Search results for: toxicity prediction

Authors: Sonam Agarwal, Renu Bist

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Current study emphasizes the toxic effects of dichlorvos on serum in terms of oxidative stress. Meanwhile, a protective action of bacoside A and bromelain was investigated against the biochemical alterations in serum. The experimental design included six groups of mice: saline was given as a vehicle to the control mice (group I). Mice belonging to groups II, III and IV, were administered with dichlorvos (40 mg/kg b.w.), bromelain and bacoside A, respectively. The fifth group received a combination of bromelain and bacoside A. In group VI, Bacoside A, and bromelain were administered 20 minutes prior to exposure of dichlorvos. Thiobarbituric acid reactive substances (TBARS), protein carbonyl content (PCC), catalase (CAT), superoxide dismutase (SOD), glutathione peroxidase (GPx) and reduced glutathione (GSH) level were used as biochemical test of toxic action for dichlorvos intoxication. Significantly increased TBARS and PCC level in second group suggests that dichlorvos enhances the production of free radicals in serum of mice (p< 0.05). Antioxidants treatment significantly decreased the levels of TBARS and PCC (p< 0.05). Dichlorvos administration causes a significant reduction in the level of CAT, SOD, GPx and GSH (p< 0.05) which was restored significantly by co-administration of bromelain and Bacoside A in dichlorvos exposed mice (p< 0.05). Treatment of bromelain and Bacoside A in combination served as better scavengers of toxicity induced by dichlorvos.

Keywords: bacoside A, bromelain, dichlorvos, serum

Procedia PDF Downloads 318

Authors: Faizul Azly Abd Dzubir, Muhammad F. Othman

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Conventional structural health monitoring approach for mechanical equipment uses inspection data from Non-Destructive Testing (NDT) during plant shut down window and fitness for service evaluation to estimate the integrity of the equipment that is prone to crack damage. Yet, this forecast is fraught with uncertainty because it is often based on assumptions of future operational parameters, and the prediction is not continuous or online. Advanced Diagnostic and Prognostic Technology (ADaPT) uses Acoustic Emission (AE) technology and a stochastic prognostic model to provide real-time monitoring and prediction of mechanical defects or cracks. The forecast can help the plant authority handle their cracked equipment before it ruptures, causing an unscheduled shutdown of the facility. The ADaPT employs process historical data trending, finite element analysis, fitness for service, and probabilistic statistical analysis to develop a prediction model for crack initiation and growth due to mechanical damage. The prediction model is combined with live equipment operating data for real-time prediction of the remaining life span owing to fracture. ADaPT was devised at a hot combined feed exchanger (HCFE) that had suffered creep crack damage. The ADaPT tool predicts the initiation of a crack at the top weldment area by April 2019. During the shutdown window in April 2019, a crack was discovered and repaired. Furthermore, ADaPT successfully advised the plant owner to run at full capacity and improve output by up to 7% by April 2019. ADaPT was also used on a coke drum that had extensive fatigue cracking. The initial cracks are declared safe with ADaPT, with remaining crack lifetimes extended another five (5) months, just in time for another planned facility downtime to execute repair. The prediction model, when combined with plant information data, allows plant operators to continuously monitor crack propagation caused by mechanical damage for improved maintenance planning and to avoid costly shutdowns to repair immediately.

Keywords: mechanical damage, cracks, continuous monitoring tool, remaining life, acoustic emission, prognostic model

Procedia PDF Downloads 60

Authors: Prashant Verma

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Commercial banks are facing immense pressure, including financial disintermediation, interest rate volatility and digital ways of finance. Retaining an existing customer is 5 to 25 less expensive than acquiring a new one. This paper explores customer churn prediction, based on various statistical & machine learning models and uses under-sampling, to improve the predictive power of these models. The results show that out of the various machine learning models, Random Forest which predicts the churn with 78% accuracy, has been found to be the most powerful model for the scenario. Customer vintage, customer’s age, average balance, occupation code, population code, average withdrawal amount, and an average number of transactions were found to be the variables with high predictive power for the churn prediction model. The model can be deployed by the commercial banks in order to avoid the customer churn so that they may retain the funds, which are kept by savings bank (SB) customers. The article suggests a customized campaign to be initiated by commercial banks to avoid SB customer churn. Hence, by giving better customer satisfaction and experience, the commercial banks can limit the customer churn and maintain their deposits.

Keywords: savings bank, customer churn, customer retention, random forests, machine learning, under-sampling

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Authors: Pitchayanin Sukholthaman

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Gradually accumulated, the enormous amount of waste has caused tremendous adverse impacts to the world. Bangkok, Thailand, is chosen as an urban city of a developing country having coped with serious MSW problems due to the vast amount of waste generated, ineffective and improper waste management problems. Waste generation is the most important factor for successful planning of MSW management system. Thus, the prediction of MSW is a very important role to understand MSW distribution and characteristic; to be used for strategic planning issues. This study aims to find influencing variables that affect the amount of Bangkok MSW generation quantity.

