Search results for: tomato yield prediction

Authors: Assal E. Haddad

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Food waste samples from Irbid were collected from 5 different sources for 12 weeks to characterize their composition in terms of four food categories; rice, meat, fruits and vegetables, and bread. Average food type compositions were 39% rice, 6% meat, 34% fruits and vegetables, and 23% bread. Methane yield was also measured for all food types and was found to be 362, 499, 352, and 375 mL/g VS for rice, meat, fruits and vegetables, and bread, respectively. A representative food waste sample was created to test the actual methane yield and compare it to calculated one. Actual methane yield (414 mL/g VS) was greater than the calculated value (377 mL/g VS) based on food type proportions and their specific methane yield. This study emphasizes the effect of the types of food and their proportions in food waste on the final biogas production. Findings in this study provide representative methane emission factors for Irbid’s food waste, which represent as high as 68% of total Municipal Solid Waste (MSW) in Irbid, and also indicate the energy and economic value within the solid waste stream in Irbid.

Keywords: food waste, solid waste management, anaerobic digestion, methane yield

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Authors: Emad Hafez, Mahmoud Seleiman

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Two field experiments were carried out in order to investigate the effect of chemical inducers [benzothiadiazole 0.9 mM L-1, oxalic acid 1.0 mM L-1, salicylic acid 0.2 mM L-1] on physiological and technological traits as well as on yields and antioxidant enzyme activities of barley grown under abiotic stress (i.e. water surplus and deficit conditions). Results showed that relative water content, leaf area, chlorophyll and yield as well as technological properties of barley were improved with chemical inducers application under water surplus and water-stress conditions. Antioxidant enzymes activity (i.e. catalase and peroxidase) were significantly increased in barley grown under water-stress and treated with chemical inducers. Yield and related parameters of barley presented also significant decrease under water-stress treatment, while chemical inducers application enhanced the yield-related traits. Starch and protein contents were higher in plants treated with salicylic acid than in untreated plants when water-stress was applied. In conclusion, results show that chemical inducers application have a positive interaction and synergetic influence and should be suggested to improve plant growth, yield and technological properties of water stressed barley. Salicylic acid application was better than oxalic acid and benzothiadiazole in terms of plant growth and yield improvement.

Keywords: antioxidant enzymes, drought stress, Hordeum vulgare L., quality, yield

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Authors: Najibullah Arsalan, Masaru Akaishi, Motohiro Sugiyama

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When constructing a structure on soft rock, adequate research and study are required concerning the shear behavior in the over-consolidation region because soft rock is considered to be in a heavily over-consolidated state. In many of the existing studies concerning the strength of soft rock, triaxial compression tests were conducted using isotropically consolidated samples. In this study, the strength of diatomaceous soft rock anisotropically consolidated under a designated consolidation pressure is examined in undrained triaxial compression tests, and studies are made of the peak and residual strengths of the sample in the over-consolidated state in the initial yield surface and the anisotropic yield surface.

Keywords: diatomaceouse mudstone, shear strength, yield surface, triaxial compression test

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Authors: Bruna Lima Gomes, Vanessa Caroline De Barros Bonato, Luciano Freschi, Eduardo Purgatto

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Plant hormones are long known to be tightly associated with fruit development and are involved in controlling various aspects of fruit ripening. For fleshy fruits, ripening is characterized for changes in texture, color, aroma and other parameters that markedly contribute to its quality. Ethylene is one of the major players regulating the ripening-related processes, but emerging evidences suggest that auxin is also part of this dynamic control. Thus, the aim of this study was providing new insights into the auxin role during ripening and the hormonal interplay between auxin and ethylene. For that, tomato fruits (Micro-Tom) were collected at mature green stage and separated in four groups: one for indole-3-acetic acid (IAA) treatment, one for ethylene, one for a combination of IAA and ethylene, and one for control. Hormone solution was injected through the stylar apex, while mock samples were injected with buffer only. For ethylene treatments, fruits were exposed to gaseous hormone. Then, fruits were left to ripen under standard conditions and to assess ripening development, hue angle was reported as color indicator and ethylene production was measured by gas chromatography. The transcript levels of three ripening-related ethylene receptors (LeETR3, LeETR4 and LeETR6) were evaluated by RT-qPCR. Results showed that ethylene treatment induced ripening, stimulated ethylene production, accelerated color changes and induced receptor expression, as expected. Nonetheless, auxin treatment showed the opposite effect once fruits remained green for longer time than control group and ethylene perception has changed, taking account the reduced levels of receptor transcripts. Further, treatment with both hormones revealed that auxin effect in delaying ripening was predominant, even with higher levels of ethylene. Altogether, the data suggest that auxin modulates several aspects of the tomato fruit ripening modifying the ethylene perception. The knowledge about hormonal control of fruit development will help design new strategies for effective manipulation of ripening regarding fruit quality and brings a new level of complexity on fruit ripening regulation.

