Search results for: root uptake models

Authors: Ahmad Saadiq, Neeraj Sahu

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Sediment, which comprises of solid particles of mineral and organic material are transported by water. In river systems, the amount of sediment transported is controlled by both the transport capacity of the flow and the supply of sediment. The transport of sediment in rivers is important with respect to pollution, channel navigability, reservoir ageing, hydroelectric equipment longevity, fish habitat, river aesthetics and scientific interests. The sediment load transported in a river is a very complex hydrological phenomenon. Hence, sediment transport has attracted the attention of engineers from various aspects, and different methods have been used for its estimation. So, several experimental equations have been submitted by experts. Though the results of these methods have considerable differences with each other and with experimental observations, because the sediment measures have some limits, these equations can be used in estimating sediment load. In this present study, two black box models namely, an SRC (Sediment Rating Curve) and ANN (Artificial Neural Network) are used in the simulation of the suspended sediment load. The study is carried out for Seonath subbasin. Seonath is the biggest tributary of Mahanadi river, and it carries a vast amount of sediment. The data is collected for Jondhra hydrological observation station from India-WRIS (Water Resources Information System) and IMD (Indian Meteorological Department). These data include the discharge, sediment concentration and rainfall for 10 years. In this study, sediment load is estimated from the input parameters (discharge, rainfall, and past sediment) in various combination of simulations. A sediment rating curve used the water discharge to estimate the sediment concentration. This estimated sediment concentration is converted to sediment load. Likewise, for the application of these data in ANN, they are normalised first and then fed in various combinations to yield the sediment load. RMSE (root mean square error) and R² (coefficient of determination) between the observed load and the estimated load are used as evaluating criteria. For an ideal model, RMSE is zero and R² is 1. However, as the models used in this study are black box models, they don’t carry the exact representation of the factors which causes sedimentation. Hence, a model which gives the lowest RMSE and highest R² is the best model in this study. The lowest values of RMSE (based on normalised data) for sediment rating curve, feed forward back propagation, cascade forward back propagation and neural network fitting are 0.043425, 0.00679781, 0.0050089 and 0.0043727 respectively. The corresponding values of R² are 0.8258, 0.9941, 0.9968 and 0.9976. This implies that a neural network fitting model is superior to the other models used in this study. However, a drawback of neural network fitting is that it produces few negative estimates, which is not at all tolerable in the field of estimation of sediment load, and hence this model can’t be crowned as the best model among others, based on this study. A cascade forward back propagation produces results much closer to a neural network model and hence this model is the best model based on the present study.

Keywords: artificial neural network, Root mean squared error, sediment, sediment rating curve

Procedia PDF Downloads 325

Authors: Ayman Aboelkassem, Mohamoud Ali, Rezq Diab

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This project aims to document and preserve boat models which were discovered in the Saqqara by Czech Institute of Egyptology archeological mission at Saqqara (GEM numbers, 46007, 46008, 46009). These boat models dates back to Egyptian Old Kingdom and have been transferred to the Conservation Center of the Grand Egyptian Museum, to be displayed at the new museum.The project objectives making such boat models more visible to visitors through the use of 3D reconstructed models and high resolution photos which describe the history of using the boats during the Ancient Egyptian history. Especially, The Grand Egyptian Museum is going to exhibit the second boat of King Khufu from Old kingdom. The project goals are to document the boat models and arrange an exhibition, where such Models going to be displayed next to the Khufu Second Boat. The project shows the importance of using boats in Ancient Egypt, and connecting their usage through Ancient Egyptian periods till now. The boat models had a unique Symbolized in ancient Egypt and connect the public with their kings. The Egyptian kings allowed high ranked employees to put boat models in their tombs which has a great meaning that they hope to fellow their kings in the journey of the afterlife.

Keywords: archaeology, boat models, 3D digital tools for heritage management, museums

Procedia PDF Downloads 137

Authors: Suparman

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Piecewise linear regression models are very flexible models for modeling the data. If the piecewise linear regression models are matched against the data, then the parameters are generally not known. This paper studies the problem of parameter estimation of piecewise linear regression models. The method used to estimate the parameters of picewise linear regression models is Bayesian method. But the Bayes estimator can not be found analytically. To overcome these problems, the reversible jump MCMC algorithm is proposed. Reversible jump MCMC algorithm generates the Markov chain converges to the limit distribution of the posterior distribution of the parameters of picewise linear regression models. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of picewise linear regression models.

