Search results for: tomato yield prediction

Authors: Areej Javaid, Nishat Fatima, Mehwish Naseer

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There is a tremendous use of biofertilizers in agriculture to increase crop productivity. Pakistan spends a huge amount on the purchase of synthetic fertilizers every year. The use of natural compounds to harness crop productivity is the major area of interest nowadays due to being safe for human health and the environment as well. Legumes have the intrinsic quality to enrich the nutrient status of soil because of the presence of nitrogen fixation bacteria on nodules. This research determined the effect of biofertilizer on nodulation attributes and yield of the pea plant. Seeds of pea varieties were treated with a slurry of biofertilizer prepared in a 10% sugar solution just before seed sowing. The impact of biofertilizer on different parameters of growth, yield and nodulation was observed. Analysis of variance showed that plant height, days to flowering, number of nodes, days to first pod, root length and plant height exhibited significant genetic variation. All the yield parameters, including the number of pods per plant, number of seeds per pod, seed fresh and dry weight showed significant results under treatment. Among nodulation parameters, nodule number responded positively to biofertilizer treatment. Genotypes 2001-40 showed better performance followed by 2001-20 and LINA-PAK in all the parameters, whereas 2001-40 and 2001-20 performed well in nodulation and yield parameters. Consequently, seed treatment with biofertilizer before sowing is recommended to obtain higher crop yield.

Keywords: biological nitrogen fixation, correlation analysis, quantitative inheritance, varietal responses

Procedia PDF Downloads 146

Authors: Joseph C. Chen, Venkata Mohan Kudapa

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Development of two real-time surface roughness (Ra) prediction systems for milling operations was attempted. The systems used not only cutting parameters, such as feed rate and spindle speed, but also the cutting current generated and corrected by a clamp type energy sensor. Two different approaches were developed. First, a fuzzy inference system (FIS), in which the fuzzy logic rules are generated by experts in the milling processes, was used to conduct prediction modeling using current cutting data. Second, a neuro-fuzzy system (ANFIS) was explored. Neuro-fuzzy systems are adaptive techniques in which data are collected on the network, processed, and rules are generated by the system. The inference system then uses these rules to predict Ra as the output. Experimental results showed that the parameters of spindle speed, feed rate, depth of cut, and input current variation could predict Ra. These two systems enable the prediction of Ra during the milling operation with an average of 91.83% and 94.48% accuracy by FIS and ANFIS systems, respectively. Statistically, the ANFIS system provided better prediction accuracy than that of the FIS system.

Keywords: surface roughness, input current, fuzzy logic, neuro-fuzzy, milling operations

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Authors: Vuk M. Popovic, Dunja D. Popovic

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Software maintenance phase is started once a software project has been developed and delivered. After that, any modification to it corresponds to maintenance. Software maintenance involves modifications to keep a software project usable in a changed or a changing environment, to correct discovered faults, and modifications, and to improve performance or maintainability. Software maintenance and management of software maintenance are recognized as two most important and most expensive processes in a life of a software product. This research is basing the prediction of maintenance, on risks and time evaluation, and using them as data sets for working with neural networks. The aim of this paper is to provide support to project maintenance managers. They will be able to pass the issues planned for the next software-service-patch to the experts, for risk and working time evaluation, and afterward to put all data to neural networks in order to get software maintenance prediction. This process will lead to the more accurate prediction of the working hours needed for the software-service-patch, which will eventually lead to better planning of budget for the software maintenance projects.

Keywords: laboratory information system, maintenance engineering, neural networks, software maintenance, software maintenance costs

Procedia PDF Downloads 351

Authors: Jeff Clarine, Chang-Shyh Peng, Daisy Sang

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Bioassay is the measurement of the potency of a chemical substance by its effect on a living animal or plant tissue. Bioassay data and chemical structures from pharmacokinetic and drug metabolism screening are mined from and housed in multiple databases. Bioassay prediction is calculated accordingly to determine further advancement. This paper proposes a four-step preprocessing of datasets for improving the bioassay predictions. The first step is instance selection in which dataset is categorized into training, testing, and validation sets. The second step is discretization that partitions the data in consideration of accuracy vs. precision. The third step is normalization where data are normalized between 0 and 1 for subsequent machine learning processing. The fourth step is feature selection where key chemical properties and attributes are generated. The streamlined results are then analyzed for the prediction of effectiveness by various machine learning algorithms including Pipeline Pilot, R, Weka, and Excel. Experiments and evaluations reveal the effectiveness of various combination of preprocessing steps and machine learning algorithms in more consistent and accurate prediction.