Keywords: MSW generation, MSW quantity prediction, MSW management, multiple regression, Bangkok

Procedia PDF Downloads 401

Authors: C. E. Ihejirika, M. I. Nwachukwu, R. F. Njoku-Tony, O. C. Ihejirika, U. O. Enwereuzoh, E. O. Imo, D. C. Ashiegbu

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Disposal of industrial solid wastes in the environment is a major environmental challenge. This study investigated the effects of calcium carbide waste dumpsites on soil quality. Soil samples were collected with hand auger from three different dumpsites at varying depths and made into composite samples. Samples were subjected to standard analytical procedures. pH varied from 10.38 to 8.28, nitrate from 5.6mg/kg to 9.3mg/kg, phosphate from 8.8mg/kg to 12.3mg/kg, calcium carbide reduced from 10% to to 3%. Calcium carbide was absent in control soil samples. Bacterial counts from dumpsites ranged from 1.8 x 105cfu/g - 2.5 x 105cfu/g while fungal ranged from 0.8 x 103cfu/g - 1.4 x 103cfu/g. Bacterial isolates included Pseudomonas spp, Flavobacterium spp, and Achromobacter spp, while fungal isolates include Penicillium notatum, Aspergillus niger, and Rhizopus stolonifer. No organism was isolated from the dumpsites at soil depth of 0-15 cm, while there were isolates from other soil depths. Toxicity might be due to alkaline condition of the dumpsite. Calcium carbide might be bactericidal and fungicidal leading to cellular physiology, growth retardation, death, general loss of biodiversity and reduction of ecosystem processes. Detoxification of calcium carbide waste before disposal on soil might be the best option in management.

Keywords: biodiversity, calcium-carbide, denitrification, toxicity

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Authors: Kudzanayi Chiteka, Wellington Makondo

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The whole world is on the drive to go green owing to the negative effects of burning fossil fuels. Therefore, there is immediate need to identify and utilise alternative renewable energy sources. Among these energy sources solar energy is one of the most dominant in Zimbabwe. Solar power plants used to generate electricity are entirely dependent on solar radiation. For planning purposes, solar radiation values should be known in advance to make necessary arrangements to minimise the negative effects of the absence of solar radiation due to cloud cover and other naturally occurring phenomena. This research focused on the prediction of Global Horizontal Irradiation values for the sixth day given values for the past five days. Artificial intelligence techniques were used in this research. Three models were developed based on Support Vector Machines, Radial Basis Function, and Feed Forward Back-Propagation Artificial neural network. Results revealed that Support Vector Machines gives the best results compared to the other two with a mean absolute percentage error (MAPE) of 2%, Mean Absolute Error (MAE) of 0.05kWh/m²/day root mean square (RMS) error of 0.15kWh/m²/day and a coefficient of determination of 0.990. The other predictive models had prediction accuracies of MAPEs of 4.5% and 6% respectively for Radial Basis Function and Feed Forward Back-propagation Artificial neural network. These two models also had coefficients of determination of 0.975 and 0.970 respectively. It was found that prediction of GHI values for the future days is possible using artificial intelligence-based predictive models.

Keywords: solar energy, global horizontal irradiation, artificial intelligence, predictive models

Procedia PDF Downloads 254

Authors: Narjes Ebrahimi, Soheila Alyasin, Navid Nezafat, Hossein Esmailzadeh, Younes Ghasemi, Seyed Hesamodin Nabavizadeh

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Immunotherapy with allergy vaccines is of great importance in allergen-specific immunotherapy. In recent years, B-cell epitope-based vaccines have attracted considerable attention and the prediction of epitopes is crucial to design these types of allergy vaccines. B-cell epitopes might be linear or conformational. The prerequisite for the identification of conformational epitopes is the information about allergens' tertiary structures. Bioinformatics approaches have paved the way towards the design of epitope-based allergy vaccines through the prediction of tertiary structures and epitopes. Mite allergens are one of the major allergy contributors. Several mite allergens can elicit allergic reactions; however, their structures and epitopes are not well established. So, B-cell epitopes of various groups of mite allergens (24 allergens in 6 allergen groups) were predicted in the present work. Tertiary structures of 17 allergens with unknown structure were predicted and refined with RaptorX and GalaxyRefine servers, respectively. The predicted structures were further evaluated by Rampage, ProSA-web, ERRAT and Verify 3D servers. Linear and conformational B-cell epitopes were identified with Ellipro, Bcepred, and DiscoTope 2 servers. To improve the accuracy level, consensus epitopes were selected. Fifty-four conformational and 133 linear consensus epitopes were predicted. Furthermore, overlapping epitopes in each allergen group were defined, following the sequence alignment of the allergens in each group. The predicted epitopes were also compared with the experimentally identified epitopes. The presented results provide valuable information for further studies about allergy vaccine design.