Keywords: regression, piecewise, Bayesian, reversible Jump MCMC

Procedia PDF Downloads 521

Authors: Phakamani Xaba, Robert Huberts, Bilainu Oboirien

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The present study was aimed at developing & comparing two neural-network-based predictive models to predict shelf-life/product age of South African margarine using free fatty acid (FFA), water droplet size (D3.3), water droplet distribution (e-sigma), moisture content, peroxide value (PV), anisidine valve (AnV) and total oxidation (totox) value as input variables to the model. Brick margarine products which had varying ages ranging from fresh i.e. week 0 to week 47 were sourced. The brick margarine products which had been stored at 10 & 25 °C and were characterized. JMP and MATLAB models to predict shelf-life/ margarine age were developed and their performances were compared. The key performance indicators to evaluate the model performances were correlation coefficient (CC), root mean square error (RMSE), and mean absolute percentage error (MAPE) relative to the actual data. The MATLAB-developed model showed a better performance in all three performance indicators. The correlation coefficient of the MATLAB model was 99.86% versus 99.74% for the JMP model, the RMSE was 0.720 compared to 1.005 and the MAPE was 7.4% compared to 8.571%. The MATLAB model was selected to be the most accurate, and then, the number of hidden neurons/ nodes was optimized to develop a single predictive model. The optimized MATLAB with 10 neurons showed a better performance compared to the models with 1 & 5 hidden neurons. The developed models can be used by margarine manufacturers, food research institutions, researchers etc, to predict shelf-life/ margarine product age, optimize addition of antioxidants, extend shelf-life of products and proactively troubleshoot for problems related to changes which have an impact on shelf-life of margarine without conducting expensive trials.

Keywords: margarine shelf-life, predictive modelling, neural networks, oil oxidation

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Authors: Mojtaba Esmaeilzad Limoudehi

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To evaluate the model ORYZA2000, under the management of irrigation and nitrogen fertilization experiment, a split plot with a randomized complete block design with three replications on hybrid cultivars (spring) in the 1388-1387 crop year was conducted at the Rice Research Institute. Permanent flood irrigation as the main plot in the fourth level, around 5 days, from 11 days to 8 days away, and the four levels of nitrogen fertilizer as the subplots 0, 90, 120, and 150 kg N Ha were considered. Simulated and measured values of leaf area index, grain yield, and biological parameters using the regression coefficient, t-test, the root mean square error (RMSE), and normalized root mean square error (RMSEn) were performed. Results, the normalized root mean square error of 10% in grain yield, the biological yield of 9%, and 23% of maximum LAI was determined. The simulation results show that grain yield and biological ORYZA2000 model accuracy are good but do not simulate maximum LAI well. The results show that the model can support ORYZA2000 test results and can be used under conditions of nitrogen fertilizer and irrigation management.

Keywords: evaluation, rice, nitrogen fertilizer, model ORYZA2000

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Authors: Sophia Liang Zhou

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For investors, businesses, consumers, and governments, an accurate assessment of future housing prices is crucial to critical decisions in resource allocation, policy formation, and investment strategies. Previous studies are contradictory about macroeconomic determinants of housing price and largely focused on one or two areas using point prediction. This study aims to develop data-driven models to accurately predict future housing market trends in different markets. This work studied five different metropolitan areas representing different market trends and compared three-time lagging situations: no lag, 6-month lag, and 12-month lag. Linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to model the real estate price using datasets with S&P/Case-Shiller home price index and 12 demographic and macroeconomic features, such as gross domestic product (GDP), resident population, personal income, etc. in five metropolitan areas: Boston, Dallas, New York, Chicago, and San Francisco. The data from March 2005 to December 2018 were collected from the Federal Reserve Bank, FBI, and Freddie Mac. In the original data, some factors are monthly, some quarterly, and some yearly. Thus, two methods to compensate missing values, backfill or interpolation, were compared. The models were evaluated by accuracy, mean absolute error, and root mean square error. The LR and ANN models outperformed the RF model due to RF’s inherent limitations. Both ANN and LR methods generated predictive models with high accuracy ( > 95%). It was found that personal income, GDP, population, and measures of debt consistently appeared as the most important factors. It also showed that technique to compensate missing values in the dataset and implementation of time lag can have a significant influence on the model performance and require further investigation. The best performing models varied for each area, but the backfilled 12-month lag LR models and the interpolated no lag ANN models showed the best stable performance overall, with accuracies > 95% for each city. This study reveals the influence of input variables in different markets. It also provides evidence to support future studies to identify the optimal time lag and data imputing methods for establishing accurate predictive models.

Keywords: linear regression, random forest, artificial neural network, real estate price prediction

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Authors: Marcos Valdez Alexander Junior, Igor Aguilar-Alonso

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Malnutrition is the root of different diseases that universally affect everyone, diseases such as obesity and malnutrition. The objective of this research is to predict the calories of the food to be eaten, developing a smart mobile application to show the user if a meal is balanced. Due to the large percentage of obesity and malnutrition in Peru, the present work is carried out. The development of the intelligent application is proposed with a three-layer architecture, and for the prediction of the nutritional value of the food, the use of pre-trained models based on convolutional neural networks is proposed.

Keywords: volume estimation, calorie estimation, artificial vision, food nutrition

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Authors: Fatih Tarlak

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Predictive microbiology can be considered as an important field in food microbiology in which it uses predictive models to describe the microbial growth in different food products. Predictive models estimate the growth of microorganisms quickly, efficiently, and in a cost-effective way as compared to traditional methods of enumeration, which are long-lasting, expensive, and time-consuming. The mathematical models used in predictive microbiology are mainly categorised as primary and secondary models. The primary models are the mathematical equations that define the growth data as a function of time under a constant environmental condition. The secondary models describe the effects of environmental factors, such as temperature, pH, and water activity (aw) on the parameters of the primary models, including the maximum specific growth rate and lag phase duration, which are the most critical growth kinetic parameters. The combination of primary and secondary models provides valuable information to set limits for the quantitative detection of the microbial spoilage and assess product shelf-life.

