Search results for: tomato yield prediction

Authors: Shital Bangar, G. B. Khandagale

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The experimentation was carried out to study the influence of sulphur and boron on growth parameters and productivity of soybean in kharif season of 2009-2010 at Experimental Farm, Department of Agricultural Botany, Marathwada Agricultural University, Parbhani (M.S.). The object was to evaluate the impact of sulphur and boron on growth, development, grain yield and physiological aspects of soybean variety MAUS-81. Nine treatments consisted of three levels of sulphur i.e. 20, 30 and 40 Kg/ha as well as three levels boron i.e.10, 15 and 20 kg boron/ha and the combinations of these two mineral elements i.e. Sulphur @30 kg/ha + Borax @15 kg/ha and Sulphur @40 kg/ha + Borax @ 20 kg/ha with one control treatment in Randomized Block Design (RBD) with three replications. The effect of sulphur and boron on various growth parameters of soybean like relative growth rate (RGR) and net assimilation rate (NAR) were remained statistically on par with each other. However, the application of higher dose of Sulphur @40 kg/ha + Borax @ 20 kg/ha enhanced significantly all the growth parameters. Application of the nutrients increased the dry matter accumulation of the crop plant and hence, other growth indices like RGR and NAR also increased significantly. RGR and NAR values were recorded highest at the initial crop growth stages and decline thereafter. The application of sulphur @40 kg/ha + Borax @ 20 kg/ha recorded significantly higher content of chlorophyll ‘a’ than rest of the treatments and chlorophyll ‘b’ observed higher in boron @15 kg/ha as well as boron@20 kg/ha, whereas total chlorophyll content was maximum in sulphur @40 kg/ha. Oil content was not influenced significantly due to above fertilization. The highest seed yield and total biological yield were obtained with combination of Sulphur @40 kg/ha + Borax @ 20 kg/ha, single sulphur and boron application also showed a significant effect on seed and biological yield.

Keywords: boron, growth, productivity, quality, soybean and sulphur

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Authors: Ercan Ceyhan, Ali Kahraman, Hasan Dalgıç

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This research was made during the 2011 and 2012 growing periods according to “Randomized Blocks Design” with 3 replications. Research material was the following chickpea genotype: CA119, CA128, CA149, CA150, CA222, CA250, CA254 and other 2 commercial varieties named as Gökçe and Yaşa. Some agronomical characteristics such as plant height (cm), number of pod per plant, number of seed per pod, number of seed per plant, 1000 seed weight (g) and seed yield (kg ha-1) were determined. Statistically significant variations were found amongst the genotypes for all variables except seeds per pod. Means of the two years showed the range for plant height was from 52.83 cm (Gökçe) to 73.00 cm (CA150), number of pod per plant was from 14.00 (CA149) to 26.83 (CA261), number of seed per pod was from 1.10 (Gökçe) to 1.19 (CA149 ve CA250), number of seed per plant was from 16.28 (CA149) to 31.65 (CA261), 1000 seed weight was from 295.85 g (CA149) to 437.80 g (CA261) and seed yield was from 1342.73 kg ha-1 (CA261) to 2161.50 kg ha-1 (CA128). Results of the research implicated that the new developed lines were superior compared with the control (commercial) varieties by means of most of the characteristics.

Keywords: agricultural characters, chickpea, seed yield, genotype variations

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Authors: Hailemariam Solomon, Taye Tadesse, Daniel Nadew, Firezer Girma

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Sorghum is an important crop in semi-arid regions and has shown resilience to drought stress. However, recurrent drought is affecting its productivity. Therefore, it is necessary to explore genes that contribute to drought stress adaptation to increase sorghum productivity. The aim of this study is to evaluate and determine the effect of root system traits, specifically root angle, on drought stress adaptation and grain yield performance in sorghum genotypes. A total of 428 sorghum genotypes from the Ethiopian breeding program were evaluated in three drought-stress environments. Field trials were conducted using a row-column design with three replications. Root system traits were phenotyped using a high-throughput phenotyping platform and analyzed using a row-column design with two replications. Data analysis was performed using R software and regression analysis. The study found significant variations in root system architecture among the sorghum genotypes. Non-stay-green genotypes had a grain yield ranging from 1.63 to 3.1 tons/ha, while stay-green genotypes had a grain yield ranging from 2.4 to 2.9 tons/ha. The analysis of root angle showed that non-stay-green genotypes had an angle ranging from 8.0 to 30.5 degrees, while stay-green genotypes had an angle ranging from 12.0 to 29.0 degrees. Improved varieties exhibited angles between 14.04 and 19.50 degrees. Positive and significant correlations were observed between leaf areas and shoot dry weight, as well as between leaf width and shoot dry weight. Negative correlations were observed between root angle and leaf area, as well as between root angle and root length. This research highlights the importance of root system architecture, particularly root angle traits, in enhancing grain yield production in drought-stressed conditions. It also establishes an association between root angle and grain yield traits for maximizing sorghum productivity.