Keywords: bioassay, machine learning, preprocessing, virtual screen

Procedia PDF Downloads 271

Authors: A. Abbas Khalaf, L. Issazadeh, Z. Arif Abdullah, J. Hassanpour

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In order to evaluate the growth and yield properties of two Sorghum-Sudangrass hybrids under different irrigation levels, an investigation was done in the experiment site of Collage of Agriculture, University of Duhok, Kurdistan region of Iraq (36°5´38^⸗ N, 42°52´02^⸗ E) in the years 2015-16. The experiment was conducted under Randomized Complete Block Design (RCBD) with three replications, which main factor was irrigation treatments (I₁₀₀, I₇₅ and I₅₀) according to evaporation pan class A and type of Sorghum-Sudangrass hybrids (KH12SU9001, G₁) and (KH12SU9002, G₂) were factors of subplots. The parameters studied were: plant height (cm), number of green leaves per plant; leaf area (m²/m²), stem thickness (mm), percent of protein, fresh and dry biomass (ton.ha^-1) and also crop water productivity. The results of variance analysis showed that KH12SU9001 variety had more amount of leaf area, percent of protein, fresh and dry biomass yield in comparison to KH12SU9002 variety. By comparing effects of irrigation levels on vegetative growth and yield properties, results showed that amount of plant height, fresh and dry biomass weight was decreased by decreasing irrigation level from full irrigation regime to 5 _o% of irrigation level. Also, results of crop water productivity (CWP) indicated that improvement in quantity of irrigation would impact fresh and dry biomass yield significantly. Full irrigation regime was recorded the highest level of CWP (1.28-1.29 kg.m^-3).

Keywords: deficit irrigation, growth, sorghum-sudangrass hybrid, yield

Procedia PDF Downloads 134

Authors: Anne-Lena Kampen, Øivind Kure

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Machine learning (ML) can be implemented in Wireless Sensor Networks (WSNs) as a central solution or distributed solution where the ML is embedded in the nodes. Embedding improves privacy and may reduce prediction delay. In addition, the number of transmissions is reduced. However, quality factors such as prediction accuracy, fault detection efficiency and coordinated control of the overall system suffer. Here, we discuss and highlight the trade-offs that should be considered when choosing between embedding and centralized ML, especially for multihop networks. In addition, we present estimations that demonstrate the energy trade-offs between embedded and centralized ML. Although the total network energy consumption is lower with central prediction, it makes the network more prone for partitioning due to the high forwarding load on the one-hop nodes. Moreover, the continuous improvements in the number of operations per joule for embedded devices will move the energy balance toward embedded prediction.

Keywords: central machine learning, embedded machine learning, energy consumption, local machine learning, wireless sensor networks, WSN

Procedia PDF Downloads 145

Authors: H. B. Torane, M. C. Kasture, S. S. Prabhudesai, P. B. Sanap, V. N. Palsande, J. J. Palkar

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The present investigation entitled “Response of briquettes application with different coating materials on yield and quality of Cucumber [Cucumis sativus (L.)]” was conducted at Central Experiment Center, Wakawali during kharif season 2013. The field experiment was laid out in Factorial Randomized Block Design with three replicate. The four coating materials viz., Co – Non coating, C1 – Wax coating, C2 – Jaggary coating, and C3 – Tar coating was applied to Konkan Annapurna Briquette along with three sub treatments of application time i.e B1 – ½ at sowing, B2 - ½ at sowing and ½ at 30 days after sowing and B3 - 1/3 at sowing, 1/3 at 30 days after sowing and 1/3 at 60 days after sowing. It was observed that the application of tar coated Konkan Annapurna Briquettes (KAB) in three times @1/3 quantity of briquettes at sowing time, 1/3 quantity of briquettes at 30 days after sowing and 1/3 quantity of briquettes at 60 days after sowing was found promising to enhancing the cucumber fruit yield, higher vine length, number of fruits vine-1, girth of fruit, length of fruit. It was also observed that the quality of the cucumber fruit increased in terms of ascorbic acid. UB-Godavari forms of briquettes .i.e. are promising source of N, P2O5 and K2O fertilizers as compared to straight fertilizers for enhancing green cucumber fruit yield of Sheetal variety of cucumber in lateritic soil. Amongst the three types of coated briquettes, the tar coated briquettes application was found to be superior for increasing cucumber fruit yield applied in three times @1/3 quantity of briquettes at sowing time, 1/3 quantity of briquettes at 30 days after sowing and 1/3 quantity of briquettes at 60 days after sowing @ 5 briquettes per plant at an interval of 30 days after sowing.

Keywords: briquettes, coating, yield, tar, wax and quality

Procedia PDF Downloads 507

Authors: Mansoureh Naderipour, Susan Bastani, Mohammad Fazel Zarandi

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Predicting links that may occur in the future and missing links in social networks is an attractive problem in social network analysis. Granular computing can help us to model the relationships between human-based system and social sciences in this field. In this paper, we present a model based on granular computing approach and Type-2 fuzzy logic to predict links regarding nodes’ activity and the relationship between two nodes. Our model is tested on collaboration networks. It is found that the accuracy of prediction is significantly higher than the Type-1 fuzzy and crisp approach.