Keywords: B-cell epitope, Immunotherapy, In silico prediction, Mite allergens, Tertiary structure

Procedia PDF Downloads 140

Authors: Nieves M. C. Talavera-Prieto, Abel G. M. Ferreira, António T. G. Portugal, Rui J. Moreira, Jaime B. Santos

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The knowledge of biodiesel density over large ranges of temperature and pressure is important for predicting the behavior of fuel injection and combustion systems in diesel engines, and for the optimization of such systems. In this study, cottonseed oil was transesterified into biodiesel and its density was measured at temperatures between 288 K and 358 K and pressures between 0.1 MPa and 30 MPa, with expanded uncertainty estimated as ±1.6 kg.m^-3. Experimental pressure-volume-temperature (pVT) cottonseed data was used along with literature data relative to other 18 biodiesels, in order to build a database used to test the correlation of density with temperarure and pressure using the Goharshadi–Morsali–Abbaspour equation of state (GMA EoS). To our knowledge, this is the first that density measurements are presented for cottonseed biodiesel under such high pressures, and the GMA EoS used to model biodiesel density. The new tested EoS allowed correlations within 0.2 kg•m-3 corresponding to average relative deviations within 0.02%. The built database was used to develop and test a new full predictive model derived from the observed linear relation between density and degree of unsaturation (DU), which depended from biodiesel FAMEs profile. The average density deviation of this method was only about 3 kg.m-3 within the temperature and pressure limits of application. These results represent appreciable improvements in the context of density prediction at high pressure when compared with other equations of state.

Keywords: biodiesel density, correlation, equation of state, prediction

Procedia PDF Downloads 587

Authors: A. Brahma

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Analysis of deferred deformations of concrete under sustained load shows that the creep has a leading role on deferred deformations of concrete structures. Knowledge of the creep characteristics of concrete is a Necessary starting point in the design of structures for crack control. Such knowledge will enable the designer to estimate the probable deformation in pre-stressed concrete or reinforced and the appropriate steps can be taken in design to accommodate this movement. In this study, we propose a prediction model that involves the acting principal parameters on the deferred behaviour of concrete structures. For the estimation of the model parameters Levenberg-Marquardt method has proven very satisfactory. A confrontation between the experimental results and the predictions of models designed shows that it is well suited to describe the evolution of the creep of concrete structures.

Keywords: concrete structure, creep, modelling, prediction

Procedia PDF Downloads 270

Authors: Wei Tian, Jie Liang, Hammad Naveed

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Beta-barrel membrane proteins are found in the outer membrane of gram-negative bacteria, mitochondria, and chloroplasts. They carry out diverse biological functions, including pore formation, membrane anchoring, enzyme activity, and bacterial virulence. In addition, beta-barrel membrane proteins increasingly serve as scaffolds for bacterial surface display and nanopore-based DNA sequencing. Due to difficulties in experimental structure determination, they are sparsely represented in the protein structure databank and computational methods can help to understand their biophysical principles. We have developed a novel computational method to predict the 3D structure of beta-barrel membrane proteins using evolutionary coupling (EC) constraints and a reduced state space. Combined with an empirical potential function, we can successfully predict strand register at > 80% accuracy for a set of 49 non-homologous proteins with known structures. This is a significant improvement from previous results using EC alone (44%) and using empirical potential function alone (73%). Our method is general and can be applied to genome-wide structural prediction.

Keywords: beta-barrel membrane proteins, structure prediction, evolutionary constraints, reduced state space

Procedia PDF Downloads 588

Authors: Tauha Hussain Ali, Shabir Hussain Khahro, Nafees Ahmed Memon

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Accurate prediction of project time for planning and bid preparation stage should contain realistic dates. Constructors use their experience to estimate the project duration for the new projects, which is based on intuitions. It has been a constant concern to both researchers and constructors to analyze the accurate prediction of project duration for bid preparation stage. In Pakistan, such study for time cost relationship has been lacked to predict duration performance for the construction projects. This study is an attempt to explore the time cost relationship that would conclude with a mathematical model to predict the time for the drainage rehabilitation projects in the province of Sindh, Pakistan. The data has been collected from National Engineering Services (NESPAK), Pakistan and regression analysis has been carried out for the analysis of results. Significant relationship has been found between time and cost of the construction projects in Sindh and the generated mathematical model can be used by the constructors to predict the project duration for the upcoming projects of same nature. This study also provides the professionals with a requisite knowledge to make decisions regarding project duration, which is significantly important to win the projects at the bid stage.

Keywords: BTC Model, project time, relationship of time cost, regression

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Authors: E. Akpinar, A. Erol, M.F. Cakir

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Precast reinforced concrete (RC) structures are excellent alternatives for construction world all over the globe, thanks to their rapid erection phase, ease mounting process, better quality and reasonable prices. Such structures are rather popular for industrial buildings. For the sake of economic importance of such industrial buildings as well as significance of safety, like every other type of structures, performance assessment and structural risk analysis are important. Fragility curves are powerful tools for damage projection and assessment for any sort of building as well as precast structures. In this study, a comparative review of current knowledge on fragility analysis of industrial precast RC structures were presented and findings in previous studies were compiled. Effects of different structural variables, parameters and building geometries as well as soil conditions on fragility analysis of precast structures are reviewed. It was aimed to briefly present the information in the literature about the procedure of damage probability prediction including fragility curves for such industrial facilities. It is found that determination of the aforementioned structural parameters as well as selecting analysis procedure are critically important for damage prediction of industrial precast RC structures using fragility curves.