Keywords: roor sysytem architecture, root angle, narrow root angle, wider root angle, drought

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Authors: Adeleye Ebenezer Omotayo

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The main effects of tillage, poultry manure and interaction effects of tillage-poultry manure combinations on soil characteristics and yam yield were investigated in a factorial experiment involving four tillage techniques namely (ploughing (p), ploughing plus harrowing (PH), manual ridging (MR), manual heaping (MH) and poultry manure at two levels 0 t ha-1 and 10 t ha-1 arranged in split-plot design. Data obtained were subjected to analysis of variance using Statistical Analysis System (SAS) Institute Package. Soil moisture content, bulk density and total porosity were significantly (p>0.05) influenced by soil tillage techniques. Manually heaped and ridged plots had the lowest soil bulk density, moisture content and highest total porosity. The soil total N, exchangeable Mg, k, base saturation and CEC were better enhanced in manually tilled plots. Soil nutrients status declined at the end of the second cropping for all the tillage techniques in the order PH>P>MH>MR. Yam tuber yields were better enhanced in manually tilled plots than mechanically tilled plots. Poultry manure application reduced soil bulk density, temperature, increased total porosity and soil moisture content. It also improved soil organic matter, total N, available P, exchangeable Mg, Ca, K and lowered exchange acidity. It also increased yam tuber yield significantly. Tillage techniques plots amended with poultry manure enhanced yam tuber yield relative to tillage techniques plots without poultry manure application. It is concluded that yam production on alfisol in Southwest Nigeria requires loose soil structure for tuber development and that the use of poultry manure in combination with tillage is recommended as it will ensure stability of soil structure, improve soil organic matter status, nutrient availability and high yam tuber yield. Also, it will help to reduce the possible deleterious effects of tillage on soil properties and yam performance.

Keywords: ploughing, poultry manure, yam, yield

Procedia PDF Downloads 269

Authors: Tshilidzi C. Manyanya, Nthaduleni S. Nethengwe, Edmore Kori

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The UNFCCC, FAO, GCF, IPCC and other global structures advocate for agro-ecology do address food security and sovereignty. However, most of the expected outcomes concerning agro-ecological were not empirically tested for universal application. Agro-ecology is theorised to increase crop health over ago-ecological farms and decrease over conventional farms. Increased crop health means increased carbon sequestration and thus less CO2 in the atmosphere. This is in line with the view that global warming is anthropogenically enhanced through GHG emissions. Agro-ecology mainly affects crop health, soil carbon content and yield on the cultivated land. Economic sustainability is directly related to yield capacity, which is theorized to increase by 3-10% in a space of 3 - 10 years as a result of agro-ecological implementation. This study aimed to empirically assess the practicality and validity of these assumptions. The study utilized mainly GIS and RS techniques to assess the effectiveness of agro-ecology in crop health improvement from satellite images. The assessment involved a longitudinal study (2013 – 2015) assessing the changes that occur after a farm retrofits from conventional agriculture to agro-ecology. The assumptions guided the objectives of the study. For each objective, an agro-ecological farm was compared with a conventional farm in the same climatic conditional occupying the same general location. Crop health was assessed using satellite images analysed through ArcGIS and Erdas. This entailed the production of NDVI and Re-classified outputs of the farm area. The NDVI ranges of the entire period of study were thus compared in a stacked histogram for each farm to assess for trends. Yield capacity was calculated based on the production records acquired from the farmers and plotted in a stacked bar graph as percentages of a total for each farm. The results of the study showed decreasing crop health trends over 80% of the conventional farms and an increase over 80% of the organic farms. Yield capacity showed similar patterns to those of crop health. The study thus showed that agro-ecology is an effective strategy for crop-health improvement and yield increase.

Keywords: agro-ecosystem, conventional farm, dialectical, sustainability

Procedia PDF Downloads 216

Authors: Muhammet M. Erdem, Erdoğan Özbay, Ibrahim H. Durmuş, Mustafa Erdemir, Murat Bikçe, Müzeyyen Balçıkanlı

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In this paper, rheological behavior of alkali activated slag concretes were investigated depending on the sodium concentration (SC), waiting time (WT) after production, and constituents’ temperature (CT) parameters. For this purpose, an experimental program was conducted with four different SCs of 1.85, 3.0, 4.15, and 5.30%, three different WT of 0 (just after production), 15, and 30 minutes and three different CT of 18, 30, and 40 °C. Solid precursors are activated by water glass and sodium hydroxide solutions with silicate modulus (Ms = SiO₂/Na₂O) of 1. Slag content and (water + activator solution)/slag ratio were kept constant in all mixtures. Yield stress and plastic viscosity values were defined for each mixture by using the ICAR rheometer. Test results were demonstrated that all of the three studied parameters have tremendous effect on the yield stress and plastic viscosity values of the alkali activated slag concretes. Increasing the SC, WT, and CT drastically augmented the rheological parameters. At the 15 and 30 minutes WT after production, most of the alkali activated slag concretes were set instantaneously, and rheological measurements were not performed.

Keywords: alkali activation, slag, rheology, yield stress, plastic viscosity

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Authors: Ignatio Madanhire, Charles Mbohwa

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This study explores to improve the resource efficiency, waste water reduction and to reduce losses of raw materials in a beverage making industry. A number of cleaner production technologies were put across in this work. It was also noted that cleaner production technology practices are not only desirable from the environmental point of view, but they also make good economic sense, in their contribution to the bottom line by conserving resources like energy, raw materials and manpower, improving yield as well as reducing treatment/disposal costs. This work is a resource in promoting adoption and implementation of CP in other industries for sustainable development.

