 31, 2023. This era, marked by sizable volatility and transformation in financial markets, affords a solid basis for schooling and checking out our predictive version. Our algorithm integrates diverse facts to construct a dynamic economic graph that correctly reflects market intricacies. We meticulously collect opening, closing, and high and low costs daily for key inventory marketplace indices (e.g., S&P 500, NASDAQ) and widespread cryptocurrencies (e.g., Bitcoin, Ethereum), ensuring a holistic view of marketplace traits. Daily trading volumes are also incorporated to seize marketplace pastime and liquidity, providing critical insights into the market's shopping for and selling dynamics. Furthermore, recognizing the profound influence of the monetary surroundings on financial markets, we integrate critical macroeconomic signs with hobby fees, inflation rates, GDP increase, and unemployment costs into our model. Our GCN algorithm is adept at learning the relational patterns amongst specific financial devices represented as nodes in a comprehensive market graph. Edges in this graph encapsulate the relationships based totally on co-movement styles and sentiment correlations, enabling our version to grasp the complicated community of influences governing marketplace moves. Complementing this, our LSTM algorithm is trained on sequences of the spatial-temporal illustration discovered through the GCN, enriched with historic fee and extent records. This lets the LSTM seize and expect temporal marketplace developments accurately. Inside the complete assessment of our GCN-LSTM algorithm across the inventory marketplace and cryptocurrency datasets, the version confirmed advanced predictive accuracy and profitability compared to conventional and opportunity machine learning to know benchmarks. Specifically, the model performed a Mean Absolute Error (MAE) of 0.85%, indicating high precision in predicting day-by-day charge movements. The RMSE was recorded at 1.2%, underscoring the model's effectiveness in minimizing tremendous prediction mistakes, which is vital in volatile markets. Furthermore, when assessing the model's predictive performance on directional market movements, it achieved an accuracy rate of 78%, significantly outperforming the benchmark models, averaging an accuracy of 65%. This high degree of accuracy is instrumental for techniques that predict the course of price moves. This study showcases the efficacy of mixing graph-based totally and sequential deep learning knowledge in economic marketplace prediction and highlights the fee of a comprehensive, records-pushed evaluation framework. Our findings promise to revolutionize investment techniques and hazard management practices, offering investors and economic analysts a powerful device to navigate the complexities of cutting-edge economic markets.

Keywords: financial market prediction, graph convolutional networks (GCNs), long short-term memory (LSTM), cryptocurrency forecasting

Procedia PDF Downloads 63

Authors: Esra Polat

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Partial Least Squares Discriminant Analysis (PLSDA) is a statistical method for classification and consists a classical Partial Least Squares Regression (PLSR) in which the dependent variable is a categorical one expressing the class membership of each observation. PLSDA can be applied in many cases when classical discriminant analysis cannot be applied. For example, when the number of observations is low and when the number of independent variables is high. When there are missing values, PLSDA can be applied on the data that is available. Finally, it is adapted when multicollinearity between independent variables is high. The aim of this study is to determine the economic and/or demographic indicators, which are effective in grouping the 28 European Union (EU) member countries and 7 candidate countries (including potential candidates Bosnia and Herzegovina (BiH) and Kosova) by using the data set obtained from database of the World Bank for 2014. Leaving the political issues aside, the analysis is only concerned with the economic and demographic variables that have the potential influence on country’s eligibility for EU entrance. Hence, in this study, both the performance of PLSDA method in classifying the countries correctly to their pre-defined groups (candidate or member) and the differences between the EU countries and candidate countries in terms of these indicators are analyzed. As a result of the PLSDA, the value of percentage correctness of 100 % indicates that overall of the 35 countries is classified correctly. Moreover, the most important variables that determine the statuses of member and candidate countries in terms of economic indicators are identified as 'external balance on goods and services (% GDP)', 'gross domestic savings (% GDP)' and 'gross national expenditure (% GDP)' that means for the 2014 economical structure of countries is the most important determinant of EU membership. Subsequently, the model validated to prove the predictive ability by using the data set for 2015. For prediction sample, %97,14 of the countries are correctly classified. An interesting result is obtained for only BiH, which is still a potential candidate for EU, predicted as a member of EU by using the indicators data set for 2015 as a prediction sample. Although BiH has made a significant transformation from a war-torn country to a semi-functional state, ethnic tensions, nationalistic rhetoric and political disagreements are still evident, which inhibit Bosnian progress towards the EU.