Keywords: shelf-life, growth model, predictive microbiology, simulation

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Authors: P. Defossez, J. M. Bonnefond, D. Garrigou, P. Trichet, F. Danjon

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Windstorm damage to European forests has ecological, social and economic consequences of major importance. Most trees during storms are uprooted. While a large amount of work has been done over the last decade on understanding the aerial tree response to turbulent wind flow, much less is known about the root-soil interface, and the impact of soil moisture and root-soil system fatiguing on tree uprooting. Anchorage strength is expected to be reduced by water-logging and heavy rain during storms due to soil strength decrease with soil water content. Our paper is focused on the maritime pine cultivated on sandy soil, as a representative species of the Forêt des Landes, the largest cultivated forest in Europe. This study aims at providing knowledge on the effects of soil saturation on root anchorage. Pulling experiments on trees were performed to characterize the resistance to wind by measuring the critical bending moment (Mc). Pulling tests were performed on 12 maritime pines of 13-years old for two unsaturated soil conditions that represent the soil conditions expected in winter when wind storms occur in France (w=11.46 to 23.34 % gg⁻¹). A magnetic field digitizing technique was used to characterize the three-dimensional architecture of root systems. The soil mechanical properties as function of soil water content were characterized by laboratory mechanical measurements as function of soil water content and soil porosity on remolded samples using direct shear tests at low confining pressure ( < 15 kPa). Remarkably Mc did not depend on w but mainly on the root system morphology. We suggested that the importance of soil water conditions on tree anchorage depends on the tree size. This study gives a new insight on young tree anchorage: roots may sustain by themselves anchorage, whereas adhesion between roots and surrounding soil may be negligible in sandy soil.

Keywords: roots, sandy soil, shear strength, tree anchorage, unsaturated soil

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Authors: V. Khosravi, F. D. Ardejani, A. Aryafar, M. Sedighi

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Heavy metals have a damaging impact for the environment, animals and humans due to their extreme toxicity and removing them from wastewaters is a very important and interesting task in the field of water pollution control. Biosorption is a relatively new method for treatment of wastewaters and recovery of heavy metals. In this study, a continuous fixed bed study was carried out by using Bacillus thuringiensis as a biosorbent for the removal of Cu and Mn ions from Sarcheshmeh Acid Mine Drainage (AMD). The effect of operating parameters such as flow rate and bed height on the sorption characteristics of B. thuringiensis was investigated at pH 6.0 for each metal ion. The experimental results showed that the breakthrough time decreased with increasing flow rate and decreasing bed height. The data also indicated that the equilibrium uptake of both metals increased with decreasing flow rate and increasing bed height. BDST, Thomas, and Yoon–Nelson models were applied to experimental data to predict the breakthrough curves. All models were found suitable for describing the whole dynamic behavior of the column with respect to flow rate and bed height. In order to regenerate the adsorbent, an elution step was carried out with 1 M HCl and five adsorption-desorption cycles were carried out in continuous manner.

Keywords: acid mine drainage, bacillus thuringiensis, biosorption, cu and mn ions, fixed bed

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Authors: Retius Chifurira

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Logistic regression model is the most used regression model to predict meteorological drought probabilities. When the dependent variable is extreme, the logistic model fails to adequately capture drought probabilities. In order to adequately predict drought probabilities, we use the generalized linear model (GLM) with the quantile function of the generalized extreme value distribution (GEVD) as the link function. The method maximum likelihood estimation is used to estimate the parameters of the generalized extreme value (GEV) regression model. We compare the performance of the logistic and the GEV regression models in predicting drought probabilities for Zimbabwe. The performance of the regression models are assessed using the goodness-of-fit tests, namely; relative root mean square error (RRMSE) and relative mean absolute error (RMAE). Results show that the GEV regression model performs better than the logistic model, thereby providing a good alternative candidate for predicting drought probabilities. This paper provides the first application of GLM derived from extreme value theory to predict drought probabilities for a drought-prone country such as Zimbabwe.

Keywords: generalized extreme value distribution, general linear model, mean annual rainfall, meteorological drought probabilities

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Authors: Pukhrambam Helena Chanu, Janardan Yadav