Keywords: resource efficiency, beverages, reduce losses, cleaner production, energy, yield

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Authors: Deokyeong Choe, Sung Hun Youn, Younggon Kim, Chul Soo Shin

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L-Cysteine is extensively used as a supplement of pharmaceuticals, cosmetics, food and feed additives. It has obtained industrially by hydrolysis of human hair and poultry feathers. However, there are some problems such as the restriction of using materials from animals and the intractable waste pollution. The enzymatic conversion has been regarded as an environmental-friendly method. Currently, the biggest bottle-neck of enzymatic conversion is the low yield of L-cysteine due to the low substrate solubility. In this study, the method of enhancing the solubility of the substrate D,L-2-amino-Δ2-thiazoline-4-carboxylicacid (ATC) was developed and the enzymatic reaction at high concentration levels was performed. A large amount of substrate in aqueous solutions was dissolved by pH control using salts. As the pH of the solution increased, the solubility of ATC increased. It was thought that a shift of ATC from acid form (-COOH) to dissociated carboxylic group (-COO-) would improve its hydrophilicity leading to solubility increase. The highest solubility of ATC was 610 mM at pH 10.5, whereas the maximum reaction rate was obtained at pH 8.3. As a result, a high L-cysteine yield of 250 mM was achieved at pH 9.1, which was obtained from a combination of optimum pH conditions for ATC solubility and enzymatic conversion. This yield corresponds to approximately 18 times of that in previous reports.

Keywords: D, L-2-amino-Δ2-thiazoline-4-carboxylicacid, enzymatic conversion, high-substrate solubilization, L-Cysteine

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Authors: Hao Yan, Xiaobing Zhang

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The working mechanism of miniaturized solid rocket motors (SRMs) is not yet fully understood. It is imperative to explore its unique features. However, there are many disadvantages to using common multi-objective evolutionary algorithms (MOEAs) in predicting the parameters of the miniaturized SRM during its conceptual design phase. Initially, the design variables and objectives are constrained in a lumped parameter model (LPM) of this SRM, which leads to local optima in MOEAs. In addition, MOEAs require a large number of calculations due to their population strategy. Although the calculation time for simulating an LPM just once is usually less than that of a CFD simulation, the number of function evaluations (NFEs) is usually large in MOEAs, which makes the total time cost unacceptably long. Moreover, the accuracy of the LPM is relatively low compared to that of a CFD model due to its assumptions. CFD simulations or experiments are required for comparison and verification of the optimal results obtained by MOEAs with an LPM. The conceptual design phase based on MOEAs is a lengthy process, and its results are not precise enough due to the above shortcomings. An artificial neural network (ANN) based parameter prediction is proposed as a way to reduce time costs and improve prediction accuracy. In this method, an ANN is used to build a surrogate model that is trained with a 3D numerical simulation. In design, the original LPM is replaced by a surrogate model. Each case uses the same MOEAs, in which the calculation time of the two models is compared, and their optimization results are compared with 3D simulation results. Using the surrogate model for the parameter prediction process of the miniaturized SRMs results in a significant increase in computational efficiency and an improvement in prediction accuracy. Thus, the ANN-based surrogate model does provide faster and more accurate parameter prediction for an initial design scheme. Moreover, even when the MOEAs converge to local optima, the time cost of the ANN-based surrogate model is much lower than that of the simplified physical model LPM. This means that designers can save a lot of time during code debugging and parameter tuning in a complex design process. Designers can reduce repeated calculation costs and obtain accurate optimal solutions by combining an ANN-based surrogate model with MOEAs.

Keywords: artificial neural network, solid rocket motor, multi-objective evolutionary algorithm, surrogate model

Procedia PDF Downloads 90

Authors: N. R. Putra, A. H. Abdul Aziz, A. S. Zaini, Z. Idham, F. Idrus, M. Z. Bin Zullyadini, M. A. Che Yunus

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The content of omega-3 in soybean oil is important in the development of infants and is an alternative for the omega-3 in fish oils. The investigation of extraction of soybean oil is needed to obtain the bioactive compound in the extract. Supercritical carbon dioxide extraction is modern and green technology to extract herbs and plants to obtain high quality extract due to high diffusivity and solubility of the solvent. The aim of this study was to obtain the optimum condition of soybean oil extraction by modified supercritical carbon dioxide. The soybean oil was extracted by using modified supercritical carbon dioxide (SC-CO₂) under the temperatures of 40, 60, 80 °C, pressures of 150, 250, 350 Bar, and constant flow-rate of 10 g/min as the parameters of extraction processes. An experimental design was performed in order to optimize three important parameters of SC-CO₂ extraction which are pressure (X₁), temperature (X₂) to achieve optimum yields of soybean oil. Box Behnken Design was applied for experimental design. From the optimization process, the optimum condition of extraction of soybean oil was obtained at pressure 338 Bar and temperature 80 °C with oil yield of 2.713 g. Effect of pressure is significant on the extraction of soybean oil by modified supercritical carbon dioxide. Increasing of pressure will increase the oil yield of soybean oil.