Keywords: ethylene, auxin, fruit ripening, hormonal crosstalk

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Authors: Cheniti Khalissa, Dekhili Mohamed

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Several fungal diseases may infect hard and soft wheat, which directly affect the yield and thus the economy of the homeland. So, a treatment fungicide is one of means of diseases control. In this context, we studied two varieties of wheat; Waha for soft wheat and Hidhab for hard wheat, at the level of the Technical Institute of crops (ITGC) in the wilaya of Setif under semi-arid conditions. This study consists of a successive application of three fungicides (Tilt, Artea, and Armistarxtra) according to three treatments (T1, T2, and T3) in addition to the witness (T0) at different stages of plant development (respectively, Montaison, earing and after flowering) whose purpose is to test and determine the effectiveness of these products used sequentially. The study showed good efficacy when we use the sum of these pesticides The comparison between these different treatments indicates that the T3 treatment reduced yield losses significantly; which is evident in the main yield components such as fertility, grain yield and weight of 1000 grains. The various components of yield and final yield are all parameters to be taken into account in such a study. In general, the fungal treatment is an effective way of improving profitability. In general, the fungal treatment is an effective way of improving profitability and positioning interventions in time is one of the requirements for an appreciable efficiency.

Keywords: hard wheat, soft wheat, diseases, fungicide treatment, fertility, 1000-grain weight, semi-arid zone

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Authors: Takwiir Tahriim Khan, Tausif Khalid, Mohammad Redwan Ahamed, Md Soebur Rahman

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The mutual connection in joints has a significant impact on the safe and cost-effective design of steel structures. Generally, the end plates are welded at the end of the beam and columns are bolted with the end plates. Thus, the moment will be transferred at the interface, which is a critical segment at the connection. 3-D Finite Element Models (FEM) has been developed using ABAQUS 2017 software to predict the yield capacity of the end plate connections. The parameters used in this study are the depth, width, and thickness of the end plate, dimensions of the bolt, sectional and material properties of beams and columns. The influence width, depth, and thicknesses of the end plate connection on yield capacity were investigated through parametric studies. The results showed that, for increasing plate thickness from 0.3 inch to 0.8 inch by an increment of 0.1 inch the yield capacity increased by 2.85% on average, for decreasing the end plate depth from 13 inch to 11 inch the yield capacity increased by 25.4 %, and for decreasing the end plate width from 6.5 inch to 5.75 inch the yield capacity increased by 35.4%. Variation in yield capacity was also found by changing the beam and column section. Besides, the numerical results showed a good agreement with published experimental literature with an average variation of less than 8.3 % in yield capacity. So the study allows for a more effective combination of beam, column, and end plate dimensions.

Keywords: steel beam-column joints, finite element analysis, yield moment capacity, parametric study, ABAQUS, bolted joints, flush end plates, moment vs rotation curves

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Authors: Asmir Gogic, Aljo Mujcic, Sandra Ibric, Nermin Suljanovic

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Ubiquity of natural disasters during last few decades have risen serious questions towards the prediction of such events and human safety. Every disaster regardless its proportion has a precursor which is manifested as a disruption of some environmental parameter such as temperature, humidity, pressure, vibrations and etc. In order to anticipate and monitor those changes, in this paper we propose an overall system for disaster prediction and monitoring, based on wireless sensor network (WSN). Furthermore, we introduce a modified and simplified WSN routing protocol built on the top of the trickle routing algorithm. Routing algorithm was deployed using the bluetooth low energy protocol in order to achieve low power consumption. Performance of the WSN network was analyzed using a real life system implementation. Estimates of the WSN parameters such as battery life time, network size and packet delay are determined. Based on the performance of the WSN network, proposed system can be utilized for disaster monitoring and prediction due to its low power profile and mesh routing feature.