Keywords: damage prediction, fragility curve, industrial buildings, precast reinforced concrete structures

Procedia PDF Downloads 167

Authors: A. B. Amna, M. A. E. Samia, A. K. Hassan

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The clinical, pathological, hematological and biological changes in Newzealand rabbits groups given daily oral doses of 0.1,0.25 and 0.5 ml/kg body weight/day of Cpmbopogon proximus oil extract were investigated in an experiment durated for 21 days. Other than the dose co-related mortality rates, the clinical signs were observed daily after dosing to be low appetite and nervous signs including restlessness and increased consciousness. Pulmonary excretion of the oil extract led to bloody spots on the lungs, lymphocyte infiltration, congestion and edema. Renal glumeruli manifested lymphocyte infiltration in addition to shrinkages and easinophilic material in the medulla, if considered with the corticomedullary generalized necrosis and the significant changes in urea, they can explain the renal dysfunction. Hepatic malfunction was manifested by significant changes in serum alkaline phosphatase and aspartate transferases accompanied by the congested, fatty changed livers. The direct physical effect of the extracted oil was detected by the catarrhal inflammation of the intestines.There was no significant haematological change except for the slight changes in RBCs and MCVs in rabbits given the highest dose. Future work for Cpmbopogon proximus oil extract was forwarded and practical implications of the result were highlighted.

Keywords: toxicity, cymbopogon proximus (maharaib), oil extract, Newzealand rabbits

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Authors: Marjan Golmaryami, Marzieh Behzadi

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Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too.

Keywords: artificial neural network (ANN), bat algorithm, particle swarm optimization algorithm (PSO), stock exchange

Procedia PDF Downloads 527

Authors: Rosdyana Mangir Irawan Kusuma, Wei-Chun Kao, Ho-Thi Trang, Yu-Yen Ou, Kai-Lung Hua

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Stock market prediction is still a challenging problem because there are many factors that affect the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment, and economic factors. This work explores the predictability in the stock market using deep convolutional network and candlestick charts. The outcome is utilized to design a decision support framework that can be used by traders to provide suggested indications of future stock price direction. We perform this work using various types of neural networks like convolutional neural network, residual network and visual geometry group network. From stock market historical data, we converted it to candlestick charts. Finally, these candlestick charts will be feed as input for training a convolutional neural network model. This convolutional neural network model will help us to analyze the patterns inside the candlestick chart and predict the future movements of the stock market. The effectiveness of our method is evaluated in stock market prediction with promising results; 92.2% and 92.1 % accuracy for Taiwan and Indonesian stock market dataset respectively.

Keywords: candlestick chart, deep learning, neural network, stock market prediction

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Authors: R. Stevens, W.L. Bryden

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The objective of this study was to determine the efficacy of zeolite in preventing the adverse effects of aflatoxin B1 (AFB1) in broilers. A total of 540 one-day-old Ross chicks were randomly divided into nine treatments, with four replicate pens per treatment and 15 chicks per pen. The treatments included 3 Levels of AFB1 (0,1and 2 mg/kg diet) and 3 levels of zeolite (0, 1.5 and 3 %) in a 3 ×3 factorial arrangement. The experimental treatments commenced on d 7 post-hatch. A starter diet was provided from d 1 to 14, a grower diet from d 15 to 28 and a finisher diet from d 29 to d 49. Diets were based on corn and soybeans and formulated to meet the bird's requirements. The evaluated parameters were as follows: Bodyweight, daily gain, feed intake (FI), feed conversion (FC), relative weights of organs (carcass, liver, heart and abdominal fat) and clinical biochemistry parameters: alanine aminotransferase (ALT) and aspartate aminotransferase (AST). Bodyweight, daily gain and FC were significantly (P<0.05) impaired by aflatoxin. Relative weights of the liver and heart were also affected. The addition of zeolite (1.5 and 3 %) to the contaminated diets ameliorated the effects of aflatoxin, especially at the higher level of inclusion. These data demonstrate that this specific sorbent (zeolite) can protect against the toxicity of AFB1in young broiler chicks.

Keywords: aflatoxin, broiler, toxicity, zeolite

Procedia PDF Downloads 136

Authors: Azza A. Ali, Toqa M. Elnahhas, Abeer I. Abd El-Fattah, Mona M. Kamal, Karema Abu-Elfotuh