Keywords: soybean oil, SC-CO₂ extraction, yield, optimization

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Authors: A. Daftari, W. Kudla

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Liquefaction is a phenomenon in which the strength and stiffness of a soil is reduced by earthquake shaking or other rapid cyclic loading. Liquefaction and related phenomena have been responsible for huge amounts of damage in historical earthquakes around the world. Modelling of soil behaviour is the main step in soil liquefaction prediction process. Nowadays, several constitutive models for sand have been presented. Nevertheless, only some of them can satisfy this mechanism. One of the most useful models in this term is UBCSAND model. In this research, the capability of this model is considered by using PLAXIS software. The real data of superstition hills earthquake 1987 in the Imperial Valley was used. The results of the simulation have shown resembling trend of the UBC3D-PLM model.

Keywords: liquefaction, plaxis, pore-water pressure, UBC3D-PLM

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Authors: Yinglisong, Jingge Chen, Jingxuan Chen, Yan Wang, Hui Huang, Jing Zhnag, Qianqian Zhang, Zhenzhen Zhang, Ji Zhang

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Purpose: The study aims to establish a nomogram prediction model for postpartum hemorrhage (PPH) in vaginal delivery. Patients and Methods: Clinical data were retrospectively collected from vaginal delivery patients admitted to a hospital in Zhengzhou, China, from June 1, 2022 - October 31, 2022. Univariate and multivariate logistic regression were used to filter out independent risk factors. A nomogram model was established for PPH in vaginal delivery based on the risk factors coefficient. Bootstrapping was used for internal validation. To assess discrimination and calibration, receiver operator characteristics (ROC) and calibration curves were generated in the derivation and validation groups. Results: A total of 1340 cases of vaginal delivery were enrolled, with 81 (6.04%) having PPH. Logistic regression indicated that history of uterine surgery, induction of labor, duration of first labor, neonatal weight, WBC value (during the first stage of labor), and cervical lacerations were all independent risk factors of hemorrhage (P <0.05). The area-under-curve (AUC) of ROC curves of the derivation group and the validation group were 0.817 and 0.821, respectively, indicating good discrimination. Two calibration curves showed that nomogram prediction and practical results were highly consistent (P = 0.105, P = 0.113). Conclusion: The developed individualized risk prediction nomogram model can assist midwives in recognizing and diagnosing high-risk groups of PPH and initiating early warning to reduce PPH incidence.

Keywords: vaginal delivery, postpartum hemorrhage, risk factor, nomogram

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Authors: Abdelaziz Sheba Abdelrahman

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Compost enriched with actinomyces and Bacillus polymyxa algae as a Partial Substitute for Mineral N in Ewaise Mango Orchards Organic fertiliser, compost enriched with actinomyces, and the biofertilizer Bacillus polymyxa algae were used as a partial replacement for mineral N fertiliser in Ewaise mango orchards during the 2019 and 2020 seasons. When compared to using mineral N alone, the results showed that reducing the percentage of mineral N fertiliser from 100 to 50% and using compost enriched with actinomyces at 25 to 50% and Bacillus polymyxa had an announced promotion on leaf area, total chlorophylls, leaf N, P, and K, yield, and fruit quality. The use of compost enriched with actinomyces and Bacillus polymyxa, as well as mineral N, resulted in a significant decrease in nitrite in the pulp. Reducing mineral N to 25% of the suitable N had a negative impact on yield. The application of appropriate N via 50% inorganic N + compost enriched with actinomyces at 50% + Bacillus polymyxa algae increased yield quantitatively and qualitatively in Ewaise mango orchards. This promised treatment significantly reduced nitrite levels in the pulp fruit.

Keywords: bacillus polymyxa algae, fertiliser, biofertilizer, ewaise mango

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Authors: Takeyuki Suzuki, Ismiyarto, Da-Yang Zhou, Kaori Asano, Hiroaki Sasai

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The development of catalytic asymmetric reaction is important for the synthesis of natural products. To construct the multiple stereogenic centers, the desymmetrization of meso compounds is powerful strategy for the synthesis of chiral molecules. Oxidative desymmetrization of meso diols using chiral iridium catalyst provides a chiral hydroxyl ketone. The reaction is practical and an environmentally benign method which does not require the use of stoichiometric amount of heavy metals. This time we report here catalytic asymmetric synthesis of catalponol based on tandem coupling of meso-diols and an aldehyde. The tandem reaction includes oxidative desymmetrization of meso-diols, aldol condensation with an aldehyde. The reaction of meso-diol, benzaldehyde in the presence of a catalytic amount of chiral Ir complex and CsOH in tetrahydrofuran afforded the desired benzylidene ketone in 82% yield with 96% ee (enantiomeric excess). Next, we applied this benzylidene ketone derivative to the synthesis of catalponol. The corresponding benzylidene ketone was obtained in 87% yield with 99% ee. Finally, catalponol was synthesized by the regio- and stereo-selective reduction of dienone moiety in good yield.