Keywords: bluetooth low energy, disaster prediction, mesh routing protocols, wireless sensor networks

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Authors: Chau-Kuang Chen, John Hughes, Jr., A. Dexter Samuels

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The prediction models for the United States Medical Licensure Examination (USMLE) Steps 1 and 2 performances were constructed by the Monte Carlo simulation modeling approach via linear regression. The purpose of this study was to build robust simulation models to accurately identify the most important predictors and yield the valid range estimations of the Steps 1 and 2 scores. The application of simulation modeling approach was deemed an effective way in predicting student performances on licensure examinations. Also, sensitivity analysis (a/k/a what-if analysis) in the simulation models was used to predict the magnitudes of Steps 1 and 2 affected by changes in the National Board of Medical Examiners (NBME) Basic Science Subject Board scores. In addition, the study results indicated that the Medical College Admission Test (MCAT) Verbal Reasoning score and Step 1 score were significant predictors of the Step 2 performance. Hence, institutions could screen qualified student applicants for interviews and document the effectiveness of basic science education program based on the simulation results.

Keywords: prediction model, sensitivity analysis, simulation method, USMLE

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Authors: Emad Amar, Tawfik Khattab, Fatma Zada

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Predicting earthquakes is an important issue in the study of geography. Accurate prediction of earthquakes can help people to take effective measures to minimize the loss of personal and economic damage, such as large casualties, destruction of buildings and broken of traffic, occurred within a few seconds. United States Geological Survey (USGS) science organization provides reliable scientific information of Earthquake Existed throughout history & Preliminary database from the National Center Earthquake Information (NEIC) show some useful factors to predict an earthquake in a seismic area like Aleutian Arc in the U.S. state of Alaska. The main advantage of this prediction method that it does not require any assumption, it makes prediction according to the future evolution of object's time series. The article compares between simulation data result from trained BP and RBF neural network versus actual output result from the system calculations. Therefore, this article focuses on analysis of data relating to real earthquakes. Evaluation results show better accuracy and higher speed by using radial basis functions (RBF) neural network.

Keywords: BP neural network, prediction, RBF neural network, earthquake

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Authors: Azzedine Hamza, Chouaib Chakour, Messaoud Ramdani

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Energy demand prediction plays a crucial role in achieving next-generation power systems for agricultural greenhouses. As a result, high prediction quality is required for efficient smart grid management and therefore low-cost energy consumption. The aim of this paper is to investigate the effectiveness of a hybrid data-driven model in day-ahead energy demand prediction. The proposed model consists of Discrete Wavelet Transform (DWT), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The DWT is employed to decompose the original signal in a set of subseries and then an ANFIS is used to generate the forecast for each subseries. The proposed hybrid method (DWT-ANFIS) was evaluated using a greenhouse energy demand data for a week and compared with ANFIS. The performances of the different models were evaluated by comparing the corresponding values of Mean Absolute Percentage Error (MAPE). It was demonstrated that discret wavelet transform can improve agricultural greenhouse energy demand modeling.

Keywords: wavelet transform, ANFIS, energy consumption prediction, greenhouse

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Authors: Xuyang Song, Jun Yin

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The smart card has been an extremely universal tool in public transit. It collects a large amount of data on buses, urban railway transit, and ferries and provides possibilities for passenger profiling. This paper combines offline analysis of passenger profiling and real-time prediction to propose a method that can accurately predict the destination station in real-time when passengers tag on. Firstly, this article constructs a static database of user travel characteristics after identifying passenger travel patterns based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The dual travel passenger habits are identified: OD travel habits and D station travel habits. Then a rapid real-time prediction algorithm based on Transit Passenger Profiling is proposed, which can predict the destination of in-board passengers. This article combines offline learning with online prediction, providing a technical foundation for real-time passenger flow prediction, monitoring and simulation, and short-term passenger behavior and demand prediction. This technology facilitates the efficient and real-time acquisition of passengers' travel destinations and demand. The last, an actual case was simulated and demonstrated feasibility and efficiency.

Keywords: travel behavior, destination prediction, public transit, passenger profiling

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Authors: Yiannis G. Smirlis

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The classification and the prediction of efficiencies in Data Envelopment Analysis (DEA) is an important issue, especially in large scale problems or when new units frequently enter the under-assessment set. In this paper, we contribute to the subject by proposing a grid structure based on interval segmentations of the range of values for the inputs and outputs. Such intervals combined, define hyper-rectangles that partition the space of the problem. This structure, exploited by Interval DEA models and a dominance relation, acts as a DEA pre-processor, enabling the classification and prediction of efficiency scores, without applying any DEA models.