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This study investigates the potential of various fungal isolates to solubilize zinc and counteract rice pathogens, with the aim of mitigating zinc deficiency and disease prevalence in rice farming. Soil samples from the rhizosphere were collected, and zinc-solubilizing fungi were isolated and purified. Molecular analysis identified Talaromyces sp, Talaromyces versatilis, Talaromyces pinophilus, and Aspergillus terreus as effective zinc solubilizers. Through qualitative and quantitative assessments, it was observed that solubilization efficiencies varied among the isolates over time, with Talaromyces versatilis displaying the highest capacity for solubilization. This variability in solubilization rates may be attributed to differences in fungal metabolic activity and their ability to produce organic acids that facilitate zinc release from insoluble sources in the soil. In inhibition assays against rice pathogens, the fungal isolates exhibited antagonistic properties, with Talaromyces versatilis demonstrating the most significant inhibition rates. This antagonistic activity may be linked to the production of secondary metabolites, such as antibiotics or lytic enzymes by fungi, which inhibit the growth of rice pathogens. The ability of Talaromyces versatilis to outperform other isolates in both zinc solubilization and pathogen inhibition highlights its potential as a multifunctional biocontrol agent in rice cultivation systems. These findings emphasize the potential of fungi as natural solutions for enhancing zinc uptake and managing diseases in rice cultivation. Utilizing indigenous zinc-solubilizing fungi offers a sustainable and environmentally friendly approach to addressing zinc deficiency in soils, reducing the need for chemical fertilizers. Moreover, harnessing the antagonistic activity of these fungi can contribute to integrated disease management strategies, minimizing reliance on synthetic pesticides and promoting ecological balance in agroecosystems. Additionally, the study included the evaluation of dipping time under different concentrations, viz.,10 ppm, 20 ppm, and 30 ppm of biosynthesized nano ZnO on rice seedlings. This investigation aimed to optimize the application of nano ZnO for efficient zinc uptake by rice plants while minimizing potential risks associated with excessive nanoparticle exposure. Evaluating the effects of varying concentrations and dipping durations provides valuable insights into the safe and effective utilization of nano ZnO as a micronutrient supplement in rice farming practices.

Keywords: biosynthesized nano ZnO, rice, root dipping, zinc solubilizing fungi.

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Authors: Abdikarim Yusuf

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This study uses data from Somalia to analyse child labour using a descriptive and qualitative method. The study set out to identify root causes of child labour in Hargeisa and its implications for children. The study shows that poverty, droughts, family separation, and loss of properties are primary drivers of child labour in Hargeisa. The study found that children work in very difficult jobs such as car wash, casual work, and shoe shining for boys while girls work as housemaids, selling tea, Khat and sometimes are at risk of exploitation such as sexual abuse, rape and harassment. The majority of the parents responded that they don’t know any policy, act or law that protects children. Men showed greater awareness than the women respondents in recognizing child labour as a child rights violation.

Keywords: abuse, child, violence, protection

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Authors: Alfred L. Guiffrida

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A review of the literature on supply chain delivery models which use delivery windows to measure delivery performance is presented. The review herein serves to meet the following objectives: (i) provide a synthesis of previously published literature on supply chain delivery performance models, (ii) provide in one paper a consolidation of research that can serve as a single source to keep researchers up to date with the research developments in supply chain delivery models, and (iii) identify gaps in the modeling of supply chain delivery performance which could stimulate new research agendas.

Keywords: delivery performance, delivery window, supply chain delivery models, supply chain performance

Procedia PDF Downloads 421

Authors: Jamshid Qodirov, Sirojiddin Komolov, Ravilov Mirahmad, Olimjon Mirzayev

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Nowadays, natural language processing tools play a crucial role in our daily lives, including various techniques with text processing. There are very advanced models in modern languages, such as English, Russian etc. But, in some languages, such as Uzbek, the NLP models have been developed recently. Thus, there are only a few NLP models in Uzbek language. Moreover, there is no such work that could show which Uzbek NLP model behaves in different situations and when to use them. This work tries to close this gap and compares the Uzbek NLP models existing as of the time this article was written. The authors try to compare the NLP models in two different scenarios: sentiment analysis and sentence similarity, which are the implementations of the two most common problems in the industry: classification and similarity. Another outcome from this work is two datasets for classification and sentence similarity in Uzbek language that we generated ourselves and can be useful in both industry and academia as well.

Keywords: NLP, benchmak, bert, vectorization

Procedia PDF Downloads 54

Authors: Zixuan Zhao, Lingbin Du, Le Wang, Youqing Wang, Yi Yang, Jingjun Chen, Hengjin Dong

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Objectives: Few results from public attitudes for lung cancer screening are available both in China and abroad. This study aimed to identify preferred lung cancer screening modalities in a Chinese population and predict uptake rates of different modalities. Materials and Methods: A discrete choice experiment questionnaire was administered to 392 Chinese individuals aged 50–74 years who were at high risk for lung cancer. Each choice set had two lung screening options and an option to opt-out, and respondents were asked to choose the most preferred one. Both mixed logit analysis and stepwise logistic analysis were conducted to explore whether preferences were related to respondent characteristics and identify which kinds of respondents were more likely to opt out of any screening. Results: On mixed logit analysis, attributes that were predictive of choice at 1% level of statistical significance included the screening interval, screening venue, and out-of-pocket costs. The preferred screening modality seemed to be screening by low-dose computed tomography (LDCT) + blood test once a year in a general hospital at a cost of RMB 50; this could increase the uptake rate by 0.40 compared to the baseline setting. On stepwise logistic regression, those with no endowment insurance were more likely to opt out; those who were older and housewives/househusbands, and those with a health check habit and with commercial endowment insurance were less likely to opt out from a screening programme. Conclusions: There was considerable variance between real risk and self-perceived risk of lung cancer among respondents, and further research is required in this area. Lung cancer screening uptake can be increased by offering various screening modalities, so as to help policymakers further design the screening modality.