Keywords: social network, link prediction, granular computing, type-2 fuzzy sets

Procedia PDF Downloads 320

Authors: Gunasekaran Raja, Rajakumar Arul, Kottilingam Kottursamy, Ramkumar Jayaraman, Sathya Pavithra, Swaminathan Venkatraman

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Wireless Interoperability for Microwave Access (WiMAX) utilizes the IEEE 802.1X mechanism for authentication. However, this mechanism incurs considerable delay during handoffs. This delay during handoffs results in service disruption which becomes a severe bottleneck. To overcome this delay, our article proposes a key caching mechanism based on user path prediction. If the user mobility follows that path, the user bypasses the normal IEEE 802.1X mechanism and establishes the necessary authentication keys directly. Through analytical and simulation modeling, we have proved that our mechanism effectively decreases the handoff delay thereby achieving fast authentication.

Keywords: authentication, authorization, and accounting (AAA), handoff, mobile, user path prediction (UPP) and user pattern

Procedia PDF Downloads 395

Authors: Ghufran Ahmed, Muhammad Osama Siddiqui, Shahbaz Siddiqui, Rauf Ahmad Shams Malick, Faisal Khan, Mubashir Khan

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In this paper, a machine learning-based approach for early prediction of diseases in cows is proposed. Different ML algos are applied to extract useful patterns from the available dataset. Technology has changed today’s world in every aspect of life. Similarly, advanced technologies have been developed in livestock and dairy farming to monitor dairy cows in various aspects. Dairy cattle monitoring is crucial as it plays a significant role in milk production around the globe. Moreover, it has become necessary for farmers to adopt the latest early prediction technologies as the food demand is increasing with population growth. This highlight the importance of state-ofthe-art technologies in analyzing how important technology is in analyzing dairy cows’ activities. It is not easy to predict the activities of a large number of cows on the farm, so, the system has made it very convenient for the farmers., as it provides all the solutions under one roof. The cattle industry’s productivity is boosted as the early diagnosis of any disease on a cattle farm is detected and hence it is treated early. It is done on behalf of the machine learning output received. The learning models are already set which interpret the data collected in a centralized system. Basically, we will run different algorithms on behalf of the data set received to analyze milk quality, and track cows’ health, location, and safety. This deep learning algorithm draws patterns from the data, which makes it easier for farmers to study any animal’s behavioral changes. With the emergence of machine learning algorithms and the Internet of Things, accurate tracking of animals is possible as the rate of error is minimized. As a result, milk productivity is increased. IoT with ML capability has given a new phase to the cattle farming industry by increasing the yield in the most cost-effective and time-saving manner.

Keywords: IoT, machine learning, health care, dairy cows

Procedia PDF Downloads 60

Authors: Olena Kavats, Dmitry Khramov, Kateryna Sergieieva, Vladimir Vasyliev, Iurii Kavats

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Geoinformation technologies of space agromonitoring are a means of operative decision making support in the tasks of managing the agricultural sector of the economy. Existing technologies use satellite images in the optical range of electromagnetic spectrum. Time series of optical images often contain gaps due to the presence of clouds and haze. A geoinformation technology is created. It allows to fill gaps in time series of optical images (Sentinel-2, Landsat-8, PROBA-V, MODIS) with radar survey data (Sentinel-1) and use information about agrometeorological conditions of the growing season for individual monitoring years. The technology allows to perform crop classification and mapping for spring-summer (winter and spring crops) and autumn-winter (winter crops) periods of vegetation, monitoring the dynamics of crop state seasonal changes, crop yield forecasting. Crop classification is based on supervised classification algorithms, takes into account the peculiarities of crop growth at different vegetation stages (dates of sowing, emergence, active vegetation, and harvesting) and agriculture land state characteristics (row spacing, seedling density, etc.). A catalog of samples of the main agricultural crops (Ukraine) is created and crop spectral signatures are calculated with the preliminary removal of row spacing, cloud cover, and cloud shadows in order to construct time series of crop growth characteristics. The obtained data is used in grain crop growth tracking and in timely detection of growth trends deviations from reference samples of a given crop for a selected date. Statistical models of crop yield forecast are created in the forms of linear and nonlinear interconnections between crop yield indicators and crop state characteristics (temperature, precipitation, vegetation indices, etc.). Predicted values of grain crop yield are evaluated with an accuracy up to 95%. The developed technology was used for agricultural areas monitoring in a number of Great Britain and Ukraine regions using EOS Crop Monitoring Platform (https://crop-monitoring.eos.com). The obtained results allow to conclude that joint use of Sentinel-1 and Sentinel-2 images improve separation of winter crops (rapeseed, wheat, barley) in the early stages of vegetation (October-December). It allows to separate successfully the soybean, corn, and sunflower sowing areas that are quite similar in their spectral characteristics.