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Background: Environmental pollution with the different aluminium (Al) containing compounds especially those in industrial waste water exposes people to higher than normal levels of Al that represents an environmental risk factor. Cosmetics, Al ware, and containers are also sources of Al besides some foods and food additives. In addition to its known neurotoxicity, Al affects other body structures like skeletal system, blood cells, liver and kidney. Accumulation of Al in kidney and liver induces nephrotoxicity and hepatotoxicity. Coenzyme Q10 (CoQ10) is a pseudo-vitamin substance primarily present in the mitochondria. It is a powerful antioxidant and acts as radical scavenger. Wheat grass is a natural product that contains carbohydrates, proteins, vitamins, minerals, enzymes and has antioxidant, anti-inflammatory, anticancer and cardiovascular protection activities. Cocoa is an excellent source of iron, potent antioxidants and can protect against many diseases. Vinpocetine is an antioxidant and anti inflammatory while zinc is an essential trace element involved in cell division and its deficiency is observed in many types of liver disease. Objective: To evaluate and compare the potency of different drugs (CoQ10, wheatgrass, cocoa, vinpocetine and zinc) against nephro- and hepato-toxicity induced by Al in rats. Methods: Rats were divided to seven groups and received daily for three weeks either saline for control group or AlCl3 (70 mg/kg, IP) for Al-toxicity model groups. Five groups of Al-toxicity model (treated groups) were orally received together with Al each of the following; CoQ10 (200mg/kg), wheat grass (100mg/kg), cocoa powder (24mg/kg), vinpocetine (20mg/kg) or zinc (32mg/kg). Biochemical changes in the serum level of Alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), lactate deshydrogenase (LDH) as well as total bilirubin, lipids, cholesterol, triglycerides, glucose, proteins, creatinine and urea were measured. Liver and kidney specimens from all groups were also collected for the assessment of hepatic and nephrotic level of inflammatory mediators (TNF-α, IL-6β, nuclear factor kappa B (NF-κB), Caspase-3, oxidative parameters (MDA, SOD, TAC, NO) and DNA fragmentation. Histopathological changes in liver and kidney were also evaluated. Results: Three weeks of AlCl3 (70 mg/kg, IP) exposure induced nephro- and hepato-toxicity in rats. Treatment by the all used drugs showed protection against hazards of AlCl3. The protective effects were indicated by the significant decrease in ALT, AST, ALP, LDH as well as total bilirubin, lipids, cholesterol, triglycerides, glucose, creatinine and urea levels which were increased by Al. Liver and kidney of the treated groups showed decrease in MDA, NO, TNF-α, IL-6β, NF-κB, caspase-3 and DNA fragmentation which were increased by Al, together with significant increase in total proteins, SOD and TAC which were decreased by Al. The protection against both nephro- and hepato-toxicity was more pronounced especially with CoQ10 and wheat grass than the other used drugs. Histopathological examinations confirmed the biochemical results of toxicity and of protection. Conclusion: Protection from nephrotoxicity, hepatotoxicity and the consequent degenerations induced by Al can be achieved by using different drugs as CoQ10, wheatgrass, cocoa, vinpocetine and zinc, but CoQ10 as well as wheat grass possesses the most superior protection.

Keywords: aluminum, nephrotoxicity, hepatotoxicity, coenzyme Q10, wheatgrass, cocoa, vinpocetine, zinc

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Authors: Bendjazia Radia, Samira Kilani-Morakchi, Nadia Aribi

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Azadirachtin is a biorational insecticide commonly reported as selective to a range of beneficial insects. It is one of the most biologically active natural inhibitors of insect growth and development and it is known to be an antagonist of the juvenile hormone and 20-hydroxyecdysone (20E). However, its mechanism of action remains still unknown. In the present study, the toxicity of a commercial formulation of Azadirachtin (Neem Azal, 1% azadirachtine) was evaluated by topical application at various doses (0.1, 0.25, 0.5, 1 and 2 µg/insect) on the third instars larvae of D. melanogaster. Lethal doses (LD25: 0.28µg and LD50: 0.67µg), were evaluated by cumulated mortality at the immature stages. The effects of azadirachtin (LD25 and LD50) were then evaluated on the development (duration of the larval and pupal instars, the weight of larvae, pupa and adults) of Drosophila melanogaster. Results showed that the insecticide increased significantly the larval and pupal instar duration. A reduction of larval and pupal weight is noted under azadirachtin treatment as compared to controls. In addition, the weight of surviving adults at the two tested dose was also reduced. In conclusion, azadirachtin seemed to interfere with the functions of the endocrine system resulting in development defects.

Keywords: azadirachtin, d.melanogaster, toxicity, development

Procedia PDF Downloads 439

Authors: Ekrem Canli, Thomas Glade

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The precise prediction of rainfall in space and time is a key element to most landslide early warning systems. Unfortunately, the spatial variability of rainfall in many early warning applications is often disregarded. A common simplification is to use uniformly distributed rainfall to characterize aerial rainfall intensity. With spatially differentiated rainfall information, real-time comparison with rainfall thresholds or the implementation in process-based approaches might form the basis for improved landslide warnings. This study suggests an automated workflow from the hourly, web-based collection of rain gauge data to the generation of spatially differentiated rainfall predictions based on kriging. Because the application of kriging is usually a labor intensive task, a simplified and consequently automated variogram modeling procedure was applied to up-to-date rainfall data. The entire workflow was carried out purely with open source technology. Validation results, albeit promising, pointed out the challenges that are involved in pure distance based, automated geostatistical interpolation techniques for ever-changing environmental phenomena over short temporal and spatial extent.