Keywords: classification, demographic indicators, economic indicators, European Union, partial least squares discriminant analysis

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Authors: Faiz N. K. Yusufi, Aquil Ahmed, Jamal Ahmad

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Predicting the risk of diabetic retinopathy (DR) in Indian type 2 diabetes patients is immensely necessary. India, being the second largest country after China in terms of a number of diabetic patients, to the best of our knowledge not a single risk score for complications has ever been investigated. Diabetic retinopathy is a serious complication and is the topmost reason for visual impairment across countries. Any type or form of DR has been taken as the event of interest, be it mild, back, grade I, II, III, and IV DR. A sample was determined and randomly collected from the Rajiv Gandhi Centre for Diabetes and Endocrinology, J.N.M.C., A.M.U., Aligarh, India. Collected variables include patients data such as sex, age, height, weight, body mass index (BMI), blood sugar fasting (BSF), post prandial sugar (PP), glycosylated haemoglobin (HbA1c), diastolic blood pressure (DBP), systolic blood pressure (SBP), smoking, alcohol habits, total cholesterol (TC), triglycerides (TG), high density lipoprotein (HDL), low density lipoprotein (LDL), very low density lipoprotein (VLDL), physical activity, duration of diabetes, diet control, history of antihypertensive drug treatment, family history of diabetes, waist circumference, hip circumference, medications, central obesity and history of DR. Cox proportional hazard regression is used to design risk scores for the prediction of retinopathy. Model calibration and discrimination are assessed from Hosmer Lemeshow and area under receiver operating characteristic curve (ROC). Overfitting and underfitting of the model are checked by applying regularization techniques and best method is selected between ridge, lasso and elastic net regression. Optimal cut off point is chosen by Youden’s index. Five-year probability of DR is predicted by both survival function, and Markov chain two state model and the better technique is concluded. The risk scores developed can be applied by doctors and patients themselves for self evaluation. Furthermore, the five-year probabilities can be applied as well to forecast and maintain the condition of patients. This provides immense benefit in real application of DR prediction in T2DM.

Keywords: Cox proportional hazard regression, diabetic retinopathy, ROC curve, type 2 diabetes mellitus

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Authors: Jain Jyoti, Jain Shorab, Sinha Shishir

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In the field of material engineering, composites are in great concern for their nonbiodegradability and their cost. In order to reduce its cost and weight, plant derived fibers witnessed miraculous triumph. Plant fibers can be of different types like seed fibers, blast fibers, leaf fibers, etc. Composites can be reinforced with exclusively one type of natural fiber or also can be combined with two or more different types of natural or synthetic fibers to boost up their specific properties. Among all natural fibers, wheat straw, bagasse, kenaf, pineapple leaf, banana, coir, ramie, flax, etc. pineapple leaf fibers have very good mechanical properties. Being hydrophilic in nature, pineapple leaf fibers have very less affinity towards all types of polymer matrixes like HDPE, LDPE, PET, epoxy, etc. Surface treatments like alkaline treatment in different concentrations were conducted to improve its adhesion and compatibility towards hydrophobic polymer matrix i.e. epoxy resin. Pineapple leaf fiber epoxy composites have been prepared using hand layup method. Effect of fiber loading and surface treatments have been studied for different mechanical properties i.e. tensile strength, flexural strength and impact properties of pineapple leaf fiber composites. Analysis of fiber morphology has also been studied using FTIR, XRD. Scanning electron microscopy has also been used to study and compare the morphology of untreated and treated fibers. Also, the fracture surface has been reviewed comparing the reported literature of other eminent researchers of this field.

Keywords: composite, mechanical, natural fiber, pineapple leaf fiber

Procedia PDF Downloads 255

Authors: Habtamu Ayenew Asegie

Abstract:

Wealth, as opposed to income or consumption, implies a more stable and permanent status. Due to natural and human-made difficulties, households' economies will be diminished, and their well-being will fall into trouble. Hence, governments and humanitarian agencies offer considerable resources for poverty and malnutrition reduction efforts. One key factor in the effectiveness of such efforts is the accuracy with which low-income or poor populations can be identified. As a result, this study aims to predict a household’s wealth status using ensemble Machine learning (ML) algorithms. In this study, design science research methodology (DSRM) is employed, and four ML algorithms, Random Forest (RF), Adaptive Boosting (AdaBoost), Light Gradient Boosted Machine (LightGBM), and Extreme Gradient Boosting (XGBoost), have been used to train models. The Ethiopian Demographic and Health Survey (EDHS) dataset is accessed for this purpose from the Central Statistical Agency (CSA)'s database. Various data pre-processing techniques were employed, and the model training has been conducted using the scikit learn Python library functions. Model evaluation is executed using various metrics like Accuracy, Precision, Recall, F1-score, area under curve-the receiver operating characteristics (AUC-ROC), and subjective evaluations of domain experts. An optimal subset of hyper-parameters for the algorithms was selected through the grid search function for the best prediction. The RF model has performed better than the rest of the algorithms by achieving an accuracy of 96.06% and is better suited as a solution model for our purpose. Following RF, LightGBM, XGBoost, and AdaBoost algorithms have an accuracy of 91.53%, 88.44%, and 58.55%, respectively. The findings suggest that some of the features like ‘Age of household head’, ‘Total children ever born’ in a family, ‘Main roof material’ of their house, ‘Region’ they lived in, whether a household uses ‘Electricity’ or not, and ‘Type of toilet facility’ of a household are determinant factors to be a focal point for economic policymakers. The determinant risk factors, extracted rules, and designed artifact achieved 82.28% of the domain expert’s evaluation. Overall, the study shows ML techniques are effective in predicting the wealth status of households.