Keywords: geoinformation technology, crop classification, crop yield prediction, agricultural monitoring, EOS Crop Monitoring Platform

Procedia PDF Downloads 447

Authors: Mahdi Nasiri Tabrizi, A. Dadkhah, M. Khirkhah

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In order to study the effect of planting on yield, yield components and quality traits in bread and durum wheat varieties, a field split-plot experiment based on complete randomized design with three replications was conducted in Agricultural and Natural Resources Research Center of Razavi Khorasan located in city of Mashhad during 2013-2014. Main factor were consisted of five sowing dates (first October, fifteenth December, first March, tenth March, twentieth March) and as sub-factors consisted of different bread wheat (Bahar, Pishgam, Pishtaz, Mihan, Falat and Karim) and two durum wheat (Dena and Dehdasht). According to results of analysis variance the effect of planting date was significant on all examined traits (grain yield, biological yield, harvest index, number of grain per spike, thousands kernel weight, number of spike per square meter, plant height, the number of days to heading, the number of days to maturity, during the grain filling period, percentage of wet gluten, percentage of dry gluten, gluten index, percentage of protein). By delay in planting, majority of traits significantly decreased, except quality traits (percentage of wet gluten, percentage of dry gluten and percentage of protein). Results of means comparison showed, among planting date the highest grain yield and biological yield were related to first planting date (Octobr) with mean of production of 5/6 and 1/17 tons per hectare respectively and the highest bread quality (gluten index) with mean of 85 and percentage of protein with mean of 13% to fifth planting date also the effect of genotype was significant on all traits. The highest grain yield among of studied wheat genotypes was related to Dehdasht cultivar with an average production of 4.4 tons per hectare. The highest protein percentage and bread quality (gluten index) were related to Dehdasht cultivar with 13.4% and Falat cultivar with number of 90 respectively. The interaction between cultivar and planting date was significant on all traits and different varieties had different trend for these traits. The highest grain yield was related to first planting date (October) and Falat cultivar with an average of production of 6/7 tons per hectare while in grain yield did not show a significant different with Pishtas and Mihan cultivars also the most of gluten index (bread quality index) and protein percentage was belonged to the third planting date and Karim cultivar with 7.98 and Dena cultivar with 7.14% respectively.

Keywords: yield component, yield, planting date, cultivar, quality traits, wheat

Procedia PDF Downloads 422

Authors: Golaleh Ghaffari

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A basin sediment yield refers to the amount of sediment exported by a basin over a period of time, which will enter a reservoir located at the downstream limit of the basin. The Soil and Water Assessment Tool (SWAT, 2008) was used to hydrology and sediment transport modeling at daily and monthly time steps within the Gheshlagh dam basin in north-west of Iran. The SWAT model and Geographic Information System (GIS) techniques were applied to evaluate basin hydrology and sediment yield using historical flow and sediment data and to identify and prioritize critical sub-basins based on sediment transport. The results of this study indicated that simulated daily discharge and sediment values matched the observed values satisfactorily. The model predicted that mean annual basin precipitation for the total study period (413 mm) was partitioned in to evapotranspiration (36%), percolation/groundwater recharge (21%) and stream water (25%), yielding 18% surface runoff. Potential source areas of erosion were also identified with the model. The range of the annual contributing erosive zones varied spatially from 0.1 to 103 t/ha according to the slope and land use at the basin scale. Also the fifteen sub basins create the 60% of the total sediment yield between the all (102) sub basins. The results of the study indicated that SWAT can be a useful tool for assessing hydrology and sediment yield response of the watersheds in the region.

Keywords: erosion, Gheshlagh dam, sediment yield, SWAT

Procedia PDF Downloads 517

Authors: Wali Ullah, Muhammad Nishat

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The study investigates the extent to which the so called Nelson-Siegel model (DNS) and its extended version that accounts for time varying volatility (DNS-EGARCH) can optimally fit the yield curve and predict its future path in the context of an emerging economy. For the in-sample fit, both models fit the curve remarkably well even in the emerging markets. However, the DNS-EGARCH model fits the curve slightly better than the DNS. Moreover, both specifications of yield curve that are based on the Nelson-Siegel functional form outperform the benchmark VAR forecasts at all forecast horizons. The DNS-EGARCH comes with more precise forecasts than the DNS for the 6- and 12-month ahead forecasts, while the two have almost similar performance in terms of RMSE for the very short forecast horizons.

Keywords: yield curve, forecasting, emerging markets, Kalman filter, EGARCH

Procedia PDF Downloads 532

Authors: M. Jahanandish, M. B. Zeydabadinejad

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In this paper, von Mises and Drucker-Prager yield criteria, as typical ones that consider the effect of intermediate principal stress σ2, have been selected and employed for investigating the influence of σ2 on the solution of a typical stability problem. The bearing capacity factors have been calculated under plane strain condition (strip footing) and axisymmetric condition (circular footing) using the method of stress characteristics together with the criteria mentioned. Different levels of σ2 relative to the other two principal stresses have been considered. While a higher σ2 entry in yield criterion gives a higher bearing capacity; its entry in equilibrium equations (axisymmetric) causes substantial reduction.

