Search results for: tomato yield prediction

Authors: Kanika Kalra, Harmeet Kaur, Dinesh Goyal

Abstract:

Total phenolics were extracted from waste orange peels by solvent extraction and alkali hydrolysis method. The most efficient solvents for extracting phenolic compounds from waste biomass were methanol (60%) > dimethyl sulfoxide > ethanol (60%) > distilled water. The extraction yields were significantly impacted by solvents (ethanol, methanol, and dimethyl sulfoxide) due to varying polarity and concentrations. Extraction of phenolics using 60% methanol yielded the highest phenolics (in terms of gallic acid equivalent (GAE) per gram of biomass) in orange peels. Alkali hydrolyzed extract from orange peels contained 7.58±0.33 mg GAE g⁻¹. By using the solvent extraction technique, it was observed that 60% methanol is comparatively the best-suited solvent for extracting polyphenolic compounds and gave the maximum yield of 4.68 ± 0.47 mg GAE g⁻¹ in orange peel extracts. DPPH radical scavenging activity and reducing the power of orange peel extract were checked, where 60% methanolic extract showed the highest antioxidant activity, 85.50±0.009% for DPPH, and dimethyl sulfoxide (DMSO) extract gave the highest yield of 1.75±0.01% for reducing power ability of the orange peels extract. Characterization of the polyphenolic compounds was done by using Fourier transformation infrared (FTIR) spectroscopy. Solvent and alkali hydrolysed extracts were evaluated for antibacterial activity using the agar well diffusion method against Gram-positive Bacillus subtilis MTCC441 and Gram-negative Escherichia coli MTCC729. Methanolic extract at 300µl concentration showed an inhibition zone of around 16.33±0.47 mm against Bacillus subtilis, whereas, for Escherichia coli, it was comparatively less. Broth-based turbidimetric assay revealed the antibacterial effect of different volumes of orange peel extracts against both organisms.

Keywords: orange peels, total phenolic content, antioxidant, antibacterial

Procedia PDF Downloads 73

Authors: James Ladzekpo

Abstract:

Background: The urgent need to identify new pharmacological targets for diabetes treatment and prevention has been amplified by the disease's extensive impact on individuals and healthcare systems. A deeper insight into the biological underpinnings of diabetes is crucial for the creation of therapeutic strategies aimed at these biological processes. Current predictive models based on genetic variations fall short of accurately forecasting diabetes. Objectives: Our study aims to pinpoint key epigenetic factors that predispose individuals to diabetes. These factors will inform the development of an advanced predictive model that estimates diabetes risk from genetic profiles, utilizing state-of-the-art statistical and data mining methods. Methodology: We have implemented a recursive feature elimination with cross-validation using the support vector machine (SVM) approach for refined feature selection. Building on this, we developed six machine learning models, including logistic regression, k-Nearest Neighbors (k-NN), Naive Bayes, Random Forest, Gradient Boosting, and Multilayer Perceptron Neural Network, to evaluate their performance. Findings: The Gradient Boosting Classifier excelled, achieving a median recall of 92.17% and outstanding metrics such as area under the receiver operating characteristics curve (AUC) with a median of 68%, alongside median accuracy and precision scores of 76%. Through our machine learning analysis, we identified 31 genes significantly associated with diabetes traits, highlighting their potential as biomarkers and targets for diabetes management strategies. Conclusion: Particularly noteworthy were the Gradient Boosting Classifier and Multilayer Perceptron Neural Network, which demonstrated potential in diabetes outcome prediction. We recommend future investigations to incorporate larger cohorts and a wider array of predictive variables to enhance the models' predictive capabilities.

Keywords: diabetes, machine learning, prediction, biomarkers

Procedia PDF Downloads 55

Authors: Shivani Sharma, Prashant Saxena, Inamul Hasan Madar

Abstract:

Since the time of Darwin, it has been considered that genetic change is the direct indicator of variation in phenotype. But a few studies in system biology in the past years have proposed that epigenetic developmental processes also affect the phenotype thus shifting the focus from a linear genotype-phenotype map to a non-linear G-P map. In this paper, we attempt at explaining the evolution of colour vision in mammals by taking LWS/ Long-wave sensitive gene under consideration.

Keywords: evolution, phenotypes, epigenetics, LWS gene, G-P map

Procedia PDF Downloads 521

Authors: Charles Iledun Oyewole, Harira Shuaib

Abstract:

While fulfilling the food security of an increasing population like Nigeria remains a major global concern, more than one-third of crop harvested is lost or wasted during harvesting or in postharvest operations. Reducing the harvest and postharvest losses, especially in developing countries, could be a sustainable solution to increase food availability, eliminate hunger and improve farmers’ livelihoods. Nigeria is one of the countries in sub-Saharan Africa with insufficient food production and high food import bill, which has had debilitating effects on the country’s economy. One of the goals of Nigeria’s agricultural development policy is to ensure that, the nation produces enough food and be less dependent on importation so as to ensure adequate and affordable food for all. Maize could fill the food gap in Nigeria’s effort to beat hunger and food insecurity. Maize is the most important cereal after rice and its production contributes immensely to food availability on the tables of many Nigerians. Maize grains constitute primary source of food for large percentage of the Nigerian populace, thus a considerable waste of this valuable food pre and post-harvest constitutes such a major agricultural bottleneck; that the reduction of pre and post-harvest losses is now a common food security strategy. In surveys conducted, as much as 60% maize outputs can be lost on the field and during the storage stage due to technical inefficiency. Field losses due to rodent damage alone can account for between 10% - 60% grain losses depending on the location. While the use of scientific storage methods can reduce losses below 2% in storage, timely harvesting of crop can check losses on the fields resulting from rodent damage or pest infestation. A push for increased crop production must be complemented by available and affordable post-harvest technologies that will reduce losses on farmers’ fields as well as in storage.