Keywords: data envelopment analysis, interval DEA, efficiency classification, efficiency prediction

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Authors: A. L. Qureshi, A. A. Mahessar, R. K. Dashti, S. M. Yasin

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In this paper, effect of marginal quality groundwater on yield of cotton crop and soil salinity was studied. In this connection, three irrigation treatments each with four replications were applied. These treatments were use of canal water, use of marginal quality groundwater from tube well, and conjunctive use by mixing with the ratio of 1:1 of canal water and marginal quality tubewell water. Water was applied to the crop cultivated in Kharif season 2011; its quantity has been measured using cut-throat flume. Total 11 watering each of 50 mm depth have been applied from 20th April to 20th July, 2011. Further, irrigations were stopped from last week of July, 2011 due to monsoon rainfall. Maximum crop yield (seed cotton) was observed under T1 which was 1,516.8 kg/ha followed by T3 (mixed canal and tube well water) having 1009 kg/ha and 709 kg/ha for T2 i.e. marginal quality groundwater. This concludes that crop yield in T2 and T3 with in comparison to T1was reduced by about 53 and 30% respectively. It has been observed that yield of cotton crop is below potential limit for three treatments due to unexpected rainfall at the time of full flowering season; thus the yield was adversely affected. However, salt deposition in soil profiles was not observed that is due to leaching effect of heavy rainfall occurred during monsoon season.

Keywords: conjunctive use, cotton crop, groundwater, soil salinity status, water use efficiency

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Authors: Jamerson Felipe Pereira Lima, Jeane Cecília Bezerra de Melo

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The difficulty and cost related to obtaining of protein tertiary structure information through experimental methods, such as X-ray crystallography or NMR spectroscopy, helped raising the development of computational methods to do so. An approach used in these last is prediction of tridimensional structure based in the residue chain, however, this has been proved an NP-hard problem, due to the complexity of this process, explained by the Levinthal paradox. An alternative solution is the prediction of intermediary structures, such as the secondary structure of the protein. Artificial Intelligence methods, such as Bayesian statistics, artificial neural networks (ANN), support vector machines (SVM), among others, were used to predict protein secondary structure. Due to its good results, artificial neural networks have been used as a standard method to predict protein secondary structure. Recent published methods that use this technique, in general, achieved a Q3 accuracy between 75% and 83%, whereas the theoretical accuracy limit for protein prediction is 88%. Alternatively, to achieve better results, support vector machines prediction methods have been developed. The statistical evaluation of methods that use different AI techniques, such as ANNs and SVMs, for example, is not a trivial problem, since different training sets, validation techniques, as well as other variables can influence the behavior of a prediction method. In this study, we propose a prediction method based on artificial neural networks, which is then compared with a selected SVM method. The chosen SVM protein secondary structure prediction method is the one proposed by Huang in his work Extracting Physico chemical Features to Predict Protein Secondary Structure (2013). The developed ANN method has the same training and testing process that was used by Huang to validate his method, which comprises the use of the CB513 protein data set and three-fold cross-validation, so that the comparative analysis of the results can be made comparing directly the statistical results of each method.

Keywords: artificial neural networks, protein secondary structure, protein structure prediction, support vector machines

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Authors: Olusegun D. Badewa, E. K. Tsado, A. S. Gana, K. D. Tolorunse, R. U. Okechukwu, P. Iluebbey, S. Ibrahim

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Farmers are faced with varying production challenges ranging from unstable weather due to climate change, low yield, malnutrition, cattle invasion, and bush fires that have always affected their livelihood. Research effort must therefore be centered on improving farmers’ livelihood, nutrition, and health by providing early bulking biofortified cassava varieties that could be harvested earlier with reasonable root yield and thereby preventing long stay of the crop on their farmland. This study evaluated cassava genotypes at different harvesting months of 3, 6, 9, and 12 months after planting in order to evaluate their bulking rate at different agroecology of Mokwa and Ubiaja. Data were collected on fresh storage root yield, Harvest index, and Dry matter content. It was shown from the study that traits FSRY, HI, and DM were significant for genotype and months after planting and variable among the genotype while location had no effect on the yield traits. Early bulking genotypes were not high yielding and showed discontinuity at some point across the months. The retrogression in yield performance across months had no effect on the highest yielding. Also, for all the genotypes and across evaluated months, FSRY reduces at 9 MAP due to a reduction in dry matter content during the same month, and the best performing genotype was the genotype IBA90581, followed by IBA120036, IBA130896, and IBA980581 while the least performing was genotype IBA130818.