Keywords: intermediate principal stress, plane strain, axisymmetric, yield criteria

Procedia PDF Downloads 458

Authors: Abdollah Javanmard, Fariborz Shekari

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Barley (Hordeum vulgare L.), vetch (Vicia villosa), and grass pea (Lathyrus sativus L.) monocultures as well as mixtures of barley with each of the above legumes, in three seeding ratios (i.e., barley: legume 75:25, 50:50 and 25:75 based on seed numbers) were used to investigate forage yield and competition indices. The results showed that intercropping reduced the dry matter yield of the three component plants, compared with their respective monocrops. The greatest value of total dry matter yield was obtained from barley25-grasspea75 (5.44 t ha-1) mixture, followed by grass pea sole crop (4.99 t ha-1). The total AYL values were positive and greater than 0 in all mixtures, indicating an advantage from intercropping over sole crops. Intercropped barley had a higher relative crowding coefficient (K=1.64) than intercropped legumes (K=1.20), indicating that barley was more competitive than legumes in mixtures. Furthermore, grass pea was more competitive than vetch in mixtures with barley. The highest LER, SPI and MAI were obtained when barley was mixed at a rate of 25% with 75% seed rate of grass pea. It is concluded that intercropping of barley with grass pea has a good potential to improve the performance of forage with high land-use efficiency.

Keywords: forage, grass pea, intercropping, LER, monetary advantage

Procedia PDF Downloads 382

Authors: Kiyoumars Roushangar, Saeid Sadaghian

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Although accurate sediment load prediction is very important in planning, designing, operating and maintenance of water resources structures, the transport mechanism is complex, and the deterministic transport models are based on simplifying assumptions often lead to large prediction errors. In this research, firstly, two intelligent ANN methods, Radial Basis and General Regression Neural Networks, are adopted to model of total sediment load transport into Madani Dam reservoir (north of Iran) using the measured data and then applicability of the sediment transport methods developed by Engelund and Hansen, Ackers and White, Yang, and Toffaleti for predicting of sediment load discharge are evaluated. Based on comparison of the results, it is found that the GRNN model gives better estimates than the sediment rating curve and mentioned classic methods.

Keywords: sediment transport, dam reservoir, RBF, GRNN, prediction

Procedia PDF Downloads 492

Authors: Hemlata Chitte, Anita Chorey, V. M. Bhale, Bharti Tijare

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A field experiment was conducted during kharif season of 2013-14 at Agronomy research farm, Dr. PDKV, Akola, to study the productivity and nitrogen use efficiency in cotton using organic nitrification inhibitors. The experiment was laid out in factorial randomized block design with three replications each having nine treatment combinations comprising three fertilizer levels viz., 75% RDF (F1), 100% RDF (F2) and 125% RDF (F3) and three nitrification inhibitors viz., neem cake @ 300 kgha-1 (N1), karanj cake @ 300 kgha-1 (N2) and control (N3). The result showed that various growth attributes viz., plant height, number of functional leaves plant-1, monopodial and sympodial branches and leaf area plant-1(dm2) were maximum in fertilizer level 125% RDF over fertilizer level 75% RDF and which at par with 100% RDF. In case of yield attributes and yield, number of bolls per plant, Seed cotton yield and stalk yield kg ha-1 significantly higher in fertilizer level 125% RDF over 100% RDF and 75% RDF. Uptake of NPK kg ha-1 after harvest of cotton crop was significantly higher in fertilizer level 125% RDF over 100% RDF and 75% RDF. Significantly highest nitrogen use efficiency was recorded with fertilizer level 75 % RDF as compared to 100 % RDF and lowest nitrogen use efficiency was recorded with 125% RDF level. Amongst nitrification inhibitors, karanj cake @ 300 kg ha-1 increases potentiality of growth characters, yield attributes, uptake of NPK and NUE as compared to control and at par with neem cake @ 300 kgha-1. Interaction effect between fertilizer level and nitrification inhibitors were found to be non significant at all growth attributes and uptake of nutrient but was significant in respect of seed cotton yield.

Keywords: cotton, fertilizer level, nitrification inhibitor and nitrogen use efficiency, nutrient uptake

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Authors: Tony K. Joseph, Balasubramanian Vathilingam, Stephane Morin

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Currently, the refiners around the world are more focused on improving the yield of light olefins (propylene and ethylene) as both of them are very prominent raw materials to produce wide spectrum of polymeric materials such as polyethylene and polypropylene. Henceforth, it is desirable to increase the yield of light olefins via selective cracking of heavy oil fractions. In this study, zeolite grown on SiC was used as the catalyst to do model cracking reaction of n-heptane. The catalytic cracking of n-heptane was performed in a fixed bed reactor (12 mm i.d.) at three different temperatures (425, 450 and 475 °C) and at atmospheric pressure. A carrier gas (N₂) was mixed with n-heptane with ratio of 90:10 (N₂:n-heptane), and the gaseous mixture was introduced into the fixed bed reactor. Various flow rate of reactants was tested to increase the yield of ethylene and propylene. For the comparison purpose, commercial zeolite was also tested in addition to Zeolite on SiC. The products were analyzed using an Agilent gas chromatograph (GC-9860) equipped with flame ionization detector (FID). The GC is connected online with the reactor and all the cracking tests were successfully reproduced. The entire catalytic evaluation results will be presented during the conference.