Keywords: lung cancer, screening, China., discrete choice experiment

Procedia PDF Downloads 259

Authors: Yajun Qiao, Yaner Yan, Ning Li, Shuqing An

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In order to relieve the pressure on a land resource from a huge population, reclamation has occurred in many coastal wetlands. Plants can maintain their elemental composition within normal limits despite the variations of external conditions. Reclamation may affect carbon (C), nitrogen (N) and phosphorus (P) stoichiometry in the plant to some extent by altering physical and chemical properties of soil in a coastal wetland. We reported the seasonal dynamic of C, N and P stoichiometry in root, stem and leaf of Suaeda salsa (L.) Pall. and in soil between reclamation plots and natural plots. Our results of three-way ANOVA indicated that sampling season always had significant effect on C, N, P concentrations and their ratios; organ had no significant effect on N, P concentration and N:P; plot type had no significant effect on N concentration and C:N. Sampling season explained the most variability of tissue N and P contents, C:N, C:P and N:P, while it’s organ for C using the restricted maximum likelihood (REML) method. By independent sample T-test, we found that reclamation affect more on C, N and P stoichiometry of stem than that of root or leaf on the whole. While there was no difference between reclamation plots and natural plots for soil in four seasons. For three organs, C concentration had peak values in autumn and minimum values in spring while N concentration had peak values in spring and minimum values in autumn. For P concentration, three organs all had peak values in spring; however, the root had minimum value in winter, the stem had that in autumn, and leaf had that in summer. The seasonal dynamic of C, N and P stoichiometry in a leaf of Suaeda salsa were much steadier than that in root or stem under the drive of reclamation.

Keywords: nitrogen, phosphorus, reclamation, seasonal dynamic, Suaeda salsa

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Authors: Albana Hasimi, Edlira Tako, Elvin Çomo, Partizan Malkaj, Blerina Papajani, Ledjan Malaj, Mirela Ndrita

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Many endeavors have been exerted during the last years for developing new artificial polymeric membranes which fulfill the demanded conditions for biomedical uses. One of the most tested polymers is Poly(vinyl alcohol) [PVA]. Ours groups, is based on the possibility of using PVA for personal protective equipment against covid. In them, we explore the possibility of modifying the properties of the polymer by adding Montmorillonite [MMT]. Heat-treatment above the glass transition temperature are used to improve mechanical properties mainly by increasing the crystallinity of the polymer, which acts as a physical network. Temperature-Modulated Differential Scanning Calorimetry (TMDSC) measurements indicated that the presence of 0.5% MMT in PVA causes a higher Tg value and shaped peak of crystallinity. Decomposition is observed at two of the melting points of the crystals during heating 25-240oC and overlap of the recrystallization ridges during cooling 240-25oC. This is indicative of the presence of two types (quality or structure ) of polymer crystals. On the other hand, some indication of improvement of the quality of the crystals by heat-treatment is given by the distinct non-reversing contribution to melting. Data on sorption and transport of water in polyvinyl alcohol films: PVA pure and PVA/MMT matrix, modified by thermal treatment, are presented. The thermal treatment has aftereffect the films become more rigid, and because of this, the water uptake is significantly lower in membranes. That is indicates by analysis of the resulting water uptake kinetics. The presence 0.5% w/w of MMT has no significant impact on the properties of PVA membranes. Water uptake kinetics deviates from Fick’s law due to slow relaxation of glassy polymer matrix for all membranes category.

Keywords: crystallinity, montmorillonite, nanocomposite, poly (vinyl alcohol)

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Authors: Shafaqat Ali, Rehan Ahmad, Muhammad Rizwan

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Chromium (Cr) is one of the most toxic pollutants among heavy metals that adversely affect living organisms and physiological processes in plants. The present study investigated the effect of without and with 15 mg L-1 5-Aminolevulinic acid (ALA) on morpho-physiological attributes of cauliflower (Brassica oleracea botrytis L.) under different Cr concentrations (0, 10, 100 and 200 μM) in the growth medium. Results showed that Cr stress decreased the plant growth, biomass, photosynthetic pigments, and gas exchange characteristics. Chromium stress enhanced the activities of enzymatic antioxidants, catalase (CAT), superoxide dismutase (SOD), and guaiacol peroxidase (POD), and caused oxidative stress, as observed by increased level of malondialdehyde (MDA), hydrogen peroxide (H2O2), electrolyte leakage (EL), in both leaves and roots of cauliflower. Chromium concentrations and total Cr uptake increased in roots, stem and leaves of plants with increasing Cr levels in the growth medium. Foliar application of ALA increased plant growth, biomass, photosynthetic pigments and gas exchange characteristics under Cr stress as compared to without ALA application. As compared to Cr stress alone, ALA application decreased the levels of MDA, H2O2 and EL while further enhanced the activities of antioxidant enzymes in both leaves and roots. Chromium concentrations and total Cr uptake decreased by the ALA application as compared to without ALA. These results showed that foliar application of ALA might be effective in reducing Cr uptake and toxicity in cauliflower.