Keywords: ensemble machine learning, households wealth status, predictive model, wealth status prediction

Procedia PDF Downloads 37

Authors: Kaewkarn Phuangsombat, Arthit Phuangsombat, Anupun Terdwongworakul

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Hard seeds will not grow and can cause mold in sprouting process. Thus, the hard seeds need to be separated from the normal seeds. Near infrared hyperspectral imaging in a range of 900 to 1700 nm was implemented to develop a model by partial least squares discriminant analysis to discriminate the hard seeds from the normal seeds. The orientation of the seeds was also studied to compare the performance of the models. The model based on hilum-up orientation achieved the best result giving the coefficient of determination of 0.98, and root mean square error of prediction of 0.07 with classification accuracy was equal to 100%.

Keywords: mung bean, near infrared, germinatability, hard seed

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Authors: Sonia Ben Hamza, Sabra Habli, Nejla Mahjoub Said, Hervé Bournot, Georges Le Palec

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In this work, we determine the turbulent dynamic structure of pollutant dispersion in two-phase free surface flow. The numerical simulation was performed using ANSYS Fluent. The flow study is three-dimensional, unsteady and isothermal. The study area has been endowed with a rectangular obstacle to analyze its influence on the hydrodynamic variables and progression of the pollutant. The numerical results show that the hydrodynamic model provides prediction of the dispersion of a pollutant in an open channel flow and reproduces the recirculation and trapping the pollutant downstream near the obstacle.

Keywords: CFD, free surface, polluant dispersion, turbulent flows

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Authors: Erkan Koç, Mehmet Ünal, Ercan Candan

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In this study, the effect of zinc content (0.5-3.0 wt.%) in as-cast Mg-3Al alloy which were fabricated with high-purity raw materials towards the microstructure and mechanical properties was studied. Microstructure results showed that increase in zinc content changed the secondary phase distribution of the alloys. Mechanical test results demonstrate that with the increasing Zn addition the enhancement of the hardness value by 29%, ultimate tensile strength by 16% and yield strength by 15% can be achieved as well as decreasing of elongation by 33%. The improvement in mechanical properties for Mg-Al–Zn alloys with increasing Zn content up to 3% of weight may be ascribed to second phase strengthening.

Keywords: magnesium, zinc, mechanical properties, Mg17Al12

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Authors: Karima Oldyerou, B. Meddah, A. Tirtouil

Abstract:

Laurusnobilis is an aromatic plant, common in Algeria and widely used by local people as a source of spice and for medicinal purposes. The essential oil of this plant is the subject of this work in a physicochemical and microbiological study. The extraction of the essential oil was carried by steam distillation and the highest yield (1.5%) was determined in May. The organoleptic and physico-chemical characters are consistent with those obtained in the literature with some differences that can be attributed to certain factors. Evaluation of antibacterial activity showed a sensitivity of Salmonella spp. with an MIC of 2,5 mg.ml-1, and other bacteria of the intestinal flora of Wistar rats: E. coli and Lactobacillus sp. have a high potential for resistance with MICs respectively equal to 10 and 20 mg.ml-1.

Keywords: laurus nobilis, essential oil, physicochemical character, MIC, intestinal flora, antibacterial activity

Procedia PDF Downloads 334

Authors: Ahmet Karakuş, Akif Can Kilic, Emre Alptekin

Abstract:

A growing number of studies have been conducted to determine how well-being may be predicted using well-designed models. It is necessary to investigate the backgrounds of features in order to construct a viable Subjective Well-Being (SWB) model. We have picked the suitable variables from the literature on SWB that are acceptable for real-world data instructions. The goal of this work is to evaluate the model by feeding it with SWB characteristics and then categorizing the stress levels using machine learning methods to see how well it performs on a real dataset. Despite the fact that it is a multiclass classification issue, we have achieved significant metric scores, which may be taken into account for a specific task.

Keywords: machine learning, multiclassification problem, subjective well-being, perceived stress scale

Procedia PDF Downloads 129