Keywords: cracking, catalyst, evaluation, ethylene, heptane, propylene

Procedia PDF Downloads 133

Authors: Mona Heydari, Ehsan Motamedian, Seyed Abbas Shojaosadati

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Prediction of perturbations after genetic manipulation (especially gene knockout) is one of the important challenges in systems biology. In this paper, a new algorithm is introduced that integrates microarray data into the metabolic model. The algorithm was used to study the change in the cell phenotype after knockout of Gss gene in Escherichia coli BW25113. Algorithm implementation indicated that gene deletion resulted in more activation of the metabolic network. Growth yield was more and less regulating gene were identified for mutant in comparison with the wild-type strain.

Keywords: metabolic network, gene knockout, flux balance analysis, microarray data, integration

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Authors: Alexandre Barbosa de Almeida, Telma Woerle de Lima Soares

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Protein structure prediction is a challenging task in the bioinformatics field. The biological function of all proteins majorly relies on the shape of their three-dimensional conformational structure, but less than 1% of all known proteins in the world have their structure solved. This work proposes a deep learning model to address this problem, attempting to predict some aspects of the protein conformations. Throughout a process of multiobjective dominance, a recurrent neural network was trained to abstract the particular bias of each individual multiobjective algorithm, generating a heuristic that could be useful to predict some of the relevant aspects of the three-dimensional conformation process formation, known as protein folding.

Keywords: Ab initio heuristic modeling, multiobjective optimization, protein structure prediction, recurrent neural network

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Authors: Mohammed Bello Sokoto, Adewumi Babatunde Adebayo, Ajit Singh

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The field experiment was conducted at the dry land Teaching and Research Farm of Usmanu Danfodiyo University, Sokoto, during the 2023 rainy season to determine the effects of organic, inorganic, soil conditioner and integrated use of soil conditioners (Agzyme) with organic (super gro) and inorganic fertilizers on the growth and yield of cowpea varieties. The research consisted of two cowpea varieties (SAMPEA-20-T and ex-GidanYunfa) and six combinations of organic and inorganic fertilizers and soil conditioners factorially combined and laid out in a Randomized Complete Block Design (RCBD) replicated three times. Data were collected on plant height, leaf area index, number of pods per plant, number of seeds per pod, days to 50% flowering, grain yield, and 100 seed weight. Results indicated that the 100% inorganic fertilizer had a significantly increased growth parameter such as plant height and number of leaves, while combined application of the organic fertilizer and soil conditioner resulted in a significant increase in yield parameters such as number of pods per plant, number of seeds per pod, 100 seed weight and grain yield. The study observed that the use of soil conditioner in combination with fertilizers supports sustainable cowpea production. Application of 50% recommended inorganic + 50% soil conditioner or 50% liquid organic + 50% soil conditioner was better in increasing the number of pods/plant, seeds/pod, 100 seed weight and grain yield. The ex-Gidan Yunfa cowpea variety generally performed better in most parameters measured, such as plant height, days to 50% flowering, number of pods per plant, number of seeds per pod, 100 seed weight and grain yield. Therefore, the combined application of 50% recommended inorganic + 50% soil conditioner or 50% liquid organic + 50% soil conditioner is effective for the sustainable production of cowpeas.

Keywords: integrated, fertilizers, growth, yield, cowpea, Sudan Savannah

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Authors: Danladi Ali, Abdullahi Mukaila

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In this work, GSM signal strength (power) was monitored in an indoor environment. Samples of the GSM signal strength was measured on mobile equipment (ME). One-dimensional multilevel wavelet is used to predict the fading phenomenon of the GSM signal measured and neural network clustering to determine the average power received in the study area. The wavelet prediction revealed that the GSM signal is attenuated due to the fast fading phenomenon which fades about 7 times faster than the radio wavelength while the neural network clustering determined that -75dBm appeared more frequently followed by -85dBm. The work revealed that significant part of the signal measured is dominated by weak signal and the signal followed more of Rayleigh than Gaussian distribution. This confirmed the wavelet prediction.