Keywords: kriging, landslide early warning system, spatial rainfall prediction, variogram modelling, web scraping

Procedia PDF Downloads 264

Authors: Mala Tankam Carine, Kekeunou Sévilor, Nukenine Elias

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Storage and preservation of agricultural products remain the only conditions ensuring the almost permanent availability of foodstuffs. However, infestations due to insects and microorganisms often occur. Callosobruchus maculatus is a pest that causes a lot of damage to cowpea stocks in the tropics. Several methods are adopted to limit their damage, but the use of synthetic chemical insecticides is the most widespread. Biopesticides in sustainable agriculture respond to several environmental, economic and social concerns while offering innovative opportunities that are ecologically and economically viable for producers, workers, consumers and ecosystems. Our main objective is to evaluate the insecticidal efficacy of binary combinations of Fossilshield with root-bark powder of Securidaca longepedunculata against Callosobruchus maculatus in stored cowpea Vigna unguiculata. Laboratory bioassays were conducted in stored grains to evaluate the toxicity of root-bark powder of Securidaca longepedunculata alone or combined with diatomaceous earth Fossil-Shield ® against C. maculatus. Twenty-hour-old adults of C. maculatus were exposed to 50g of cowpea seeds treated with four doses (10, 20, 30, and 40g/kg) of root-bark powder of S. longepedunculata, on the one hand, and (0.5, 1, 1.5, and 2 g/kg) on DE and binary combinations on the other hand. 0g/kg corresponded to untreated control. Adult mortality was recorded up to 7 days (d) post-treatment, whereas the number of F1 progeny was assessed after 30 d. Weight loss and germinative ability were conducted after 120 d. All treatments were arranged according to a completely randomized block with four replicates. The combined mixture of S. longepedunculata and DE controlled the beetle faster compared to the root-bark powder of S. longepedunculata alone. According to the Co-toxicity coefficient, additive effect of binary combinations was recorded at 3-day post-exposure time with the mixture 25% FossilShield + 75% S. longepedunculata. A synergistic action was observed after 3-d post-exposure at mixture 50% FossilShield + 50% S. longepedunculata and at 1-d and 3-d post-exposure periods at mixture 75% FossilShield + 25% S. longepedunculata. The mixture 25% FossilShield + 75% S. longepedunculata induced a decreased progeny of 6 times fewer individuals for 4.5 times less weight loss and 2, 9 times more sprouted grains than with root-bark powder of S. longepedunculata. The combination of FossilShield + S. longepedunculata was more potent than root-bark powder of S. longepedunculata alone, although the root-bark powder of S. longepedunculata caused significant reduction of F1 adults compared to the control. Combined action of botanical insecticides with FossilShield as a grain protectant in an integrated pest management approach is discussed.

Keywords: diatomaceous earth, cowpea, callosobruchus maculatus, securidaca longepedunculata, combined action, co-toxicity coefficient

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Authors: Zhaopeng Zhu, Xianzhi Song, Gensheng Li, Shuo Zhu, Shiming Duan, Xuezhe Yao

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Accurate calculation of wellbore pressure is of great significance to prevent wellbore risk during drilling. The traditional mechanism model needs a lot of iterative solving procedures in the calculation process, which reduces the calculation efficiency and is difficult to meet the demand of dynamic control of wellbore pressure. In recent years, many scholars have introduced artificial intelligence algorithms into wellbore pressure calculation, which significantly improves the calculation efficiency and accuracy of wellbore pressure. However, due to the ‘black box’ property of intelligent algorithm, the existing intelligent calculation model of wellbore pressure is difficult to play a role outside the scope of training data and overreacts to data noise, often resulting in abnormal calculation results. In this study, the multi-phase flow mechanism is embedded into the objective function of the neural network model as a constraint condition, and an intelligent prediction model of wellbore pressure under the constraint condition is established based on more than 400,000 sets of pressure measurement while drilling (MPD) data. The constraint of the multi-phase flow mechanism makes the prediction results of the neural network model more consistent with the distribution law of wellbore pressure, which overcomes the black-box attribute of the neural network model to some extent. The main performance is that the accuracy of the independent test data set is further improved, and the abnormal calculation values basically disappear. This method is a prediction method driven by MPD data and multi-phase flow mechanism, and it is the main way to predict wellbore pressure accurately and efficiently in the future.

Keywords: multiphase flow mechanism, pressure while drilling data, wellbore pressure, mechanism constraints, combined drive

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Authors: Adekunle Taiwo Adenike, I. K. Ogundoyin

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Lassa fever is a neglected tropical virus that has become a significant public health issue in Nigeria, with the country having the greatest burden in Africa. This paper presents a Geo-Computational Model for Analysis and Prediction of Lassa Fever Dynamics and Outbreaks in Nigeria. The model investigates the dynamics of the virus with respect to environmental factors and human populations. It confirms the role of the rodent host in virus transmission and identifies how climate and human population are affected. The proposed methodology is carried out on a Linux operating system using the OSGeoLive virtual machine for geographical computing, which serves as a base for spatial ecology computing. The model design uses Unified Modeling Language (UML), and the performance evaluation uses machine learning algorithms such as random forest, fuzzy logic, and neural networks. The study aims to contribute to the control of Lassa fever, which is achievable through the combined efforts of public health professionals and geocomputational and machine learning tools. The research findings will potentially be more readily accepted and utilized by decision-makers for the attainment of Lassa fever elimination.