Keywords: antioxidant enzymes, cauliflower, photosynthesis, chromium, ALA, hydrogen peroxide, electrolyte leakage

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Authors: Marian Sorin Ionescu, Olivia Negoita, Cosmin Dobrin

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This scientific communication reports and discusses learning models adaptable to modern business problems and models specific to digital concepts and paradigms. In the PAC (probably approximately correct) learning model approach, in which the learning process begins by receiving a batch of learning examples, the set of learning processes is used to acquire a hypothesis, and when the learning process is fully used, this hypothesis is used in the prediction of new operational examples. For complex business models, a lot of models should be introduced and evaluated to estimate the induced results so that the totality of the results are used to develop a predictive rule, which anticipates the choice of new models. In opposition, for online learning-type processes, there is no separation between the learning (training) and predictive phase. Every time a business model is approached, a test example is considered from the beginning until the prediction of the appearance of a model considered correct from the point of view of the business decision. After choosing choice a part of the business model, the label with the logical value "true" is known. Some of the business models are used as examples of learning (training), which helps to improve the prediction mechanisms for future business models.

Keywords: machine learning, business models, convex analysis, online learning

Procedia PDF Downloads 140

Authors: Ines Cherif, Ghassen Kharroubi, Leila Bouabid, Adel Gharbi, Aicha Boukthir, Margaret Mccarron, Nissaf Ben Alaya, Afif Ben Salah, Jihene Bettaieb

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Healthcare workers' flu vaccination prevents influenza disease among both patients and caregivers. We aimed in this study to assess influenza vaccine (IV) coverage in 2018-2019 among Tunisian physicians and to determine factors associated with IV receipt. A cross sectional study was carried out in Tunisian primary and secondary health care facilities in the 2018-2019 influenza season. Physicians with direct patient contact were recruited according to a self-weighted multistage sampling. Data were collected through a face to face questionnaire containing questions on knowledge, attitudes, and practices regarding IV. Bivariate analysis was used in order to determine factors associated with IV receipt. A total of 167 physicians were included in the study with a mean age of 48.2 ± 7.7 years and a sex-ratio (M: F) of 0.37. Among participants, 15.1% (95% CI: [9.7%-20.3%]) were vaccinated against influenza in the 2018-2019 influenza season. Bivariate analysis revealed that previous flu immunization in the four years preceding the 2018-2019 influenza season (OR=32.3; p < 10-3), belief that vaccinating healthcare workers may reduce work absenteeism (OR=4.7, p=0.028), belief that flu vaccine should be mandatory to healthcare workers (OR=3.3, p=0.01) and high confidence towards IV efficacy in preventing influenza among caregivers (OR= 4.5, p=0.01) were associated with a higher IV receipt in 2018-2019 among physicians. Less than one fifth of Tunisian physicians were vaccinated against influenza in 2018-2019. Higher vaccine uptake was related to a higher belief in vaccine efficacy in preventing influenza disease among both patients and caregivers. This underscores the need for periodic educational campaigns to raise physicians' awareness about IV efficacy. The switch to an IV mandatory policy should also be considered.

Keywords: influenza vaccine, physicians, Tunisia, vaccination uptake

Procedia PDF Downloads 136

Authors: Ingrid R. Avanzi, Marcela dos P. G. Baltazar, Louise H. Gracioso, Luciana J. Gimenes, Bruno Karolski, Elen A. Perpetuo, Claudio Auguto Oller do Nascimento

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In this study was developed a natural process using a biological system for the uptake of Copper and possible removal of copper from wastewater by dead biomass of the strain Burkholderia cenocepacia. Dead and live biomass of Burkholderia cenocepacia was used to analyze the equilibrium and kinetics of copper biosorption by this strain in function of the pH. Living biomass exhibited the highest biosorption capacity of copper, 50 mg g−1, which was achieved within 5 hours of contact, at pH 7.0, temperature of 30°C, and agitation speed of 150 rpm. The dead biomass of Burkholderia cenocepacia may be considered an efficiently bioprocess, being fast and low-cost to production of copper and also a probably nano-adsorbent of this metal ion in wastewater in bioremediation process. In this study was developed a natural process using a biological system for the uptake of Copper and possible removal of copper from wastewater by dead biomass of the strain Burkholderia cenocepacia. Dead and live biomass of Burkholderia cenocepacia was used to analyze the equilibrium and kinetics of copper biosorption by this strain in function of the pH. Living biomass exhibited the highest biosorption capacity of copper, 50 mg g−1, which was achieved within 5 hours of contact, at pH 7.0, temperature of 30°C, and agitation speed of 150 rpm. The dead biomass of Burkholderia cenocepacia may be considered an efficiently bioprocess, being fast and low-cost to production of copper and also a probably nano-adsorbent of this metal ion in wastewater in bioremediation process.

Keywords: biosorption, dead biomass, biotechnology, copper recovery

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Authors: Chibuzo Okoye Daniels, Gillian Glegg, Lynda Rodwell

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The continuously growing quantity of very slow degrading litter deliberately discarded into the coastal waters around Lagos as marine debris is obvious. What is not known is how to tackle this problem to reduce its prevalence and impact on the environment, economy and community. To identify ways of tackling the marine debris problem two case study areas (Ikoyi and Victoria Islands of Lagos State) were used to assess the root causes, the threat posed by marine debris in the coastal waters around Lagos and the efficacy of current instruments, programmes and initiatives that address marine debris in the study areas. The following methods were used: (1) Self-completed questionnaires for households and businesses within the study areas; (2) Semi-structured interviews with key stakeholders; (3) Observational studies of waste management from collection to disposal and waste management facilities for waste originating from land and maritime sources; (4) Beach surveys and marine debris surveys on shorelines and ports; and (5) Fishing for marine debris. Results of this study identified the following root causes: (1) Indiscriminate human activities and behaviors, and lack of awareness on the part of the main stakeholders and the public of the potential consequences of their actions; (2) Poor solid waste management practices; (3) Lack of strict legal frameworks addressing waste and marine debris problem; and (4) Disposal of non-degradable wastes into domestic sewer system and open streets drains. To effectively tackle marine debris problem in the study areas, adequate, appropriate and cost effective solutions to the above mentioned root causes needs to be identified and effectively transferred for implementation in the study areas.