Keywords: early bulking, dry mater, harvest index, high yielding, root yield

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Authors: M. Karimpour, L. Hitihamillage, N. Elkhoury, S. Moridpour, R. Hesami

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Rail transport authorities around the world have been facing a significant challenge when predicting rail infrastructure maintenance work for a long period of time. Generally, maintenance monitoring and prediction is conducted manually. With the restrictions in economy, the rail transport authorities are in pursuit of improved modern methods, which can provide precise prediction of rail maintenance time and location. The expectation from such a method is to develop models to minimize the human error that is strongly related to manual prediction. Such models will help them in understanding how the track degradation occurs overtime under the change in different conditions (e.g. rail load, rail type, rail profile). They need a well-structured technique to identify the precise time that rail tracks fail in order to minimize the maintenance cost/time and secure the vehicles. The rail track characteristics that have been collected over the years will be used in developing rail track degradation prediction models. Since these data have been collected in large volumes and the data collection is done both electronically and manually, it is possible to have some errors. Sometimes these errors make it impossible to use them in prediction model development. This is one of the major drawbacks in rail track degradation prediction. An accurate model can play a key role in the estimation of the long-term behavior of rail tracks. Accurate models increase the track safety and decrease the cost of maintenance in long term. In this research, a short review of rail track degradation prediction models has been discussed before estimating rail track degradation for the curve sections of Melbourne tram track system using Adaptive Network-based Fuzzy Inference System (ANFIS) model.

Keywords: ANFIS, MGT, prediction modeling, rail track degradation

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Authors: Vijay Kr. Yadav, Nilam Rathi

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Accurate prediction of glucose level for diabetes mellitus is required to avoid affecting the functioning of major organs of human body. This study describes the fundamental assumptions and two different methodologies of the Blood glucose prediction. First is based on the back-propagation algorithm of Artificial Neural Network (ANN), and second is based on the Neuro-Fuzzy technique, called Fuzzy Inference System (FIS). Errors between proposed methods further discussed through various statistical methods such as mean square error (MSE), normalised mean absolute error (NMAE). The main objective of present study is to develop mathematical model for blood glucose prediction before 12 hours advanced using data set of three patients for 60 days. The comparative studies of the accuracy with other existing models are also made with same data set.

Keywords: back-propagation, diabetes mellitus, fuzzy inference system, neuro-fuzzy

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Authors: Sanjay Kumar Behera, Kanhu Charan Patra

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A GIS-based method has been applied for the determination of soil erosion and sediment yield in a small watershed in Ong River basin, Odisha, India. The method involves spatial disintegration of the catchment into homogenous grid cells to capture the catchment heterogeneity. The gross soil erosion in each cell was calculated using Universal Soil Loss Equation (USLE) by carefully determining its various parameters. The concept of sediment delivery ratio is used to route surface erosion from each of the discretized cells to the catchment outlet. The process of sediment delivery from grid cells to the catchment outlet is represented by the topographical characteristics of the cells. The effect of DEM resolution on sediment yield is analyzed using two different resolutions of DEM. The spatial discretization of the catchment and derivation of the physical parameters related to erosion in the cell are performed through GIS techniques.

Keywords: DEM, GIS, sediment delivery ratio, sediment yield, soil erosion

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Authors: Andreas Livera, Spyros Theocharides, Michalis Florides, Charalambos Anastassiou

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A main challenge in the quest for ensuring quality of operation, especially for photovoltaic (PV) systems, is to safeguard the reliability and optimal performance by detecting and diagnosing potential failures and performance losses at early stages or before the occurrence through real-time monitoring, supervision, fault detection, and predictive maintenance. The purpose of this work is to present the functionalities and results related to the development and validation of a software platform for PV assets diagnosis and maintenance. The platform brings together proprietary hardware sensors and software algorithms to enable the early detection and prediction of the most common and critical faults in PV systems. It was validated using field measurements from operating PV systems. The results showed the effectiveness of the platform for detecting faults and losses (e.g., inverter failures, string disconnections, and potential induced degradation) at early stages, forecasting PV power production while also providing recommendations for maintenance actions. Increased PV energy yield production and revenue can be thus achieved while also minimizing operation and maintenance (O&M) costs.