Keywords: government policy, maize, population increase, storage, sustainable food production, yield, yield losses

Procedia PDF Downloads 136

Authors: Shingo Murakami, Shinichi Enoki

Abstract:

In structures, stress concentration is a factor of fatigue fracture. Basically, the stress concentration is a phenomenon that should be avoided. However, it is difficult to avoid the stress concentration. Therefore, relaxation of the stress concentration is important. The stress concentration arises from notches and circular holes. There is a relaxation method that a composite patch covers a notch and a circular hole. This relaxation method is used to repair aerial wings, but it is not systematized. Composites are more expensive than single materials. Accordingly, we propose the relaxation method that a single material patch covers a notch and a circular hole, and aim to systematize this relaxation method. We performed FEA (Finite Element Analysis) about an object by using a three-dimensional FEA model. The object was that a patch adheres to a plate with a circular hole. And, a uniaxial tensile load acts on the patched plate with a circular hole. In the three-dimensional FEA model, it is not easy to model the adhesion layer. Basically, the yield stress of the adhesive is smaller than that of adherents. Accordingly, the adhesion layer gets to plastic deformation earlier than the adherents under the yield stress of adherents. Therefore, we propose the three-dimensional FEA model which is applied a nonlinear elastic region to the adhesion layer. The nonlinear elastic region was calculated by a bilinear approximation. We compared the analysis results with the tensile test results to confirm whether the analysis model has usefulness. As a result, the analysis results agreed with the tensile test results. And, we confirmed that the analysis model has usefulness. As a result that the three-dimensional FEA model was used to the analysis, it was confirmed that an out-of-plane deformation occurred to the patched plate with a circular hole. The out-of-plane deformation causes stress increase of the patched plate with a circular hole. Therefore, we investigate that the out-of-plane deformation affects relaxation of the stress concentration in the plate with a circular hole on this relaxation method. As a result, it was confirmed that the out-of-plane deformation inhibits relaxation of the stress concentration on the plate with a circular hole.

Keywords: stress concentration, patch, out-of-plane deformation, Finite Element Analysis

Procedia PDF Downloads 266

Authors: Theodora-Venetia Missirli, Konstantina Kyriakopoulou, Magdalini Krokida

Abstract:

Olive mill solid waste is an olive oil mill industry by-product with high phenolic, lipid and organic acid concentrations that can be used as a low cost source of natural antioxidants. In this study, extracts of Olea europaea (olive tree) solid olive mill waste (SOMW) were evaluated in terms of their antiradical activity and total phenolic compounds concentrations, such as oleuropein, hydroxytyrosol etc. SOMW samples were subjected to drying prior to extraction as a pretreatment step. Two drying processes, accelerated solar drying (ASD) and air-drying (AD) (at 35, 50, 70°C constant air velocity of 1 m/s), were applied. Subsequently, three different extraction methods were employed to recover extracts from untreated and dried SOMW samples. The methods include the green Microwave Assisted (MAE) and Ultrasound Assisted Extraction (UAE) and the conventional Soxhlet extraction (SE), using water and methanol as solvents. The efficiency and selectivity of the processes were evaluated in terms of extraction yield. The antioxidant activity (AAR) and the total phenolic content (TPC) of the extracts were evaluated using the DPPH assay and the Folin-Ciocalteu method, respectively. The results showed that bioactive content was significantly affected by the extraction technique and the solvent. Specifically, untreated SOMW samples showed higher performance in the yield for all solvents and higher antioxidant potential and phenolic content in the case of water. UAE extraction method showed greater extraction yields than the MAE method for both untreated and dried leaves regardless of the solvent used. The use of ultrasound and microwave assisted extraction in combination with industrially applied drying methods, such as air and solar drying, was feasible and effective for the recovery of bioactive compounds.

Keywords: antioxidant potential, drying treatment, olive mill pomace, microwave assisted extraction, ultrasound assisted extraction

Procedia PDF Downloads 304

Authors: Gabriela T. Nakashima, Elias R. D. Padilla, Joao L. Barros, Gabriela B. Belini, Hiroyuki Yamamoto, Fabio M. Yamaji

Abstract:

Biochar can be an alternative to use sugarcane trash. Biochar is a solid material obtained from pyrolysis, that is a biomass thermal degradation with low or no O₂ concentration. Pyrolysis transforms the carbon that is commonly found in other organic structures into a carbon with more stability that can resist microbial decomposition. Biochar has a versatility of uses such as soil fertility, carbon sequestration, energy generation, ecological restoration, and soil remediation. Biochar has a great ability to retain water and nutrients in the soil so that this material can improve the efficiency of irrigation and fertilization. The aim of this study was to characterize biochar produced from sugarcane trash in three different pyrolysis temperatures and determine the lowest temperature with the high yield and carbon content. Physical characterization of this biochar was performed to help the evaluation for the best production conditions. Sugarcane (Saccharum officinarum) trash was collected at Corredeira Farm, located in Ibaté, São Paulo State, Brazil. The farm has 800 hectares of planted area with an average yield of 87 t·ha⁻¹. The sugarcane varieties planted on the farm are: RB 855453, RB 867515, RB 855536, SP 803280, SP 813250. Sugarcane trash was dried and crushed into 50 mm pieces. Crucibles and lids were used to settle the sugarcane trash samples. The higher amount of sugarcane trash was added to the crucible to avoid the O₂ concentration. Biochar production was performed in three different pyrolysis temperatures (200°C, 325°C, 450°C) in 2 hours residence time in the muffle furnace. Gravimetric yield of biochar was obtained. Proximate analysis of biochar was done using ASTM E-872 and ABNT NBR 8112. Volatile matter and ash content were calculated by direct weight loss and fixed carbon content calculated by difference. Porosity measurement was evaluated using an automatic gas adsorption device, Autosorb-1, with CO₂ described by Nakatani. Approximately 0.5 g of biochar in 2 mm particle sizes were used for each measurement. Vacuum outgassing was performed as a pre-treatment in different conditions for each biochar temperature. The pore size distribution of micropores was determined using Horváth-Kawazoe method. Biochar presented different colors for each treatment. Biochar - 200°C presented a higher number of pieces with 10mm or more and did not present the dark black color like other treatments after 2 h residence time in muffle furnace. Also, this treatment had the higher content of volatiles and the lower amount of fixed carbon. In porosity analysis, while the temperature treatments increase, the amount of pores also increase. The increase in temperature resulted in a biochar with a better quality. The pores in biochar can help in the soil aeration, adsorption, water retention. Acknowledgment: This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brazil – PROAP-CAPES, PDSE and CAPES - Finance Code 001.