Keywords: catalponol, desymmetrization, iridium, oxidation

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Authors: Soha A. Bahanshal, Vaibhav Verdhan, Bayong Kim

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Graduation rates at six-year colleges are becoming a more essential indicator for incoming fresh students and for university rankings. Predicting student graduation is extremely beneficial to schools and has a huge potential for targeted intervention. It is important for educational institutions since it enables the development of strategic plans that will assist or improve students' performance in achieving their degrees on time (GOT). A first step and a helping hand in extracting useful information from these data and gaining insights into the prediction of students' progress and performance is offered by machine learning techniques. Data analysis and visualization techniques are applied to understand and interpret the data. The data used for the analysis contains students who have graduated in 6 years in the academic year 2017-2018 for science majors. This analysis can be used to predict the graduation of students in the next academic year. Different Predictive modelings such as logistic regression, decision trees, support vector machines, Random Forest, Naïve Bayes, and KNeighborsClassifier are applied to predict whether a student will graduate. These classifiers were evaluated with k folds of 5. The performance of these classifiers was compared based on accuracy measurement. The results indicated that Ensemble Classifier achieves better accuracy, about 91.12%. This GOT prediction model would hopefully be useful to university administration and academics in developing measures for assisting and boosting students' academic performance and ensuring they graduate on time.

Keywords: prediction, decision trees, machine learning, support vector machine, ensemble model, student graduation, GOT graduate on time

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Authors: Fatma Zehra Engin Sagirli, Eyup Sabri Kayali, A. Sezai Sarac

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In this study, Poly (acrylonitrile-co-methylacrylate)/N-Methyl Pyrrole and Pyrrole ([P(AN-co-MA)]-NMPy and [P(AN-co-MA)]-PPy) core–shell nanoparticles were obtained by in situ emulsion polymerization in the presence of Sodium dodecyl benzene sulfonate and sodium dodecyl sulfate (SDBS and SDS) by using ammonium per sulphate in the aqueous medium. The spectroscopic characterizations during the formation of nanocomposites were studied using Attenuated total reflectance Fourier transform infrared (FTIR-ATR) spectroscopy, ultraviolet–visible spectrophotometer (Uv-Vis). Electrical conductivity of the emulsion solution was measured by Conductivity Meter from aqueous sample solution. Also, yield of the powder nanocomposites was measured. SDBS and SDS used for investigation of surfactant effect on yield, electrical conductivity and polymerization process. Determination of polymerization yield, (FTIR-ATR) and (Uv-Vis) prove that the SDBS surfactant become more incorporated into the conducting polymers and there is strong interaction between the [P(AN-co-MA)]-PPy derivatives which prepared by these surfactants. The similar inclusion of SDS into conducting polymers was not observed, there is a remarkable difference at nanocomposites which prepared with SDS.

Keywords: nanocomposites, core-shell, pyrole, surfactant

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Authors: T. S. Bubuche, L. Abubakar, N.D. Ibrahim, A. A. Aliero, H. M. Sama, B. S. Haliru

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A two-year study was conducted at the Fadama farm of Usmanu Danfodiyo University Sokoto, Nigeria. Correlations and path coefficients analysis were used to determine the interrelationship and importance of various characters as components of yield in sugarcane during 20011-012 and 2012-013 growing seasons. Fourteen sugarcane hybrids and a local check were evaluated. The experiment was laid out in a randomized complete block design (RCBD) and replicated three times. Significant and positive correlation were recorded between total cane weight/ha and single stalk weight, between single stalk weight and final brix and between stalk girth and stalk length while final brix and number of milliable cane/ha recorded no significant correlation. Traits that had high direct contribution to the final yield were number of stalk/stool, number of milliable cane/ha, single stalk weight and brix content while high indirect positive contributions were observed in growth habit, number of internode per stalk and stalk length..

Keywords: correlation, path analysis, sugarcane, yield components

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Authors: Lucia Steenkamp, Chris V. D. Westhuyzen, Kgama Mathiba

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Ambergris, and more specifically its oxidation product (–)-ambrafuran, is a scarce, valuable, and sought-after perfumery ingredient. The material is used as a fixative agent to stabilise perfumes in formulations by reducing the evaporation rate of volatile substances. Ambergris is a metabolic product of the sperm whale (Physeter macrocephatus L.), resulting from intestinal irritation. Chemically, (–)-ambrafuran is produced from the natural product sclareol in eight synthetic steps – in the process using harsh and often toxic chemicals to do so. An overall yield of no more than 76% can be achieved in some routes, but generally, this is lower. A new 'green' route has been developed in our laboratory in which sclareol, extracted from the Clary sage plant, is converted to (–)-ambrafuran in two steps with an overall yield in excess of 80%. The first step uses a microorganism, Hyphozyma roseoniger, to bioconvert sclareol to an intermediate diol using substrate concentrations up to 50g/L. The yield varies between 90 and 67% depending on the substrate concentration used. The purity of the diol product is 95%, and the diol is used without further purification in the next step. The intermediate diol is then cyclodehydrated to the final product (–)-ambrafuran using a zeolite, which is not harmful to the environment and is readily recycled. The yield of the product is 96%, and following a single recrystallization, the purity of the product is > 99.5%. A preliminary LC-MS study of the bioconversion identified several intermediates produced in the fermentation broth under oxygen-restricted conditions. Initially, a short-lived ketone is produced in equilibrium with a more stable pyranol, a key intermediate in the process. The latter is oxidised under Norrish type I cleavage conditions to yield an acetate, which is hydrolysed either chemically or under lipase action to afford the primary fermentation product, an intermediate diol. All the intermediates identified point to the likely CYP450 action as the key enzyme(s) in the mechanism. This invention is an exceptional example of how the power of biocatalysis, combined with a mild, benign chemical step, can be deployed to replace a total chemical synthesis of a specific chiral antipode of a commercially relevant material.