Keywords: geo-computational model, lassa fever dynamics, lassa fever, outbreak prediction, nigeria

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Authors: Yogesh Bacchaw, Ashish Dabade

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Nowadays oral health is raising concern in society. Acid producing microorganisms changes the oral pH and creates a favorable environment for microbial growth. This growth not only promotes dental decay but also bad breath. Generally Recognized As Safe (GRAS) listed component was incorporated in chewing gum as an antimicrobial agent. The chewing gum produced exhibited up to 9 hours of antimicrobial activity against oral microflora. The toxicity of GRAS component per RACC value of chewing gum was negligible as compared to actual toxicity level of GRAS component. The antibacterial efficiency of chewing gum was tested by using total plate count (TPC) and colony forming unit (CFU). Nine hours were required to microflora to reach TPC/CFU of before chewing gum consumption. This chewing gum not only provides mouth freshening activity but also helps in lowering dental decay, bad breath, and enamel whitening.

Keywords: colony forming unit (CFU), chewing gum, generally recognized as safe (GRAS), microbial growth, microorganisms, oral health, RACC, total plate count (TPC), antimicrobial agent, enamel whitening, oral pH

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Authors: Luis C. Parra

Abstract:

The significant wave height prediction is an issue of great interest in the field of coastal activities because of the non-linear behavior of the wave height and its complexity of prediction. This study aims to present a machine learning model to forecast the significant wave height of the oceanographic wave measuring buoys anchored at Mooloolaba of the Queensland Government Data. Modeling was performed by a multilayer perceptron neural network-genetic algorithm (GA-MLP), considering Relu(x) as the activation function of the MLPNN. The GA is in charge of optimized the MLPNN hyperparameters (learning rate, hidden layers, neurons, and activation functions) and wrapper feature selection for the window width size. Results are assessed using Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The GAMLPNN algorithm was performed with a population size of thirty individuals for eight generations for the prediction optimization of 5 steps forward, obtaining a performance evaluation of 0.00104 MSE, 0.03222 RMSE, 0.02338 MAE, and 0.71163% of MAPE. The results of the analysis suggest that the MLPNNGA model is effective in predicting significant wave height in a one-step forecast with distant time windows, presenting 0.00014 MSE, 0.01180 RMSE, 0.00912 MAE, and 0.52500% of MAPE with 0.99940 of correlation factor. The GA-MLP algorithm was compared with the ARIMA forecasting model, presenting better performance criteria in all performance criteria, validating the potential of this algorithm.

Keywords: significant wave height, machine learning optimization, multilayer perceptron neural networks, evolutionary algorithms

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Authors: Maryam Zahedifard, Fadhil Lafta Faraj, Nazia Abdul Majid, Hapipah Mohd Ali, Mahmood Ameen Abdulla

Abstract:

The new quinazoline Schiff bases have been synthesized through condensation reaction of 2-aminobenzhydrazide with 5-bromosalicylaldehyde and 3-methoxy-5-bromosalicylaldehyde. The compound was investigated for anticancer activity against MCF-7 human breast cancer cell line. It demonstrated a remarkable antiproliferative effect, with an IC50 value of 3.41±0.34, after 72 hours of treatment. Most apoptosis morphological features in treated MCF-7 cells were observed by AO/PI staining. The results of cell cycle analysis indicate that compounds did not induce S and M phase arrest in cell after 24 hours of treatment. Furthermore, MCF-7 cells treated with compound subjected to apoptosis death, as exhibited by perturbation of mitochondrial membrane potential and cytochrome C release as well as increase in ROS generation. We also found activation of caspases 3/7 and -9. Moreover, acute toxicity results demonstrated the nontoxic nature of the compounds in mice. Our results showed the selected compound significantly induce apoptosis in MCF-7 cells via intrinsic pathway, which might be considered as a potential candidate for further in vivo and clinical breast cancer studies.

Keywords: quinazoline Schiff base, apoptosis, MCF-7, caspase, cell cycle, acute toxicity

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Authors: Mehrzad Mohabbi Yadollahi, Ramazan Demirboğa, Majid Atashafrazeh

Abstract:

Geopolymers are highly complex materials which involve many variables which makes modeling its properties very difficult. There is no systematic approach in mix design for Geopolymers. Since the amounts of silica modulus, Na2O content, w/b ratios and curing time have a great influence on the compressive strength an ANFIS (Adaptive neuro fuzzy inference system) method has been established for predicting compressive strength of ground pumice based Geopolymers and the possibilities of ANFIS for predicting the compressive strength has been studied. Consequently, ANFIS can be used for geopolymer compressive strength prediction with acceptable accuracy.

Keywords: geopolymer, ANFIS, compressive strength, mix design

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Authors: Strahinja Kovačević, Sanja Podunavac-Kuzmanović, Lidija Jevrić, Milica Karadžić

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The present study is based on the quantitative structure-activity relationship (QSAR) analysis of eighteen compounds with anti-prion activity. The structures and anti-prion activities (expressed in response units, RU%) of the analyzed compounds are taken from CHEMBL database. In the first step of analysis 85 molecular descriptors were calculated and based on them the hierarchical cluster analysis (HCA) and principal component analysis (PCA) were carried out in order to detect potential significant similarities or dissimilarities among the studied compounds. The calculated molecular descriptors were physicochemical, lipophilicity and ADMET (absorption, distribution, metabolism, excretion and toxicity) descriptors. The first stage of the QSAR analysis was simple linear regression modeling. It resulted in one acceptable model that correlates Henry's law constant with RU% units. The obtained 2D-QSAR model was validated by cross-validation as an internal validation method. The validation procedure confirmed the model’s quality and therefore it can be used for prediction of anti-prion activity. The next stage of the analysis of anti-prion activity will include 3D-QSAR and molecular docking approaches in order to select the most promising compounds in treatment of prion diseases. These results are the part of the project No. 114-451-268/2016-02 financially supported by the Provincial Secretariat for Science and Technological Development of AP Vojvodina.