Keywords: proximate analysis, pyrolysis, soil amendment, sugarcane straw

Procedia PDF Downloads 214

Authors: Sahil Kumar, Rajaram Ghadge, Ramesh Bhujade

Abstract:

Historically, lipid extraction from dried algal biomass remained a focus area of the algal research. It has been realized over the past few years that the lipid-centric approach and conversion technologies that require dry algal biomass have several challenges. Algal culture in cultivation systems contains more than 99% water, with algal concentrations of just a few hundred milligrams per liter ( < 0.05 wt%), which makes harvesting and drying energy intensive. Drying the algal biomass followed by extraction also entails the loss of water and nutrients. In view of these challenges, focus has shifted toward developing processes that will enable oil production from wet algal biomass without drying. Hydrothermal liquefaction (HTL), an emerging technology, is a thermo-chemical conversion process that converts wet biomass to oil and gas using water as a solvent at high temperature and high pressure. HTL processes wet algal slurry containing more than 80% water and significantly reduces the adverse cost impact owing to drying the algal biomass. HTL, being inherently feedstock agnostic, i.e., can convert carbohydrates and proteins also to fuels and recovers water and nutrients. It is most effective with low-lipid (10--30%) algal biomass, and bio-crude yield is two to four times higher than the lipid content in the feedstock. In the early 2010s, research remained focused on increasing the oil yield by optimizing the process conditions of HTL. However, various techno-economic studies showed that simply converting algal biomass to only oil does not make economic sense, particularly in view of low crude oil prices. Making the best use of every component of algae is a key for economic viability of algal to oil process. On investigation of HTL reactions at the molecular level, it has been observed that sequential HTL has the potential to recover value-added products along with biocrude and improve the overall economics of the process. This potential of sequential HTL makes it a most promising technology for converting wet waste to wealth. In this presentation, we will share our experience on the techno-economic and engineering aspects of sequential HTL for conversion of algal biomass to algal bio-oil and co-products.

Keywords: algae, biomass, lipid, protein

Procedia PDF Downloads 214

Authors: Yusuf Ersoy Yildirim, Ismail Tas, Ceren Gorgusen, Tugba Yeter, Aysegul Boyacioglu, K. Mehmet Tugrul, Murat Tugrul, Ayten Namli, H. Sabri Ozturk, M. Onur Akca

Abstract:

In recent years, a lot of research has been done for the sustainable use of scarce resources in the world. Especially, effective and sustainable use of water resources has been researched for many years. Sub-surface drip irrigation (SDI) is one of the most effective irrigation methods in which efficient and sustainable use of irrigation water can be achieved. When the literature is taken into consideration, it is often emphasized that, besides its numerous advantages, it also allows the application of irrigation water to the plant root zone along with air. It is stated in different studies that the air applied to the plant root zone with irrigation water has a positive effect on the root zone. Plants need sufficient oxygen for root respiration as well as for the metabolic functions of the roots. Decreased root respiration due to low oxygen content reduces transpiration, disrupts the flow of ions, and increases the ingress of salt reaching toxic levels, seriously affecting plant growth. Lack of oxygen (Hypoxia) can affect the survival of plants. The lack of oxygen in the soil is related to the exchange of gases in the soil with the gases in the atmosphere. Soil aeration is an important physical parameter of a soil. It is highly dynamic and is closely related to the amount of water in the soil and its bulk weight. Subsurface drip irrigation; It has higher water use efficiency compared to irrigation methods such as furrow irrigation and sprinkler irrigation. However, in heavy clay soils, subsurface drip irrigation creates continuous wetting fronts that predispose the rhizosphere region to hypoxia or anoxia. With subsurface drip irrigation, the oxygen is limited for root microbial respiration and root development, with the continuous spreading of water to a certain region of the root zone. In this study, the change in sugar beet yield caused by air application in the SDI system will be explained.

Keywords: sugar beet, subsurface drip irrigation, air application, irrigation efficiency

Procedia PDF Downloads 81

Authors: Chularat Leewuttanakul, Khanitta Ruttarattanamongkol, Sasivimon Chittrakorn

Abstract:

Rice during grain development stage is a rich source of many bioactive compounds. Dough stage rice contains high amounts of photochemical and can be used for rice milling industries. However, rice grain at dough stage had low milling quality due to high moisture content. Thermal processing can be applied to rice grain for improving milled rice yield. This experiment was conducted to study the chemical and physic properties of dough stage rice grain after roasting treatment. Rice were roasted with two different methods including traditional pan roasting at 140 °C for 60 minutes and using the electrical roasting machine at 140 °C for 30, 40, and 50 minutes. The chemical, physical properties, and bioactive compounds of brown rice and milled rice were evaluated. The result of this experiment showed that moisture content of brown and milled rice was less than 10 % and amylose contents were in the range of 26-28 %. Rice grains roasting for 30 min using electrical roasting machine had high head rice yield and length and breadth of grain after milling were close to traditional pan roasting (p > 0.05). The lightness (L*) of rice did not affect by roasting treatment (p > 0.05) and the a* indicated the yellowness of milled rice was lower than brown rice. The bioactive compounds of brown and milled rice significantly decreased with increasing of drying time. Brown rice roasted for 30 minutes had the highest of total phenolic content, antioxidant activity, α-tocopherol, and ɤ-oryzanol content. Volume expansion and elongation of cooked rice decreased as roasting time increased and quality of cooked rice roasted for 30 min was comparable to traditional pan roasting. Hardness of cooked rice as measured by texture analyzer increased with increasing roasting time. The results indicated that rice grains at dough stage, containing a high amount of bioactive compounds, have a great potential for rice milling industries and the electrical roasting machine can be used as an alternative to pan roasting which decreases processing time and labor costs.