Keywords: ambrafuran, biocatalysis, fragrance, microorganism

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Authors: Najmeh Mohsenifar, Narjes Mohsenifar, Abbas Kargar

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In this paper, has been presented a stable method for predicting the ECG signals through the RBF neural networks, by the PSO algorithm. In spite of quasi-periodic ECG signal from a healthy person, there are distortions in electro cardiographic data for a patient. Therefore, there is no precise mathematical model for prediction. Here, we have exploited neural networks that are capable of complicated nonlinear mapping. Although the architecture and spread of RBF networks are usually selected through trial and error, the PSO algorithm has been used for choosing the best neural network. In this way, 2 second of a recorded ECG signal is employed to predict duration of 20 second in advance. Our simulations show that PSO algorithm can find the RBF neural network with minimum MSE and the accuracy of the predicted ECG signal is 97 %.

Keywords: electrocardiogram, RBF artificial neural network, PSO algorithm, predict, accuracy

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Authors: Yuichi Kida, Takuro Kida

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In a new smart society supported by the recent development of 5G and 6G Communication systems, the im- portance of wireless power transmission is increasing. These systems contain discrete sampling systems in the middle of the transmission path and equivalent circuit representation of lossless or lossy power transmission through these systems is an important issue in circuit theory. In this paper, for the given weight function, we show that a lossless power transmission system with the given weight is expressed by an equivalent circuit representation of the Kida’s optimal signal prediction system followed by a reactance multi-port circuit behind it. Further, it is shown that, when the system is lossy, the system has an equivalent circuit in the form of connecting a multi-port positive-real circuit behind the Kida’s optimal signal prediction system. Also, for the convenience of the reader, in this paper, the equivalent circuit expression of the reactance multi-port circuit and the positive- real multi-port circuit by Cauer and Ohno, whose information is currently being lost even in the world of the Internet.

Keywords: signal prediction, pseudo inverse matrix, artificial intelligence, power transmission

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Authors: Samih Abubaker, Amjad Abuserhan, Ghandi Anfoka

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Potato yield reduction and soil salt accumulation are the main obstacles of using brackish water in irrigation. This study was carried out at Al- Balqa` Applied University research station, to investigate the impact of compost use on potato production and salt accumulation in the soil under brackish water, during 2014 growing season. Whole tubers of three imported potato cultivars (Spunta, Faluka and Ammbetion) were planted in pots with different soil and compost percentages (0, 20, 40, 60, 80, and 100%) and were irrigated with three water salinity levels (1.25, 5 and 10 ds/cm). A split-split plot design was used, where potato cultivars were arranged in the main plots, the brackish water treatments were in the sub-main and the soil amended treatments were in the sub-sub plots. Potato yield was generally decreased only when pots were irrigated by water of 10 ds/cm salinity compared with 1.25 and 5 ds/cm. Drainage water salinity, however, was increased as compost percentage increased. Nevertheless, salt accumulation in the growing media was decreased as the compost percentage level increased. Therefore, it can be concluded that brackish water, up to 5 ds/cm can be used to irrigate potato especially, when organic amendments were added to the soil to promote plant growth, yield and reduce salt accumulation.

Keywords: brackish water, compost, potato, salt accumulation

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Authors: Khin Aye Myint, Mohd Rafii Yusop, Mohd Yusoff Abd Samad, Shairul Izan Ramlee, Mohd Din Amiruddin, Zulkifli Yaakub