Keywords: anti-prion activity, chemometrics, molecular modeling, QSAR

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Authors: A. Brahma

Abstract:

Drying is a phenomenon that accompanies the hardening of hydraulic materials. It can, if it is not prevented, lead to significant spontaneous dimensional variations, which the cracking is one of events. In this context, cracking promotes the transport of aggressive agents in the material, which can affect the durability of concrete structures. Drying shrinkage develops over a long period almost 30 years although most occurred during the first three years. Drying shrinkage stabilizes when the material is water balance with the external environment. The drying shrinkage of cementitious materials is due to the formation of capillary tensions in the pores of the material, which has the consequences of bringing the solid walls of each other. Knowledge of the shrinkage characteristics of concrete is a necessary starting point in the design of structures for crack control. Such knowledge will enable the designer to estimate the probable shrinkage movement in reinforced or prestressed concrete and the appropriate steps can be taken in design to accommodate this movement. This study is concerned the modelling of drying shrinkage of the hydraulic materials and the prediction of the rate of spontaneous deformations of hydraulic materials during hardening. The model developed takes in consideration the main factors affecting drying shrinkage. There was agreement between drying shrinkage predicted by the developed model and experimental results. In last we show that developed model describe the evolution of the drying shrinkage of high performances concretes correctly.

Keywords: drying, hydraulic concretes, shrinkage, modeling, prediction

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Authors: S. Benchelha, H. C. Aoudjehane, M. Hakdaoui, R. El Hamdouni, H. Mansouri, T. Benchelha, M. Layelmam, M. Alaoui

Abstract:

The logistic regression (LR) and multivariate adaptive regression spline (MarSpline) are applied and verified for analysis of landslide susceptibility map in Oudka, Morocco, using geographical information system. From spatial database containing data such as landslide mapping, topography, soil, hydrology and lithology, the eight factors related to landslides such as elevation, slope, aspect, distance to streams, distance to road, distance to faults, lithology map and Normalized Difference Vegetation Index (NDVI) were calculated or extracted. Using these factors, landslide susceptibility indexes were calculated by the two mentioned methods. Before the calculation, this database was divided into two parts, the first for the formation of the model and the second for the validation. The results of the landslide susceptibility analysis were verified using success and prediction rates to evaluate the quality of these probabilistic models. The result of this verification was that the MarSpline model is the best model with a success rate (AUC = 0.963) and a prediction rate (AUC = 0.951) higher than the LR model (success rate AUC = 0.918, rate prediction AUC = 0.901).

Keywords: landslide susceptibility mapping, regression logistic, multivariate adaptive regression spline, Oudka, Taounate

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Authors: Alemu Tadesse Feroche

Abstract:

In this study, some 2, 3 distributed quinazoline -4 (3H) - one derivative were synthesized using a three-step synthetic route. They were obtained in a good yield (59.5-85%) by applying different chemical reactions like cyclization and condensation reactions. The chemical structure of the final compounds was also verified by spectroscopic methods (IR, ¹HNMR) and elemental microanalysis. The in vivo antimalarial activity of these compounds on P. berghei infected mice was found to be moderate to high at an oral dose of 0.04846 mmol/kg /day. This is equal to 25 mg/kg of chloroquine phosphate, which causes 100% inhibition of the parasite. It is worth mentioning that most active compounds (E) -3 Phenyl -2- [2- (pyridine -4- yl) vinyl] -4 (3H) -quinazolinone IVa (64.02%, (E)-2-[2-(4 - Hydroxy-3 - methoxystyryl) - vinyl) -3 - phenyl -4 (3H ) - quinazolinone IVc (77.25%) and (E)-2 –[2 –(Pyridin -4-yl) –vinyl] -3 phenenylamine -4(3H) quinazolinone IVe (73.54%) showed a dose-dependent increase in present suppression in antimalarial activities. Furthermore, the synthesized compounds were screened for their in vitro antileishmanial activity against L. aethiopica isolate (CL/039/09). All tested compounds (IVa (0.03766 ug/ml), IVb (0.00538 ug/ml, IVc (0.00412 ug/ml, IVd (0.00110 ug/ml), IVe (0.03017 ug/ml) and IVf (0.03894 ug/ml)) showed excellent potency that is much better than amphotericin B (IC50 = 0,04359 ug/ml). The results of acute toxicity indicated that all test compounds (IVa –IVf) proved to be nontoxic and well tolerated by the experimental animals up to 300 mg/kg in oral and 140 mg/kg in parental studies.

Keywords: 4(3H)-quinazolinone, in vivo antimalarial activity, in vitro antileishmanial activity, acute toxicity

Procedia PDF Downloads 76