Keywords: bioactive compounds, cooked rice, dough stage rice grain, grain development, roasting

Procedia PDF Downloads 163

Authors: Mohammad Mirwais Yusufi

Abstract:

Skillful management is one of the most important success factors for today’s farms. When a farm is well managed, it can generate funds for its sustainability. Grape is one of the most diffused fruits in the world and one of the most important cash crops with high potential of production in Afghanistan as well. While there are several organizations intervening for improvement of this cash crop, the quality and quantity are still not satisfactory for producers and external markets. The situation has not changed over the years. Therefore, a survey was conducted in 2017 with 60 grape growers, supported by questionnaires in Mirbachakot, Kalakan and Shakardara districts of Kabul province. The purpose was to get an understanding of the current socio-demographic characteristics of farmers, management methods, constraints, farm size, yield and contribution of grape farming to household income. Findings indicate that grape farming was predominant 83.3% male, 16.6% female and small-scale farmers were the main grape producers, 60% < 1 ha of land under grape production. Likewise, 50% had more than > 10 years and 33.3% between 1-5 years’ experience in grape farming. The high level of illiteracy and diseases had significant digit effect on growth, yield and quality of grapes. The results showed that vineyard management operations to protect grapes from mechanical damage are very poor or completely absent. Comparing developed countries, table grape is one of the fruits with the highest input of technology, while in developing countries the cost of labor is low but the purchase of the equipment is very high due to financial situation. Hence the low quality and quantity of grape are influenced by poor management methods, such as non-availability of experts and lack of technical guidance in the study site. Thereby, the study suggested that improved agricultural extension services and managerial skills could contribute to addressing the problems.

Keywords: constraints, effect, management, Kabul

Procedia PDF Downloads 112

Authors: Yung-Chung Chuang

Abstract:

The cultural layers of archaeological sites are mainly affected by surface land use, land cover, and root system of surface vegetation. For this reason, continuous monitoring of land use and land cover change is important for archaeological sites protection and management. However, in actual operation, on-site investigation and orthogonal photograph interpretation require a lot of time and manpower. For this reason, it is necessary to perform a good alternative for surface vegetation survey in an automated or semi-automated manner. In this study, we applied semi-automatic digital aerial survey technology and canopy characters classification with very high-resolution aerial photographs for surface vegetation interpretation of archaeological sites. The main idea is based on different landscape or forest type can easily be distinguished with canopy characters (e.g., specific texture distribution, shadow effects and gap characters) extracted by semi-automatic image classification. A novel methodology to classify the shape of canopy characters using landscape indices and multivariate statistics was also proposed. Non-hierarchical cluster analysis was used to assess the optimal number of canopy character clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy character classification (seven categories). Therefore, people could easily predict the forest type and vegetation land cover by corresponding to the specific canopy character category. The results showed that the semi-automatic classification could effectively extract the canopy characters of forest and vegetation land cover. As for forest type and vegetation type prediction, the average prediction accuracy reached 80.3%~91.7% with different sizes of test frame. It represented this technology is useful for archaeological site survey, and can improve the classification efficiency and data update rate.

Keywords: digital aerial survey, canopy characters classification, archaeological sites, multivariate statistics

Procedia PDF Downloads 142

Authors: Filippo Portera

Abstract:

Usually standard algorithms employ a loss where each error is the mere absolute difference between the true value and the prediction, in case of a regression task. In the present, we present several error weighting schemes that are a generalization of the consolidated routine. We study both a binary classification model for Support Vextor Classification and a regression net for Multylayer Perceptron. Results proves that the error is never worse than the standard procedure and several times it is better.

Keywords: loss, binary-classification, MLP, weights, regression

Procedia PDF Downloads 95

Authors: Evangeline Mbah, Margret Okeke, Agbo Joseph

Abstract:

Agriculture was primarily a rural activity in Nigeria, but due to increasing demand for food and jobs for many urban dwellers, it became necessary for urban households to embark on farming as a means of improving household food security and additional income for economic empowerment. Urban agriculture serves as a veritable tool for poverty reduction among people living in urban areas mostly low-income earners and unemployed. The survey was conducted to identify key challenges encountered by households in Makurdi metropolis of Benue state, Nigeria who are engaged in urban agriculture. A well-structured questionnaire was used to collect data from a sample of respondents used for the study. Data were analyzed using frequency, percentage, mean score and standard deviation. Results show that a greater percentage (46.0%) of the respondents engaged in cultivation of leafy vegetable, 22.0% cultivated cassava, 21.0% planted sweet potato, 18.0% cultivated tomato while 56.0% reared poultry, 23.0% kept goat, among others. Sources of agricultural information indicated by the respondents were family members/relations (85.0%), friends/neighbours (73.0%), radio (68.0%), extension agents (57.0%), etc. Major challenges encountered by the respondents in urban agriculture include inadequate size of farmland (M= 2.72), lack of access to credit facilities (M= 2.63), lack of funds (M= 2.50), high cost of labour (M= 2.49), insecurity of lands (M= 2.46), theft of crops at maturity (M= 2.38), lack of farm inputs such as improved varieties of seeds, fertilizer and exotic breeds of livestock (M= 2.23), destruction of crops by stray farm animals (M= 1.96), among others. The study recommends that there is a need for adequate provision of farm inputs by the government at all levels at a subsidized rate in order to reduce the cost of production and enhance optimum productivity.

Keywords: urban, agriculture, household, challenges, Makurdi, Nigeria

Procedia PDF Downloads 134

Authors: Azad Jarrahian, Ehsan Heidaryan

Abstract:

Matrix acidizing treatments can yield impressive production increase if properly applied. In this study, carbonate samples taken from Ahwaz Oilfield have undergone static solubility, sludge, emulsion, and core flooding tests. In each test interaction of acid and rock is reported and at the end it has been shown that how initial permeability and type of acid affects the overall treatment efficiency.

Keywords: carbonate acidizing, organic acids, spending rate, acid penetration, incomplete spending.