Keywords: failure detection and prediction, operation and maintenance, performance monitoring, photovoltaic, platform, recommendations, predictive maintenance

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Authors: Raviv Harris, Maggie Levy

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Natural product-based pesticides may serve as an alternative to the traditional synthetic pesticides, which have a potentially damaging effect, both to human health and for the environment. Along with plants, microorganisms are a prospective source of such biological pesticides. A unique and active strain of P. aphidis (designated isolate L12, Israel 2004), an epiphytic and non-pathogenic basidiomycete yeast, was isolated in our lab from strawberry leaves. P. aphidis L12 secretions were found to inhibit broad range of plant pathogens. This work demonstrates that metabolites isolated from the biocontrol agent P. aphidis (isolate L12) can inhibit varied fungal and bacterial phytopathogens. Biologically active metabolites were extracted from P. aphidis biomass, using the organic solvent ethyl acetate. The antimicrobial activity of the extract was demonstrated, both in vitro and in planta. Using disk diffusion assays, the following inhibition zones were obtained: 43cm² for Pseudomonas syringae pv. tomato, 28.5cm² for Xanthomonas campestris pv. vesicatoria, 59cm² for Clavibacter michiganensis subsp. michiganensis, 34cm² for Erwinia amylovora and 34cm² for Agrobacterium tumefaciens. Additionally, strong inhibitory activity of the extract against fungi mycelial growth was established, with IC₅₀ values of 606µg ml⁻¹ for Botrytis cinerea, 221µg ml⁻¹ for Pythium spp., 519µg ml⁻¹ for Rhizoctonia solani, 455µg ml⁻¹ for Sclerotinia sclerotiorum, 2270µg ml⁻¹ for Fusarium oxysporum f. sp. lycopersici, and 2038µg ml⁻¹ for Alternaria alternata. The results of the in planta experiments demonstrated a dose-dependent reduction in disease infection. Significant inhibition of B. cinerea lesions on tomato plants was obtained when a spore suspension of this pathogen was treated with extract concentrations higher than 4.2mg ml⁻¹. Concentration of 7mg ml⁻¹ caused a reduction of over 95% in the lesion size of B. cinerea on tomato plants. The strong antimicrobial activity demonstrated both in vitro and in planta against varied phytopathogens, may indicate that the extracted antimicrobial metabolites have potential to serve as natural pesticides in the field.

Keywords: antimicrobial, B. cinerea, metabolites, natural pesticides, P. aphidis

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Authors: Ravinder Bahl, Jamini Sharma

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The paper demonstrates analysis of the cervical cancer based on a probabilistic model. It involves technique for classification and prediction by recognizing typical and diagnostically most important test features relating to cervical cancer. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases. The combination of the conventional statistical and machine learning tools is applied for the analysis. Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.

Keywords: cervical cancer, recurrence, no recurrence, probabilistic, classification, prediction, machine learning

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Authors: Anurita Selvarajoo, Svenja Hanson

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Growing energy needs and increasing environmental issues are creating awareness for alternative energy which substitutes the non-renewable and polluting fossil fuels. Agricultural wastes are a good feedstock for biochar production through the pyrolysis process. There is potential to generate solid fuel from agricultural wastes, as there are large quantities of agricultural wastes available in Malaysia. This paper outlines the experimental study on the pyrolysis of banana peel. The effects of pyrolysis temperatures on the yield of biochar from the banana peel were investigated. Banana peel was pyrolysed in a horizontal tubular reactor under inert atmosphere by varying the temperatures between 300 and 700 0C. With increasing temperature, the total biochar yield decreased with increased heating value. It was found that the pyrolysis temperature had major effect on the yield of biochar product. It also exerted major influence on the heating value and C,H and O composition. The obtained biochar ranged between 31.9 to 56.7 %wt, at different pyrolysis temperatures. The optimum biochar yield was obtained at 325 0C. Biochar yield obtained at optimum temperature was 47 % wt with a heating value of 25.9 MJ kg-1. The study has been performed in order to demonstrate that agricultural wastes like banana peel are also important source of solid fuel.

Keywords: agricultural Wastes, banana peel, biochar, pyrolysis

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Authors: Muazu A.

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Haphazard and inappropriate plant spacing and poor soilfertility management practice are among the major factorsconstraining onion production in Gashua, Bade Locale Government Yobe State.Field experiments were conducted in 2023 dry season ar Federal University, Gashua university farm assess the influence of intra-row spacing (2.5, 5, 7.5, 10 and 12.5 cm) and nitrogen fertilizerrate (0, 41, 82 and 123kg Nha-1) growth, bulb yield and quality of onion. The experiment was laid out in a randomized complete block design (RCBD) with three replications. The main effects of nitrogen rate and intra-row spacing influenced only the plant height stand count significantly obtained from 7.5cm and 82kg Nha-1 intra-row spacing and nitrogen fertilizer respectively. The highest yield was obtained from the application of 82kg Nha-1 and plant spacing of 5.0cm and 7.5cm respectively.