Keywords: decomposition, clustering, propagation, model, wavelet, signal strength and spectral efficiency

Procedia PDF Downloads 444

Authors: Ciro Cursio, Giulia Cursio, Pio Luigi Cursio, Luigi Cursio

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Communication between practitioner and patient is of the utmost importance in aesthetic medicine: as of today, images of previous treatments are the most common tool used by doctors to describe and anticipate future results for their patients. However, using photos of other people often reduces the engagement of the prospective patient and is further limited by the number and quality of pictures available to the practitioner. Pre-existing work solves this issue in two ways: 3D scanning of the area with manual editing of the 3D model by the doctor or automatic prediction of the treatment by warping the image with hand-written parameters. The first approach requires the manual intervention of the doctor, while the second approach always generates results that aren’t always realistic. Thus, in one case, there is significant manual work required by the doctor, and in the other case, the prediction looks artificial. We propose an AI-based algorithm that autonomously generates a realistic prediction of treatment results. For the purpose of this study, we focus on hyaluronic acid treatments in the facial area. Our approach takes into account the individual characteristics of each face, and furthermore, the prediction system allows the patient to decide which area of the face she wants to modify. We show that the predictions generated by our system are realistic: first, the quality of the generated images is on par with real images; second, the prediction matches the actual results obtained after the treatment is completed. In conclusion, the proposed approach provides a valid tool for doctors to show patients what they will look like before deciding on the treatment.

Keywords: prediction, hyaluronic acid, treatment, artificial intelligence

Procedia PDF Downloads 109

Authors: Etienne Alain Feukeu, L. W. Snyman

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Water scarcity is becoming a real issue nowadays. Most countries in the world are facing it in their own way based on their own geographical coordinate and condition. Many countries are facing a challenge of a growing water demand as a result of not only an increased population, economic growth, but also as a pressure of the population dynamic and urbanization. In view to mitigate some of this related problem, an accurate method of water estimation and future prediction, forecast is essential to guarantee not only the sufficient quantity, but also a good water distribution and management system. Beside the fact that several works have been undertaken to address this concern, there is still a considerable disparity between different methods and standard used for water prediction and estimation. Hence this work contrast and compare two well-defined and established methods from two countries (USA and South Africa) to demonstrate the inconsistency when different method and standards are used interchangeably.

Keywords: water scarcity, water estimation, water prediction, water forecast.

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Authors: Narayanappa Devakumar, Chengalappa Seenappa

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The field experiments were conducted during Rabi 2013 and summer 2014 at College of Sericulture, Chintamani, Chickaballapur district, Karnataka, India to find out the response of mulberry to different methods, levels of irrigation and mulching. The results showed that leaf yield and water productivity of mulberry were significantly influenced by different methods, levels of irrigation and mulching. Subsurface drip with lower level of irrigation at 0.8 CPE (Cumulative Pan Evaporation) recorded higher leaf yield and water productivity (42857 kg ha-1 yr-1and 364.41 kg hacm-1) than surface drip with higher level of irrigation at 1.0 CPE (38809 kg ha-1 yr-1 and 264.10 kg hacm-1) and micro spray jet (39931 kg ha-1 yr-1 and 271.83 kg hacm-1). Further, subsurface drip recorded minimum water used to produce one kg of leaf and to earn one rupee of profit (283 L and 113 L) compared to surface drip (390 L and 156 L) and micro spray jet (379 L and 152 L) irrigation methods. Mulberry leaf yield increased and water productivity decreased with increased levels of irrigation. However, these results indicated that irrigation of mulberry with subsurface drip increased leaf yield and water productivity by saving 20% of irrigation water than surface drip and micro spray jet irrigation methods in Eastern Dry Zone (EDZ) of Karnataka.

Keywords: cumulative pan evaporation, mulaberry, subsurface drip irrigation, water productivity

Procedia PDF Downloads 274

Authors: G. K. Madhu, S. Bhaskar, M. S. Dinesh, R. Manii, C. A. Srinivasamurthy

Abstract:

A field experiment was conducted in the premises of M/s Sri Chamundeshwari Sugars Ltd., Bharathinagar, Mandya District Pvt. Ltd., during 2014 to study the effect of sugar mill effluent (SME) on growth, yield and soil properties of ratoon cane with eight treatments replicated thrice using RCBD design. Significantly higher growth parameters like cane height (249.77 cm) and number of tillers per clump (12.22) were recorded in treatment which received cycle of 3 irrigations with freshwater + 1 irrigation with sugar mill effluent + RDF as compared to other treatments. Significantly lower growth attributes were recorded in treatment which received irrigation with sugar mill effluent alone. Significantly higher cane yield (104. 93 t -1) was recorded in treatment which received cycle of 3 irrigations with freshwater + 1 irrigation with sugar mill effluent + RDF as compared to other treatments. Significantly lower cane yield (87.40 t ha-1) was observed in treatment which received irrigation with sugar mill effluent alone. Soil properties like pH (7.84) was higher in treatment receiving Alternate irrigation with freshwater and sugar mill effluent + RDF. But EC was significantly higher in treatment which received Cycle of1 irrigation with freshwater + 2 irrigations with sugar mill effluent + RDF as compared to other treatments.