Procedia PDF Downloads 437

Authors: Auwal Ibrahim Magashi, Lawan Dan Larai Fagwalawa, Muhammad Bello Ibrahim

Abstract:

A research was conducted to study genetic variability of some quantitative traits in varieties of cowpea (Vigna unguiculata L. [Walp]) under water stressed from Zaria, Nigeria. Seeds of seven varieties of cowpea (Sampea 1, Sampea 2, IAR1074, Sampea 7, Sampea 8, Sampea 10 and Sampea 12) collected from Institute for Agricultural Research (IAR), Samaru, Zaria were screened for water stressed tolerance. The seeds were then sown in poly bags containing sandy-loam arranged in Completely Randomized Design with three replications for quantitative traits evaluation. The nutritional composition of the seeds obtained from the water stress tolerant varieties of cowpea were analyzed. The result obtained revealed highly significant difference (P ≤ 0.01) in the effects of water stress on the number of wilted and dead plants at 40 days after sowing (DAS) and significant (P ≤ 0.05) 34 DAS. However, sampea 10 has the highest mean performance in terms of number of wilted plants at 34 DAS while sampea 2 and IAR 1074 has the lowest mean performance. However, sampea 7 was found to have the highest mean performance for the number of wilted plants at 40 DAS and sampea 2 is lowest. The result for quantitative traits study indicated highly significant difference (P ≤ 0.01) in the plant height, number of days to 50% flowering, number of days to maturity, number of pods per plant, pod length, number of seeds per plant and 100 seed weight; and significant (P ≤ 0.05) at seedling height and number of branches per plant. Similarly, IAR1074 was found to have high performance in terms of most of the quantitative traits under study. However, sampea 8 has the highest mean performance at nutritional level. It was therefore concluded that, all the seven cowpea genotypes were water stress tolerant and produced considerable yield that contained significant nutrients. It was recommended that IAR1074 should be grown for yield while sampea 8 should be grown for protein supplements.

Keywords: cowpea, genetic variability, quantitative traits, water stress

Procedia PDF Downloads 157

Authors: Bliss Singhal

Abstract:

Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.

Keywords: intractable epilepsy, seizure, deep learning, prediction, electroencephalogram channels

Procedia PDF Downloads 84

Authors: Gayathri Nagarajan, L. D. Dhinesh Babu

Abstract:

Health care is one of the prominent industries that generate voluminous data thereby finding the need of machine learning techniques with big data solutions for efficient processing and prediction. Missing data, incomplete data, real time streaming data, sensitive data, privacy, heterogeneity are few of the common challenges to be addressed for efficient processing and mining of health care data. In comparison with other applications, accuracy and fast processing are of higher importance for health care applications as they are related to the human life directly. Though there are many machine learning techniques and big data solutions used for efficient processing and prediction in health care data, different techniques and different frameworks are proved to be effective for different applications largely depending on the characteristics of the datasets. In this paper, we present a framework that uses ensemble machine learning technique gradient boosted trees for data classification in health care big data. The framework is built on Spark platform which is fast in comparison with other traditional frameworks. Unlike other works that focus on a single technique, our work presents a comparison of six different machine learning techniques along with gradient boosted trees on datasets of different characteristics. Five benchmark health care datasets are considered for experimentation, and the results of different machine learning techniques are discussed in comparison with gradient boosted trees. The metric chosen for comparison is misclassification error rate and the run time of the algorithms. The goal of this paper is to i) Compare the performance of gradient boosted trees with other machine learning techniques in Spark platform specifically for health care big data and ii) Discuss the results from the experiments conducted on datasets of different characteristics thereby drawing inference and conclusion. The experimental results show that the accuracy is largely dependent on the characteristics of the datasets for other machine learning techniques whereas gradient boosting trees yields reasonably stable results in terms of accuracy without largely depending on the dataset characteristics.

Keywords: big data analytics, ensemble machine learning, gradient boosted trees, Spark platform

Procedia PDF Downloads 240

Authors: Shingo Murakami, Shinichi Enoki

Abstract:

In structures, stress concentration is a factor of fatigue fracture. Basically, the stress concentration is a phenomenon that should be avoided. However, it is difficult to avoid the stress concentration. Therefore, relaxation of the stress concentration is important. The stress concentration arises from notches and circular holes. There is a relaxation method that a composite patch covers a notch and a circular hole. This relaxation method is used to repair aerial wings, but it is not systematized. Composites are more expensive than single materials. Accordingly, we propose the relaxation method that a single material patch covers a notch and a circular hole, and aim to systematize this relaxation method. We performed FEA (Finite Element Analysis) about an object by using a three-dimensional FEA model. The object was that a patch adheres to a plate with a circular hole. And, a uniaxial tensile load acts on the patched plate with a circular hole. In the three-dimensional FEA model, it is not easy to model the adhesion layer. Basically, the yield stress of the adhesive is smaller than that of adherents. Accordingly, the adhesion layer gets to plastic deformation earlier than the adherents under the yield load of adherents. Therefore, we propose the three-dimensional FEA model which is applied a nonlinear elastic region to the adhesion layer. The nonlinear elastic region was calculated by a bilinear approximation. We compared the analysis results with the tensile test results to confirm whether the analysis model has usefulness. As a result, the analysis results agreed with the tensile test results. And, we confirmed that the analysis model has usefulness. As a result that the three-dimensional FEA model was used to the analysis, it was confirmed that an out-of-plane deformation occurred to the patched plate with a circular hole. The out-of-plane deformation causes stress increase of the patched plate with a circular hole. Therefore, we investigated that the out-of-plane deformation affects relaxation of the stress concentration in the plate with a circular hole on this relaxation method. As a result, it was confirmed that the out-of-plane deformation inhibits relaxation of the stress concentration on the plate with a circular hole.

Keywords: stress concentration, patch, out-of-plane deformation, Finite Element Analysis

Procedia PDF Downloads 301

Authors: Ehsan Pegah, Huabei Liu

Abstract:

Characterizing the deformation properties of fill materials in a wide stress range always has been an important issue in geotechnical engineering. The hyperbolic Duncan-Chang model is a very popular model of stress-strain relationship that captures the nonlinear deformation of granular geomaterials in a very tractable manner. It consists of a particular set of the model parameters, which are generally measured from an extensive series of laboratory triaxial tests. This practice is both time-consuming and costly, especially in large projects. In addition, undesired effects caused by soil disturbance during the sampling procedure also may yield a large degree of uncertainty in the results. Accordingly, non-invasive geophysical seismic approaches may be utilized as the appropriate alternative surveys for measuring the model parameters based on the seismic wave velocities. To this end, the conventional seismic refraction profiles were carried out in the test sites with the granular fill materials to collect the seismic waves information. The acquired shot gathers are processed, from which the P- and S-wave velocities can be derived. The P-wave velocities are extracted from the Seismic Refraction Tomography (SRT) technique while S-wave velocities are obtained by the Multichannel Analysis of Surface Waves (MASW) method. The velocity values were then utilized with the equations resulting from the rigorous theories of elasticity and soil mechanics to evaluate the Duncan-Chang model parameters. The derived parameters were finally compared with those from laboratory tests to validate the reliability of the results. The findings of this study may confidently serve as the useful references for determination of nonlinear deformation parameters of granular fill geomaterials. Those are environmentally friendly and quite economic, which can yield accurate results under the actual in-situ conditions using the surface seismic methods.