Keywords: onion, intra-row spacing, nitrogen fertilizer, yield

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Authors: Senthilkumar S, Vijayakumar R M , Soorianathasundaram K, Durga Devi D

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A study was conducted to assess the influence of brassinosteroid and other bioregulators as pre-harvest sprays on yield and quality of newly introduced Californian grape cv. Italia. The vines were exposed to standardized pruning level of pruning 50% of the canes to 5-6 bud level for fruiting and 50% of the canes to two bud level for vegetative growth. The influence of brassinosteroid was assessed using BR (1 ppm) alone and in combination with GA3 and CPPU, sprayed at three different stages over the control (water spray) were given as treatments. The results revealed that the bunches treated with Brassinosteroid (1 ppm) + GA3 (10 ppm) at pea stage i.e., 7-8 mm berry size, recorded the maximum values on yield characters like bunch weight (719.94 g), yield per vine (12.70 kg/vine) and yield per hectare (15.88 t). The berry characters and quality traits were also significantly influenced by the application of bioregulators. The maximum value for all those characters was registered under bunch sprays of Brassinosteroid (1 ppm) + GA3 (10 ppm) at pea stage. The economic feasibility indicated that the treatment combination Brassinosteroid (1 ppm) + GA3 (10 ppm) at pea stage (7-8 mm berry size) had registered the maximum benefit cost ratio of 3.13, as compared to 1.89 in control (water spray). Overall, it was observed that a combined bunch spray of Brassinosteroid (1 ppm) + GA3 (10 ppm) at pea stage (7-8 mm berry size) was adjudged as the best treatment for promoting the crop for better the bunch quality and yield.

Keywords: bioregulators, brassinosteroid, CPPU, GA3, Italia grape cultivar

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Authors: Atif Naim, Faisal E. Ahmed, Sershen

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A field experiment was conducted for two consecutive winter seasons at the Demonstration Farm of the Faculty of Agriculture, University of Khartoum, Sudan, to study effects of different levels of irrigation regime and plant density on yield of introduced small seeded (desi type) chickpea cultivar (ILC 482). The experiment was laid out in a 3X3 factorial split-plot design with 4 replications. The treatments consisted of three irrigation regimes (designated as follows: I1 = optimum irrigation, I2 = moderate stress and I3 = severe stress; this corresponded with irrigation after drainage of 50%, 75% and 100% of available water based on 70%, 60% and 50% of field capacity, respectively) assigned as main plots and three plant densities (D₁=20, D₂= 40 and D₃= 60 plants/m²) assigned as subplots. The results indicated that the yield components (number of pods per plant, number of seeds per pod, 100 seed weight), seed yield per plant, harvest index and yield per unit area of chickpea were significantly (p < 0.05) affected by irrigation regime. Decreasing irrigation regime significantly (p < 0.05) decreased all measured parameters. Alternatively, increasing plant density significantly (p < 0.05) decreased the number of pods and seed yield per plant and increased seed yield per unit area. While number of seeds per pod and harvest index were not significantly (p > 0.05) affected by plant density. Interaction between irrigation regime and plant density was also significantly (p < 0.05) affected all measured parameters of yield, except for harvest index. It could be concluded that the best irrigation regime was full irrigation (after drainage of 50% available water at 70% field capacity) and the optimal plant density was 20 plants/m² under conditions of semi-arid regions.

Keywords: irrigation regime, Cicer arietinum, chickpea, plant density

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Authors: Mohammad Mahdi Mousavi

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Bankruptcy prediction models have been implemented for continuous evaluation and monitoring of firms. With the huge number of bankruptcy models, an extensive number of studies have focused on answering the question that which of these models are superior in performance. In practice, one of the drawbacks of existing comparative studies is that the relative assessment of alternative bankruptcy models remains an exercise that is mono-criterion in nature. Further, a very restricted number of criteria and measure have been applied to compare the performance of competing bankruptcy prediction models. In this research, we overcome these methodological gaps through implementing an extensive range of criteria and measures for comparison between dynamic and static bankruptcy models, and through proposing a multi-criteria framework to compare the relative performance of bankruptcy models in forecasting firm distress for UK firms.