Keywords: sugar mill effluent, sugarcane, irrigation, cane yield

Procedia PDF Downloads 327

Authors: Maricon E. Gepolani, Edna A. Aguilar, Pearl B. Sanchez, Enrico P. Supangco

Abstract:

Intercropping farming system and biofertilizer application are sustainable agricultural practices that increase farm productivity by improving the yield performance of the components involved in the production system. Thus, this on-farm trial determined the yield and forage quality of corn and cowpea with and without biofertilizer application when intercropped with ratooned sugarcane. Intercropping corn and cowpea without biofertilizer application had no negative effect on the vegetative growth of sugarcane. However, application of biofertilizer on intercrops decreased tiller production at 117 days after stubble shaving (DASS), consequently reducing the estimated tonnage yield of sugarcane. The yield of intercrops and forage production of Cp3 cowpea variety increased when intercropped to ratooned sugarcane. In contrast, intercropping PSB 97-92 corn variety to ratooned sugarcane reduced its forage production, but when biofertilizer was applied to intercropped Cp5 cowpea variety, the forage production increased. Profitability (income equivalent ratio) of intercropping for both corn and cowpea are higher than monocropping and are thus suitable intercrops to ratooned sugarcane. Unaffected tiller count (a determinant of sugarcane tonnage yield) when biofertilizer was not applied to intercrops and a reduced tiller count with biofertilizer application to intercrops implies the need to develop a nutrient management practices specific for intercropping systems.

Keywords: biofertilizer, corn, cowpea, intercropping system, ratooned sugarcane

Procedia PDF Downloads 124

Authors: K. Hinde, R. Bookun, P. Tran

Abstract:

Percutaneous CT guided biopsies provide a fast, minimally invasive, cost effective and safe method for obtaining tissue for histopathology and culture. Standards for diagnostic yield vary depending on whether the tissue is being obtained for histopathology or culture. We present a retrospective audit from Western Health in Melbourne Australia over a 12-month period which aimed to determine the diagnostic yield, technical success and complication rate for CT guided bone biopsies and identify factors affecting these results. The digital imaging storage program (Synapse Picture Archiving and Communication System – Fujifilm Australia) was analysed with key word searches from October 2015 to October 2016. Nineteen CT guided bone biopsies were performed during this time. The most common referring unit was oncology, work up imaging included CT, MRI, bone scan and PET scan. The complication rate was 0%, overall diagnostic yield was 74% with a technical success of 95%. When performing biopsies for histologic analysis diagnostic yield was 85% and when performing biopsies for bacterial culture diagnostic yield was 60%. There was no significant relationship identified between size of lesion, distance of lesion to skin, lesion appearance on CT, the number of samples taken or gauge of needle to diagnostic yield or technical success. CT guided bone biopsy at Western Health meets the standard reported at other major clinical centres for technical success and safety. It is a useful investigation in identification of primary malignancy in distal bone metastases.

Keywords: bone biopsy, computed tomography, core biopsy, histopathology

Procedia PDF Downloads 197

Authors: Francis Mark A. Fernandez, Chelca Ubay, Armithan Suguitan

Abstract:

Regions or provinces in a definite state certainly contribute to the economy of their mainland. These regions or provinces are the ones supplying the mainland with different resources and assets. Thus, with a certain region separating from the mainland would indeed impinge the heart of an entire state to develop and expand. With these, the researchers decided to study on the effects of the separation of one’s region to its mainland and the consequences that will take place if the mainland would rule out the region to separate from them. The researchers wrote this paper to present the causes of the separation of Catalonia from Spain and the prediction regarding the pursuance of this region to revolt from its mainland, Spain. In conducting this research, the researchers utilized two analyses, namely: qualitative and quantitative. In qualitative, numerous of information regarding the existing experiences of the citizens of Catalonia were gathered by the authors to give certainty to the prediction of the researchers. Besides this undertaking, the researchers will also gather needed information and figures through books, journals and the published news and reports. In addition, to further support this prediction under qualitative analysis, the researchers intended to operate the Phenomenological research in which the examiners will exemplify the lived experiences of each citizen in Catalonia. Moreover, the researchers will utilize one of the types of Phenomenological research which is hermeneutical phenomenology by Van Manen. In quantitative analysis, the researchers utilized the regression analysis in which it will ascertain the causality in an underlying theory in understanding the relationship of the variables. The researchers assigned and identified different variables, wherein the dependent variable or the y which represents the prediction of the researchers, the independent variable however or the x represents the arising problems that grounds the partition of the region, the summation of the independent variable or the ∑x represents the sum of the problem and finally the summation of the dependent variable or the ∑y is the result of the prediction. With these variables, using the regression analysis, the researchers will be able to show the connections and how a single variable could affect the other variables. From these approaches, the prediction of the researchers will be specified. This research could help different states dealing with this kind of problem. It will further help certain states undergoing this problem by analyzing the causes of these insurgencies and the effects on it if it will obstruct its region to consign their full-pledge autonomy.

Keywords: autonomy, liberty, prediction, separation

Procedia PDF Downloads 246