Keywords: Duncan-Chang deformation parameters, granular fill materials, seismic waves velocity, multichannel analysis of surface waves, seismic refraction tomography

Procedia PDF Downloads 182

Authors: M. Pienaar, J. W. Odendaal, J. C. Smit, J. Joubert

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Simulations are commonly used to predict the bistatic radar cross section (RCS) of military targets since characterization measurements can be expensive and time consuming. It is thus important to accurately predict the bistatic RCS of targets. Computational electromagnetic (CEM) methods can be used for bistatic RCS prediction. CEM methods are divided into full-wave and asymptotic methods. Full-wave methods are numerical approximations to the exact solution of Maxwell’s equations. These methods are very accurate but are computationally very intensive and time consuming. Asymptotic techniques make simplifying assumptions in solving Maxwell's equations and are thus less accurate but require less computational resources and time. Asymptotic techniques can thus be very valuable for the prediction of bistatic RCS of electrically large targets, due to the decreased computational requirements. This study extends previous work by validating the accuracy of asymptotic techniques to predict bistatic RCS through comparison with full-wave simulations as well as measurements. Validation is done with canonical structures as well as complex realistic aircraft models instead of only looking at a complex slicy structure. The slicy structure is a combination of canonical structures, including cylinders, corner reflectors and cubes. Validation is done over large bistatic angles and at different polarizations. Bistatic RCS measurements were conducted in a compact range, at the University of Pretoria, South Africa. The measurements were performed at different polarizations from 2 GHz to 6 GHz. Fixed bistatic angles of β = 30.8°, 45° and 90° were used. The measurements were calibrated with an active calibration target. The EM simulation tool FEKO was used to generate simulated results. The full-wave multi-level fast multipole method (MLFMM) simulated results together with the measured data were used as reference for validation. The accuracy of physical optics (PO) and geometrical optics (GO) was investigated. Differences relating to amplitude, lobing structure and null positions were observed between the asymptotic, full-wave and measured data. PO and GO were more accurate at angles close to the specular scattering directions and the accuracy seemed to decrease as the bistatic angle increased. At large bistatic angles PO did not perform well due to the shadow regions not being treated appropriately. PO also did not perform well for canonical structures where multi-bounce was the main scattering mechanism. PO and GO do not account for diffraction but these inaccuracies tended to decrease as the electrical size of objects increased. It was evident that both asymptotic techniques do not properly account for bistatic structural shadowing. Specular scattering was calculated accurately even if targets did not meet the electrically large criteria. It was evident that the bistatic RCS prediction performance of PO and GO depends on incident angle, frequency, target shape and observation angle. The improved computational efficiency of the asymptotic solvers yields a major advantage over full-wave solvers and measurements; however, there is still much room for improvement of the accuracy of these asymptotic techniques.

Keywords: asymptotic techniques, bistatic RCS, geometrical optics, physical optics

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Authors: Mohammad Javad Behzadnia, Amir Bahador Boroumand

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Introduction: Limited facility situations require allocating the most available resources for most casualties. Accordingly, Traumatic Brain Injury (TBI) is the one that may need to transport the patient as soon as possible. In a mass casualty event, deciding when the facilities are restricted is hard. The Extended Glasgow Outcome Score (GOSE) has been introduced to assess the global outcome after brain injuries. Therefore, we aimed to evaluate the prognostic factors associated with GOSE. Materials and Methods: In a multicenter cross-sectional study conducted on 144 patients with TBI admitted to trauma emergency centers. All the patients with isolated TBI who were mentally and physically healthy before the trauma entered the study. The patient’s information was evaluated, including demographic characteristics, duration of hospital stays, mechanical ventilation on admission laboratory measurements, and on-admission vital signs. We recorded the patients’ TBI-related symptoms and brain computed tomography (CT) scan findings. Results: GOSE assessments showed an increasing trend by the comparison of on-discharge (7.47 ± 1.30), within a month (7.51 ± 1.30), and within three months (7.58 ± 1.21) evaluations (P < 0.001). On discharge, GOSE was positively correlated with Glasgow Coma Scale (GCS) (r = 0.729, P < 0.001) and motor GCS (r = 0.812, P < 0.001), and inversely with age (r = −0.261, P = 0.002), hospitalization period (r = −0.678, P < 0.001), pulse rate (r = −0.256, P = 0.002) and white blood cell (WBC). Among imaging signs and trauma-related symptoms in univariate analysis, intracranial hemorrhage (ICH), interventricular hemorrhage (IVH) (P = 0.006), subarachnoid hemorrhage (SAH) (P = 0.06; marginally at P < 0.1), subdural hemorrhage (SDH) (P = 0.032), and epidural hemorrhage (EDH) (P = 0.037) were significantly associated with GOSE at discharge in multivariable analysis. Conclusion: Our study showed some predictive factors that could help to decide which casualty should transport earlier to a trauma center. According to the current study findings, GCS, pulse rate, WBC, and among imaging signs and trauma-related symptoms, ICH, IVH, SAH, SDH, and EDH are significant independent predictors of GOSE at discharge in TBI patients.

Keywords: field, Glasgow outcome score, prediction, traumatic brain injury.