Keywords: marine debris problem, Lagos state, litter, coastal waters

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Authors: Yohannes Kelifa, Gomathi Periasamy, Aman Karim

Abstract:

Introduction: Diabetes mellitus has become a major public health and economical problem across the globe. Modern antidiabetic drugs have a number of limitations, and scientific investigation of traditional herbal remedies used for diabetes may provide novel leads for the development of new antidiabetic drugs that can be used as alternative or complementary to available antidiabetic allopathic medications. Though Myrica salicifolia Hochst. ex A. Rich. is used for the management of diabetes in Ethiopian traditional medicine, there was no previous scientific evidence about its antidiabetic effect to the authors’ knowledge. This study was undertaken to evaluate the antidiabetic activity the root extracts of Myrica salicifolia in streptozotocin (STZ)-induced diabetic mice. Methods: Experimental diabetes was induced by intraperitoneal administration of STZ (150 mg/kg) in male mice. Diabetic mice were treated with oral doses of M. salicifolia root extracts at 200, 400 and 600 mg/kg, and its fractions (chloroform, ethyl acetate, n-butanol and aqueous) at a dose of 400 mg/kg daily for 15 days. Fasting blood glucose level (BGL) was measured at 0, 5th,10th, and 15th day. The free radical scavenging activity of the crude extract was determined using in vitro by DPPH assay. The statistical significance was assessed by one-way ANOVA, followed by Tukey’s multiple comparison tests. Results were considered significant when p < 0.05. Results: Daily administration of the M. salicifolia 80% methanol root extracts (at three different doses (200, 400 and 600 mg/kg) significantly (p < 0.05, p < 0.01 and p < 0.001) reduced fasting BGL compared with diabetic control. The aqueous and butanol fractions at a dose of 400 mg/kg resulted in maximum reduction of fasting BGL by 42.39%, and 52.13%, respectively at the 15th day in STZ-induced diabetic mice. Free radical scavenging activity of the 80% methanol extract of M. salicifolia was comparable to ascorbic acid. The IC50 values of the crude extract and ascorbic acid (a reference compound) were found to be 4.54 μg/ml and 4.39 μg/ml, respectively. Conclusion: These findings demonstrated that the methanolic extracts of M. salicifolia root and its fractions (n-butanol and aqueous) exhibit a significant antihyperglycemic activity in STZ-induced diabetic mice. Furthermore, the result of the present study indicates that M. salicifolia root extract is a potential source of natural antioxidants.

Keywords: antidiabetic, diabetes mellitus, DPPH, mice, Myrica salicifolia, streptozotocin

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Authors: Pawan P. Kalbende, Anil B. Zade

Abstract:

A novel terpolymer resin has been synthesized by condensation polymerization reaction of 4,4’-biphenol and 4,4’-oxydianiline with formaldehyde in presence of 2M hydrochloric acid as catalyst. Composition of resin was determined on the basis of their elemental analysis and further characterized by UV-Visible, infra-red and nuclear magnetic resonance spectroscopy to confine the most probable structure of synthesized terpolymer. Newly synthesized terpolymer was proved to be a selective chelating ion-exchanger for certain metal ions and were studied for Fe3+, Cu2+, Ni2+, Co2+, Zn2+, Cd2+, Hg2+ and Pb2+ ions using their metal nitrate solutions. A batch equilibrium method was employed to study the selectivity of metal ions uptake involving the measurements of the distribution of a given metal ion between the terpolymer sample and a solution containing the metal ion. The study was carried out over a wide pH range, shaking time and in media of different electrolytes at different ionic strengths. Distribution ratios of metal ions were found to be increased by rising pH of the solutions. Hence, it can be used to recover certain metal ions from waste water for the purpose of purification of water and removal of iron from boiler water.

Keywords: terpolymers, ion-exchangers, distribution ratio, metal ion uptake

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Authors: Raid Hommady, Waseem Al-Talalwah, Shorok Al Dorazi, Roger Soames

Abstract:

The iliolumbar artery is a regular branch of posterior division of the internal iliac artery. The present study investigate 82 specimens to identify the origin of iliolumbar artery. The present study targets the sciatic nerve root supply from iliolumbar artery based on its origin and course. In present study, the ililumbar artery arose from the posterior division of internal iliac artery in 52.2%. In few cases, it arose from dorsomedial aspect of the internal iliac artery in 28.8%. In few cases, the iliolumbar artery arose from the dorsal aspects of the internal iliac artery as well as from the common and external iliac artery 1.7%. Also, the iliolumbar artery arose from the sciatic artery as well as from superior and inferior gluteal arteries in 5.1%. Conversely, it found to be congenital absent in 8.5%. Therefore, the posterior trunk of the internal iliac artery is the most common origin of the iliolumbar artery. With the origin variability of the iliolumbar artery, there is a vascular supply variability of the lumbosacral trunk and sacral root of sciatic nerve. The iliolumbar artery provides vascular supply for lumbosacral trunk 57.3% in whereas the sacral root in 5.1%. As a result, surgeons should pay attention to these variations to decrease iatrogenic fault.