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The narrow base of genetic is the main obstacle of breeding and genetic improvement in oil palm industry. In order to broaden the genetic bases, the Malaysian Palm Oil Board has been extensively collected wild germplasm from its original area of 11 African countries which are Nigeria, Senegal, Gambia, Guinea, Sierra Leone, Ghana, Cameroon, Zaire, Angola, Madagascar, and Tanzania. The germplasm collections were established and maintained as a field gene bank in Malaysian Palm Oil Board (MPOB) Research Station in Kluang, Johor, Malaysia to conserve a wide range of oil palm genetic resources for genetic improvement of Malaysian oil palm industry. Therefore, assessing the performance and genetic diversity of the wild materials is very important for understanding the genetic structure of natural oil palm population and to explore genetic resources. Principal component analysis (PCA) and Cluster analysis are very efficient multivariate tools in the evaluation of genetic variation of germplasm and have been applied in many crops. In this study, eight populations of MPOB-Senegal oil palm germplasm were studied to explore the genetic variation pattern using PCA and cluster analysis. A total of 20 yield and yield component traits were used to analyze PCA and Ward’s clustering using SAS 9.4 version software. The first four principal components which have eigenvalue >1 accounted for 93% of total variation with the value of 44%, 19%, 18% and 12% respectively for each principal component. PC1 showed highest positive correlation with fresh fruit bunch (0.315), bunch number (0.321), oil yield (0.317), kernel yield (0.326), total economic product (0.324), and total oil (0.324) while PC 2 has the largest positive association with oil to wet mesocarp (0.397) and oil to fruit (0.458). The oil palm population were grouped into four distinct clusters based on 20 evaluated traits, this imply that high genetic variation existed in among the germplasm. Cluster 1 contains two populations which are SEN 12 and SEN 10, while cluster 2 has only one population of SEN 3. Cluster 3 consists of three populations which are SEN 4, SEN 6, and SEN 7 while SEN 2 and SEN 5 were grouped in cluster 4. Cluster 4 showed the highest mean value of fresh fruit bunch, bunch number, oil yield, kernel yield, total economic product, and total oil and Cluster 1 was characterized by high oil to wet mesocarp, and oil to fruit. The desired traits that have the largest positive correlation on extracted PCs could be utilized for the improvement of oil palm breeding program. The populations from different clusters with the highest cluster means could be used for hybridization. The information from this study can be utilized for effective conservation and selection of the MPOB-Senegal oil palm germplasm for the future breeding program.

Keywords: cluster analysis, genetic variability, germplasm, oil palm, principal component analysis

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Authors: Omar M. Elmabrouk

Abstract:

The cutting tool, in the high-speed machining process, is consistently dealing with high localized stress at the tool tip, tip temperature exceeds 800°C and the chip slides along the rake face. These conditions are affecting the tool wear, the cutting tool performances, the quality of the produced parts and the tool life. Therefore, a thin film coating on the cutting tool should be considered to improve the tool surface properties while maintaining its bulks properties. One of the general coating processes in applying thin film for hard coating purpose is PVD magnetron sputtering. In this paper, the prediction of the effects of PVD magnetron sputtering coating process parameters, sputter power in the range of (4.81-7.19 kW), bias voltage in the range of (50.00-300.00 Volts) and substrate temperature in the range of (281.08-600.00 °C), were studied using artificial neural network (ANN). The results were compared with previously published results using RSM model. It was found that the ANN is more accurate in prediction of tool hardness, and hence, it will not only improve the tool life of the tool but also significantly enhances the efficiency of the machining processes.

Keywords: artificial neural network, hardness, prediction, titanium aluminium nitrate coating

Procedia PDF Downloads 554

Authors: S. R. M. Krishna, R. Santosh Kumar, V. Kamakshi Prasad

Abstract:

Machine learning techniques are applied for the analysis of the impact of mental illness on the burden of disease. It is calculated using the disability-adjusted life year (DALY). DALYs for a disease is the sum of years of life lost due to premature mortality (YLLs) + No of years of healthy life lost due to disability (YLDs). The critical analysis is done based on the Data sources, machine learning techniques and feature extraction method. The reviewing is done based on major databases. The extracted data is examined using statistical analysis and machine learning techniques were applied. The prediction of the impact of mental illness on the population using machine learning techniques is an alternative approach to the old traditional strategies, which are time-consuming and may not be reliable. The approach makes it necessary for a comprehensive adoption, innovative algorithms, and an understanding of the limitations and challenges. The obtained prediction is a way of understanding the underlying impact of mental illness on the health of the people and it enables us to get a healthy life expectancy. The growing impact of mental illness and the challenges associated with the detection and treatment of mental disorders make it necessary for us to understand the complete effect of it on the majority of the population.

Keywords: ML, DAL, YLD, YLL

Procedia PDF Downloads 36

Authors: A. Elgendi, H. Farag, M. E. Ossman, M. Abd-Elfatah

Abstract:

This paper explores the using of waste cooking oil (WCO) as an attractive option to reduce the raw material cost for the biodiesel production. This can be achieved through two steps; esterification using g-C3N4photocatalyst and then alkali transesterification. Several parameters have been studied to determine the yield of the biodiesel produced such as: Reaction time (2-6 hrs), catalyst concentration (0.3-1.5 wt.%), number of UV lamps (1or 3 lamps) and methanol: oil ratio (6:1-12:1). From the obtained results, the highest percentage yield was obtained using methanol: Oil molar ratio of 12:1, catalyst dosage 0.3%, time of 4 hrs and using 1 lamp. From the results it was clear that the produced biodiesel from waste cooking oil can be used as fuel.