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Authors: Fei Sun, Meilan Xu, Jianghui Zhu, Maria Stefanie Dwiyanti, Cheolwoo Park, Fanjiang Kong, Baohui Liu, Tetsuya Yamada, Jun Abe

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Reduced or lack of sensitivity to long daylengths is a key character for soybean, a short-day crop, to adapt to higher latitudinal environments. However, the photoperiod-insensitivity often results in a reduction of the duration of vegetative growth and final yield. To overcome this limitation, a photoperiod insensitive line (RIL16) was developed in this study that delayed flowering from the recombinant inbred population derived from a cross between a photoperiod-insensitive cultivar AGS292 and a late-flowering Thai cultivar K3. Expression analyses under SD and LD conditions revealed that the expression levels of FLOWERING LOCUS T (FT) orthologues, FT2a and FT5a, were lowered in RIL16 relative to AGS292, although the expression of E1, a soybean-specific suppressor for FTs, was inhibited in both conditions. A soybean orthologue of TARGET OF EAT1 (TOE1), another suppressor of FT, showed an upregulated expression in RIL16, which appeared to reflect a lower expression of miR172a. Our data suggest that the delayed flowering of RIL16 most likely is controlled by genes involved in an age-dependent pathway in flowering. The QTL analysis based on 1,125 SNPs obtained from Restriction Site Associated DNA Sequencing revealed two major QTLs for flowering dates in Chromosome 16 and two minor QTLs in Chromosome 4, all of which accounted for 55% and 48% of the whole variations observed in natural day length and artificially-induced long day length conditions, respectively. The intervals of the major QTLs harbored FT2a and FT5a, respectively, on the basis of annotated genes in the Williams 82 reference genome. Sequencing analysis further revealed a nonsynonymous mutation in FT2a and an SNP in the 3′ UTR region of FT5a. A further study may elucidate a detailed mechanism underlying the QTL for late flowering. The alleles from K3 at the two QTLs can be used singly or in combination to retain an appropriate duration of vegetative growth to maximize the final yield of photoperiod-insensitive soybeans.

Keywords: FT genes, miR72a, photoperiod-insensitive, soybean flowering

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Authors: D. Dannehl, Z. Taylor, J. Suhl, L. Miranda, R., Ulrichs, C., Salazar, E. Fitz-Rodriguez, I. Lopez-Cruz, A. Rojano-Aguilar, G. Navas-Gomez, U. Schmidt

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Growing environmental and sustainability concerns have driven continual modernization of horticultural practices, especially for urban farming. Controlled environment and soilless production methods are increasing in popularity because of their efficient resource use and intensive cropping capabilities. However, some popular substrates used for hydroponic cultivation, particularly rock wool, represent a large environmental burden in regard to their manufacture and disposal. Substrate-less hydroponic systems are effective in producing short cropping cycle plants such as lettuce or herbs, but less information is available for the production of plants with larger root-systems and longer cropping times. Here, we investigated the viability of a hybrid aeroponic/nutrient film technique (AP/NFT) system for the cultivation of greenhouse tomatoes (Solanum lycopersicum ‘Panovy’). The plants grown in the AP/NFT system had a more compact phenotype, accumulated more Na⁺ and less P and S than the rock wool grown counterparts. Due to forced irrigation interruptions, we propose that the differences observed were cofounded by the differing severity of water-stress for plants with and without substrate. They may also be caused by a higher root zone temperature predominant in plants exposed to AP/NFT. However, leaf area, stem diameter, and number of trusses did not differ significantly. The same was found for leaf pigments and plant photosynthetic efficiency. Overall, the AP/NFT system appears to be viable for the production of greenhouse tomato, enabling the environment to be relieved by way of lessening rock wool usage.

Keywords: closed aeroponic systems, fruit quality, nutrient dynamics, substrate waste reduction, urban farming systems, water savings

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Authors: Neha Bhadauria, Indu Gaur, Shilpi Shilpi, Susmita Goswami, Prabir K. Paul

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The aerial surface of plants colonized by Human Enteric Pathogens ()has been implicated in outbreaks of enteric diseases in humans. Practice of organic farming primarily using animal dung as manure and sewage water for irrigation are the most significant source of enteric pathogens on the surface of leaves, fruits and vegetables. The present work aims to have an insight into the molecular mechanism of interaction of Human Enteric Pathogens or their metabolites with cell wall receptors in plants. Tomato plants grown under aseptic conditions at 12 hours L/D photoperiod, 25±1°C and 75% RH were inoculated individually with S. fonticola and K. pneumonia. The leaves from treated plants were sampled after 24 and 48 hours of incubation. The cell wall and cytoplasmic proteins were extracted and isocratically separated on 1D SDS-PAGE. The sampled leaves were also subjected to formaldehyde treatment prior to isolation of cytoplasmic proteins to study protein-protein interactions induced by Human Enteric Pathogens. Protein bands extracted from the gel were subjected to MALDI-TOF-TOF MS analysis. The foremost interaction of Human Enteric Pathogens on the plant surface was found to be cell wall bound receptors which possibly set ups a wave a critical protein-protein interaction in cytoplasm. The study revealed the expression and suppression of specific cytoplasmic and cell wall-bound proteins, some of them being important components of signaling pathways. The results also demonstrated HEP induced rearrangement of signaling pathways which possibly are crucial for adaptation of these pathogens to plant surface. At the end of the study, it can be concluded that controlling the over-expression or suppression of these specific proteins rearrange the signaling pathway thus reduces the outbreaks of food-borne illness.

Keywords: cytoplasmic protein, cell wall-bound protein, Human Enteric Pathogen (HEP), protein-protein interaction

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Authors: Esra Zengin, Sinan Akkar

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Reliable and accurate prediction of nonlinear structural response requires specification of appropriate earthquake ground motions to be used in nonlinear time history analysis. The current research has mainly focused on selection and manipulation of real earthquake records that can be seen as the most critical step in the performance based seismic design and assessment of the structures. Utilizing amplitude scaled ground motions that matches with the target spectra is commonly used technique for the estimation of nonlinear structural response. Representative ground motion ensembles are selected to match target spectrum such as scenario-based spectrum derived from ground motion prediction equations, Uniform Hazard Spectrum (UHS), Conditional Mean Spectrum (CMS) or Conditional Spectrum (CS). Different sets of criteria exist among those developed methodologies to select and scale ground motions with the objective of obtaining robust estimation of the structural performance. This study presents ground motion selection and scaling procedure that considers the spectral variability at target demand with the level of ground motion dispersion. The proposed methodology provides a set of ground motions whose response spectra match target median and corresponding variance within a specified period interval. The efficient and simple algorithm is used to assemble the ground motion sets. The scaling stage is based on the minimization of the error between scaled median and the target spectra where the dispersion of the earthquake shaking is preserved along the period interval. The impact of the spectral variability on nonlinear response distribution is investigated at the level of inelastic single degree of freedom systems. In order to see the effect of different selection and scaling methodologies on fragility curve estimations, results are compared with those obtained by CMS-based scaling methodology. The variability in fragility curves due to the consideration of dispersion in ground motion selection process is also examined.