Keywords: bankruptcy prediction, data envelopment analysis, performance criteria, performance measures

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Authors: Feng Guolin, Gong Zhiqiang

Abstract:

In order to study the spatial structure and dynamical mechanism of extreme precipitation in East Asia, a corresponding climate network is constructed by employing the method of event synchronization. It is found that the area of East Asian summer extreme precipitation can be separated into two regions: one with high area weighted connectivity receiving heavy precipitation mostly during the active phase of the East Asian Summer Monsoon (EASM), and another one with low area weighted connectivity receiving heavy precipitation during both the active and the retreat phase of the EASM. Besides，a way for the prediction of extreme precipitation is also developed by constructing a directed climate networks. The simulation accuracy in East Asia is 58% with a 0-day lead, and the prediction accuracy is 21% and average 12% with a 1-day and an n-day (2≤n≤10) lead, respectively. Compare to the normal EASM year, the prediction accuracy is lower in a weak year and higher in a strong year, which is relevant to the differences in correlations and extreme precipitation rates in different EASM situations. Recognizing and identifying these effects is good for understanding and predicting extreme precipitation in East Asia.

Keywords: synchronization, climate network, prediction, rainfall

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Authors: Nabilah Filzah Mohd Radzuan, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan

Abstract:

Uncertain data is believed to be an important issue in building up a prediction model. The main objective in the time series uncertainty analysis is to formulate uncertain data in order to gain knowledge and fit low dimensional model prior to a prediction task. This paper discusses the performance of a number of techniques in dealing with uncertain data specifically those which solve uncertain data condition by minimizing the loss of compression properties.

Keywords: compression properties, uncertainty, uncertain time series, mining technique, weather prediction

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Authors: Ulas Vural, M. Ergun Okay, E. Mesut Yildiz

Abstract:

Telecommunication service providers demand accurate and precise prediction of customer churn probabilities to increase the effectiveness of their customer relation services. The large amount of customer data owned by the service providers is suitable for analysis by machine learning methods. In this study, expenditure data of customers are analyzed by using an artificial neural network (ANN). The ANN model is applied to the data of customers with different billing duration. The proposed model successfully predicts the churn probabilities at 83% accuracy for only three months expenditure data and the prediction accuracy increases up to 89% when the nine month data is used. The experiments also show that the accuracy of ANN model increases on an extended feature set with information of the changes on the bill amounts.

Keywords: customer relationship management, churn prediction, telecom industry, deep learning, artificial neural networks

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Authors: Hailekiros Tadesse Tekle, Yemane Tsehaye, Fetien Abay

Abstract:

Barley (Hordeum vulgare L.) is one of the most important cereal crops in the world, grown for the poor farmers in Tigray with low yield production. The purpose of this research was to estimate the performance of 166 barley genotypes against the quantitative traits with detailed analysis of the variance component, heritability, genetic advance, and genetic usefulness parameters. The finding of ANOVA was highly significant variation (p ≤ 0:01) for all the genotypes. We found significant differences in coefficient of variance (CV of 15%) for 5 traits out of the 12 quantitative traits. The topmost broad sense heritability (H2) was recorded for seeds per spike (98.8%), followed by thousand seed weight (96.5%) with 79.16% and 56.25%, respectively, of GAM. The traits with H2 ≥ 60% and GA/GAM ≥ 20% suggested the least influenced by the environment, governed by the additive genes and direct selection for improvement of such beneficial traits for the studied genotypes. Hence, the 20 outstanding recombinant inbred lines (RIL) barley genotypes performing early maturity, high yield, and 1000 seed weight traits simultaneously were the top ranked group barley genotypes out of the 166 genotypes. These are; G5, G25, G33, G118, G36, G123, G28, G34, G14, G10, G3, G13, G11, G32, G8, G39, G23, G30, G37, and G26. They were early in maturity, high TSW and GYP (TSW ≥ 55 g, GYP ≥ 15.22 g/plant, and DTM below 106 days). In general, the 166 genotypes were classified as high (group 1), medium (group 2), and low yield production (group 3) genotypes in terms of yield and yield component trait analysis by clustering; and genotype parameter analysis such as the heritability, genetic advance, and genetic usefulness traits in this investigation.

Keywords: barley, clustering, genetic advance, heritability, usefulness, variability, yield

Procedia PDF Downloads 86