Keywords: iliolumbar, sciatic artery, internal iliac, external iliac, posterior division

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Authors: Kaewkarn Phuangsombat, Arthit Phuangsombat, Anupun Terdwongworakul

Abstract:

Hard seeds will not grow and can cause mold in sprouting process. Thus, the hard seeds need to be separated from the normal seeds. Near infrared hyperspectral imaging in a range of 900 to 1700 nm was implemented to develop a model by partial least squares discriminant analysis to discriminate the hard seeds from the normal seeds. The orientation of the seeds was also studied to compare the performance of the models. The model based on hilum-up orientation achieved the best result giving the coefficient of determination of 0.98, and root mean square error of prediction of 0.07 with classification accuracy was equal to 100%.

Keywords: mung bean, near infrared, germinatability, hard seed

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Authors: Jayanta Kumar Ghosh, Swastik Sunil Goriwale, Himangshu Sarkar

Abstract:

Groundwater is one of the most valuable natural resources that society consumes for its domestic, industrial, and agricultural water supply. Its bulk and indiscriminate consumption affects the groundwater resource. Often, it has been found that the groundwater recharge rate is much lower than its demand. Thus, to maintain water and food security, it is necessary to monitor and management of groundwater storage. However, it is challenging to estimate groundwater storage (GWS) by making use of existing hydrological models. To overcome the difficulties, machine learning (ML) models are being introduced for the evaluation of groundwater level (GWL). Thus, the objective of this research work is to develop an ML-based model for the prediction of GWL. This objective has been realized through the development of an artificial neural network (ANN) model based on hydro-gravimetry. The model has been developed using training samples from field observations spread over 8 months. The developed model has been tested for the prediction of GWL in an observation well. The root means square error (RMSE) for the test samples has been found to be 0.390 meters. Thus, it can be concluded that the hydro-gravimetric-based ANN model can be used for the prediction of GWL. However, to improve the accuracy, more hydro-gravimetric parameter/s may be considered and tested in future.

Keywords: machine learning, hydro-gravimetry, ground water level, predictive model

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Authors: Josip Arneric, Blanka Skrabic Peric

Abstract:

Forecasting of volatility, i.e. returns fluctuations, has been a topic of interest to portfolio managers, option traders and market makers in order to get higher profits or less risky positions. Based on the fact that volatility is time varying in high frequency data and that periods of high volatility tend to cluster, the most common used models are GARCH type models. As standard GARCH models show high volatility persistence, i.e. integrated behaviour of the conditional variance, it is difficult the predict volatility using standard GARCH models. Due to practical limitations of these models different approaches have been proposed in the literature, based on Markov switching models. In such situations models in which the parameters are allowed to change over time are more appropriate because they allow some part of the model to depend on the state of the economy. The empirical analysis demonstrates that Markov switching GARCH model resolves the problem of excessive persistence and outperforms uni-regime GARCH models in forecasting volatility for selected emerging markets.

Keywords: emerging markets, Markov switching, GARCH model, transition probabilities

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Authors: Nisha Thapa, Ram Prasad Mainali, Prakriti Chand

Abstract:

Broadleaf mustard is a commercialized leafy vegetable of Nepal. However, its utilization is hindered in terms of production and productivity due to the high intensity of insects, pests, and diseases causing great loss. The plant protection part of the crop’s disease and damage intensity has not been studied much from research perspectives in Nepal. The research aimed to evaluate broadleaf mustard genotypes for resistance against different diseases. A total of 35 native genotypes of broadleaf mustard were screened at weekly intervals by scoring the plants for ten weeks. Five different diseases, such as Rhizoctonia root rot, Alternaria blight, black rot, turnip mosaic virus disease, and white rust, were reported from the broad leaf mustard genotypes. Out of 35 genotypes, 23 genotypes were found with very high Rhizoctonia Root Rot severity, whereas 8 genotypes showed very high Alternaria blight severity. Likewise, 3 genotypes were found with high Black rot severity, and 1 genotype was found with very high Turnip mosaic virus disease incidence. Similarly, 2 genotypes were found to have very high White rust severity. Among the disease of national importance, Rhizoctonia root rot was found to be the most severe disease with the greatest loss. Broadleaf mustard genotypes like Rato Rayo, CO 1002, and CO 11007 showed average to the high level of field resistance; therefore, these genotypes should be used, conserved, and stored in a mustard improvement program as the disease resistance quality or susceptibility of these genotypes can be helpful for seed producing farmers, companies and other stakeholders through varietal improvement and developmental works that further aids in sustainable disease management of the vegetable.

Keywords: genotype, disease resistance, Rhizoctonia root rot severity, varietal improvement

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