Keywords: ground motion selection, scaling, uncertainty, fragility curve

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Authors: Vikrant Gupta, Amrit Goswami

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The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

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Authors: Aishi Kundu, Elhan Raza

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"Necessity is the mother of invention" - Rapid increase in the global human population has directed the agricultural domain toward machine learning. The basic need of human beings is considered to be food which can be satisfied through farming. Farming is one of the major revenue generators for the Indian economy. Agriculture is not only considered a source of employment but also fulfils humans’ basic needs. So, agriculture is considered to be the source of employment and a pillar of the economy in developing countries like India. This paper provides a brief review of the progress made in implementing Machine Learning in the agricultural sector. Accurate predictions are necessary at the right time to boost production and to aid the timely and systematic distribution of agricultural commodities to make their availability in the market faster and more effective. This paper includes a thorough analysis of various machine learning algorithms applied in different aspects of agriculture (crop management, soil management, water management, yield tracking, livestock management, etc.).Due to climate changes, crop production is affected. Machine learning can analyse the changing patterns and come up with a suitable approach to minimize loss and maximize yield. Machine Learning algorithms/ models (regression, support vector machines, bayesian models, artificial neural networks, decision trees, etc.) are used in smart agriculture to analyze and predict specific outcomes which can be vital in increasing the productivity of the Agricultural Food Industry. It is to demonstrate vividly agricultural works under machine learning to sensor data. Machine Learning is the ongoing technology benefitting farmers to improve gains in agriculture and minimize losses. This paper discusses how the irrigation and farming management systems evolve in real-time efficiently. Artificial Intelligence (AI) enabled programs to emerge with rich apprehension for the support of farmers with an immense examination of data.

Keywords: machine Learning, artificial intelligence, crop management, precision farming, smart farming, pre-harvesting, harvesting, post-harvesting

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Authors: Tia Kristian Tajnšek, Matjaž Mazaj, Nataša Zabukovec Logar

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Metal-organic frameworks (MOFs) with their specific properties and the possibility of tuning the structure represent excellent candidates for use in the biomedical field. Their advantage lies in large pore surfaces and volumes, as well as the possibility of using bio-friendly or bioactive constituents. So-called bioMOFs are representatives of MOFs, which are constructed from at least one biomolecule (metal, a small bioactive molecule in metal clusters and/or linker) and are intended for bio-application (usually in the field of medicine; most commonly drug delivery). When designing a bioMOF for biomedical applications, we should adhere to some guidelines for an improved toxicological profile of the material. Such as (i) choosing an endogenous/nontoxic metal, (ii) GRAS (generally recognized as safe) linker, and (iii) nontoxic solvents. Design and synthesis of bioNICS-1 (bioMOF of National Institute of Chemistry Slovenia – 1) consider all these guidelines. Zinc (Zn) was chosen as an endogenous metal with an agreeable recommended daily intake (RDI) and LD50 value, and ascorbic acid (Vitamin C) was chosen as a GRAS and active linker. With these building blocks, we have synthesized a bioNICS-1 material. The synthesis was done in ethanol using a solvothermal method. The synthesis protocol was further optimized in three separate ways. Optimization of (i) synthesis parameters to improve the yield of the synthesis, (ii) input reactant ratio and addition of specific modulators for production of larger crystals, and (iii) differing of the heating source (conventional, microwave and ultrasound) to produce nano-crystals. With optimization strategies, the synthesis yield was increased. Larger crystals were prepared for structural analysis with the use of a proper species and amount of modulator. Synthesis protocol was adjusted to different heating sources, resulting in the production of nano-crystals of bioNICS-1 material. BioNICS-1 was further activated in ethanol and structurally characterized, resolving the crystal structure of new material.

Keywords: ascorbic acid, bioMOF, MOF, optimization, synthesis, zinc ascorbate

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Authors: C. Deangeli, P. Obentaku Obenebot, O. Omwanghe

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The study focuses on the analysis of wellbore instability in rock masses affected by weakness planes. The occurrence of failure in such a type of rocks can occur in the rock matrix and/ or along the weakness planes, in relation to the mud weight gradient. In this case the simple Kirsch solution coupled with a failure criterion cannot supply a suitable scenario for borehole instabilities. Two different numerical approaches have been used in order to investigate the onset of local failure at the wall of a borehole. For each type of approach the influence of the inclination of weakness planes has been investigates, by considering joint sets at 0°, 35° and 90° to the horizontal. The first set of models have been carried out with FLAC 2D (Fast Lagrangian Analysis of Continua) by considering the rock material as a continuous medium, with a Mohr Coulomb criterion for the rock matrix and using the ubiquitous joint model for accounting for the presence of the weakness planes. In this model yield may occur in either the solid or along the weak plane, or both, depending on the stress state, the orientation of the weak plane and the material properties of the solid and weak plane. The second set of models have been performed with PFC2D (Particle Flow code). This code is based on the Discrete Element Method and considers the rock material as an assembly of grains bonded by cement-like materials, and pore spaces. The presence of weakness planes is simulated by the degradation of the bonds between grains along given directions. In general the results of the two approaches are in agreement. However the discrete approach seems to capture more complex phenomena related to local failure in the form of grain detachment at wall of the borehole. In fact the presence of weakness planes in the discontinuous medium leads to local instability along the weak planes also in conditions not predicted from the continuous solution. In general slip failure locations and directions do not follow the conventional wellbore breakout direction but depend upon the internal friction angle and the orientation of the bedding planes. When weakness plane is at 0° and 90° the behaviour are similar to that of a continuous rock material, but borehole instability is more severe when weakness planes are inclined at an angle between 0° and 90° to the horizontal. In conclusion, the results of the numerical simulations show that the prediction of local failure at the wall of the wellbore cannot disregard the presence of weakness planes and consequently the higher mud weight required for stability for any specific inclination of the joints. Despite the discrete approach can simulate smaller areas because of the large number of particles required for the generation of the rock material, however it seems to investigate more correctly the occurrence of failure at the miscroscale and eventually the propagation of the failed zone to a large portion of rock around the wellbore.

Keywords: continuous- discontinuous, numerical modelling, weakness planes wellbore, FLAC 2D

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