Keywords: biodiesel, heterogeneous catalyst, photocatalytic esterification, waste cooking oil

Procedia PDF Downloads 528

Authors: Waqas Ahmed Khan Afridi, Subhas Chandra Mukhopadhyay, Bandita Mainali

Abstract:

The research paper presents a unique approach to evapotranspiration (ET) prediction using a Support Vector Machine (SVM) learning algorithm. The study leverages real-time sensor node data to develop an accurate and adaptable prediction model, addressing the inherent challenges of traditional ET estimation methods. The integration of the SVM algorithm with real-time sensor node data offers great potential to improve spatial and temporal resolution in ET predictions. In the model development, key input features are measured and computed using mathematical equations such as Penman-Monteith (FAO56) and soil water balance (SWB), which include soil-environmental parameters such as; solar radiation (Rs), air temperature (T), atmospheric pressure (P), relative humidity (RH), wind speed (u2), rain (R), deep percolation (DP), soil temperature (ST), and change in soil moisture (∆SM). The one-year field data are split into combinations of three proportions i.e. train, test, and validation sets. While kernel functions with tuning hyperparameters have been used to train and improve the accuracy of the prediction model with multiple iterations. This paper also outlines the existing methods and the machine learning techniques to determine Evapotranspiration, data collection and preprocessing, model construction, and evaluation metrics, highlighting the significance of SVM in advancing the field of ET prediction. The results demonstrate the robustness and high predictability of the developed model on the basis of performance evaluation metrics (R2, RMSE, MAE). The effectiveness of the proposed model in capturing complex relationships within soil and environmental parameters provide insights into its potential applications for water resource management and hydrological ecosystem.

Keywords: evapotranspiration, FAO56, KNIME, machine learning, RStudio, SVM, sensors

Procedia PDF Downloads 69

Authors: Vaidehi Iyer, Konstantin Borozdin

Abstract:

Time series prediction problems have many important practical applications, but are notoriously difficult for statistical modeling. Recently, machine learning methods have been attracted significant interest as a practical tool applied to a variety of problems, even though developments in this field tend to be semi-empirical. This paper explores application of Long Short Term Memory based Recurrent Neural Networks to the one-step prediction of time series for both trend and stochastic components. Two types of data are analyzed - daily stock prices, that are often considered to be a typical example of a random walk, - and weather patterns dominated by seasonal variations. Results from both analyses are compared, and reinforced learning framework is used to select more efficient between Recurrent Neural Networks and more traditional auto regression methods. It is shown that both methods are able to follow long-term trends and seasonal variations closely, but have difficulties with reproducing day-to-day variability. Future research directions and potential real world applications are briefly discussed.

Keywords: long short term memory, prediction methods, recurrent neural networks, reinforcement learning

Procedia PDF Downloads 228

Authors: Jens Friedrich, Matthias A. Gebele, Armin Lechler, Alexander Verl

Abstract:

Chatter vibrations and process instabilities are the most important factors limiting the productivity of the milling process. Chatter can leads to damage of the tool, the part or the machine tool. Therefore, the estimation and prediction of the process stability is very important. The process stability depends on the spindle speed, the depth of cut and the width of cut. In milling, the process conditions are defined in the NC-program. While the spindle speed is directly coded in the NC-program, the depth and width of cut are unknown. This paper presents a new simulation based approach for the prediction of the depth and width of cut of a milling process. The prediction is based on a material removal simulation with an analytically represented tool shape and a multi-dexel approach for the work piece. The new calculation method allows the direct estimation of the depth and width of cut, which are the influencing parameters of the process stability, instead of the removed volume as existing approaches do. The knowledge can be used to predict the stability of new, unknown parts. Moreover with an additional vibration sensor, the stability lobe diagram of a milling process can be estimated and improved based on the estimated depth and width of cut.

Keywords: dexel, process stability, material removal, milling

Procedia PDF Downloads 525

Authors: Diqin Qi, Jiaming Li, Siman Li

Abstract:

Based on the use of the three-point smoothing method for selectively processing original data columns, this paper establishes a group of grey GM(1,1) models to predict the concentration ranges of four major air pollutants in Shanghai from 2023 to 2024. The results indicate that PM₁₀, SO₂, and NO₂ maintain the national Grade I standards, while the concentration of PM₂.₅ has decreased but still remains within the national Grade II standards. Combining the forecast results, recommendations are provided for the Shanghai municipal government's efforts in air pollution prevention and control.

Keywords: atmospheric pollutant prediction, Grey GM(1, 1), model group, three-point smoothing method

Procedia PDF Downloads 35

Authors: K. Benyounes, A. Benmounah

Abstract:

Cement-based grouts has been used successfully to repair cracks in many concrete structures such as bridges, tunnels, buildings and to consolidate soils or rock foundations. In the present study, the rheological characterization of cement grout with water/binder ratio (W/B) is fixed at 0.5. The effect of the replacement of cement by bentonite (2 to 10 % wt) in presence of superplasticizer (0.5 % wt) was investigated. Several rheological tests were carried out by using controlled-stress rheometer equipped with vane geometry in temperature of 20°C. To highlight the influence of bentonite and superplasticizer on the rheological behavior of grout cement, various flow tests in a range of shear rate from 0 to 200 s-1 were observed. Cement grout showed a non-Newtonian viscosity behavior at all concentrations of bentonite. Three parameter model Herschel-Bulkley was chosen for fitting of experimental data. Based on the values of correlation coefficients of the estimated parameters, The Herschel-Bulkley law model well described the rheological behavior of the grouts. Test results showed that the dosage of bentonite increases the viscosity and yield stress of the system and introduces more thixotropy. While the addition of both bentonite and superplasticizer with cement grout improve significantly the fluidity and reduced the yield stress due to the action of dispersion of SP.

Keywords: rheology, cement grout, bentonite, superplasticizer, viscosity, yield stress

Procedia PDF Downloads 361