Search results for: artificial neural networks; crop water stress index; canopy temperature

Authors: Chouahda Salima, Soltani Noureddine

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The present study is a part of biological control against mosquitoes. It aims to assess the impact of two xenobiotics (a selective insect growth regulator: halofenozide and heavy metals: cadmium, more toxic and widespread in the region) in mosquitofish: Gambusia affinis. Several approaches were examined: Acute toxicity of cadmium and halofenozide: The acute toxicity of cadmium and halofenozide was examined in juvenile and adult males and females of G. affinis at different concentrations, cadmium causes mortality of the species studied with a relation dose-response. In laboratory conditions, the impact of cadmium was determined on two biomarkers of environmental stress: glutathione and acetylcholinesterase. The results show that the juvenile followed by adult males are more susceptible than adult females, while the halofenozide does not have any effect on the mortality of juvenile and adult males and females of G.affinis. Chronic toxicity of cadmium and halofenozide: both xenobiotics were added to the water fish raising at different doses tested in juveniles and adults males and females during two months of experience. Growth and metric indices; results show that halofenozide added to the water juveniles of G. affinis has no effect on their growth (length and weight). On the other side, the cadmium at the dose 5 µg/L shows a higher toxicity against juvenile, where he appears to reduce significantly their linear growth and weight. In females, the both xenobiotics have significant effects on metric indices, but these effects are more important on the hepatosomatic index that the gonadosomatic index and the coefficient of condition. Biomarkers; acetylcholinesterase (AChE), glutathione S-transferase (GST) and glutathione (GSH) used in assessing of environmental stress were measured in juveniles and adults males and females. The response of these biomarkers reveals an inhibition of AChE specific activity, an induction of GST activity, and decrease of GSH rates in juveniles in the end of experiment and during chronic treatment adult males and females. The effect of these biomarkers is more pronounced in females compared to males and juveniles. These different biomarkers have a similar profile for the duration of exposure.

Keywords: gambusia affinis, insecticide, heavy metal, morphology, biomarkers, chronic toxicity, acute toxicity, pollution

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Authors: Victor Bodell, Lukas Ekstrom, Somayeh Aghanavesi

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Fuel consumption (FC) is one of the key factors in determining expenses of operating a heavy-duty vehicle. A customer may therefore request an estimate of the FC of a desired vehicle. The modular design of heavy-duty vehicles allows their construction by specifying the building blocks, such as gear box, engine and chassis type. If the combination of building blocks is unprecedented, it is unfeasible to measure the FC, since this would first r equire the construction of the vehicle. This paper proposes a machine learning approach to predict FC. This study uses around 40,000 vehicles specific and o perational e nvironmental c onditions i nformation, such as road slopes and driver profiles. A ll v ehicles h ave d iesel engines and a mileage of more than 20,000 km. The data is used to investigate the accuracy of machine learning algorithms Linear regression (LR), K-nearest neighbor (KNN) and Artificial n eural n etworks (ANN) in predicting fuel consumption for heavy-duty vehicles. Performance of the algorithms is evaluated by reporting the prediction error on both simulated data and operational measurements. The performance of the algorithms is compared using nested cross-validation and statistical hypothesis testing. The statistical evaluation procedure finds that ANNs have the lowest prediction error compared to LR and KNN in estimating fuel consumption on both simulated and operational data. The models have a mean relative prediction error of 0.3% on simulated data, and 4.2% on operational data.

Keywords: artificial neural networks, fuel consumption, friedman test, machine learning, statistical hypothesis testing

Procedia PDF Downloads 178

Authors: Angelica Gonzalez O, Leonardo Rivera Cadavid, Diego Fernando Manotas

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Energy poverty considers a population that does not have access to modern energy service. In particular, an area of a country that is not connected to the national electricity grid is known as a Non-Interconnected Zone (NIZ). Access to electricity has a significant impact on the welfare and development opportunities of the population. Different studies have shown that most health problems have an empirical cause and effect relationship with multidimensional energy poverty. Likewise, research has been carried out to review the consequences of not having access to electricity, and its results have concluded a statistically significant relationship between energy poverty and sources of drinking water, access to clean water, risks of mosquito bites, obesity, sterilization, marital status, occupation, and residence. Therefore, extensive research has been conducted in the construction of an energy poverty measure based on an index. Some of these studies introduce a Multidimensional Energy Poverty Index (MEPI), Compose Energy Poverty Index (CEPI), Low Income High Costs indicator (LIHC), among others. For this purpose, this study analyzes the energy poverty index using a multicriteria analysis determining the set of feasible alternatives - for which Colombia's ZNI will be used as a case study - to be considered in the problem and the set of relevant criteria in the characterization of the ZNI, from which the prioritization is obtained to determine the level of adjustment of each alternative with respect to the performance in each criterion. Additionally, this study considers the installation of Micro-Grids (MG). This is considered a straightforward solution to this problem because an MG is a local electrical grid, able to operate in grid-connected and island mode. Drawing on those insights, this study compares an energy poverty index considering an MG installation and calculates the impacts of different criterias in an energy poverty index in NIZ.

Keywords: multicirteria, energy poverty, rural, microgrids, non-interconnect zones

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Authors: Ahmet Kayabasi, Ali Akdagli

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In this study, three robust predicting methods, namely artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for computing the resonant frequency of A-shaped compact microstrip antennas (ACMAs) operating at UHF band. Firstly, the resonant frequencies of 144 ACMAs with various dimensions and electrical parameters were simulated with the help of IE3D™ based on method of moment (MoM). The ANN, ANFIS and SVM models for computing the resonant frequency were then built by considering the simulation data. 124 simulated ACMAs were utilized for training and the remaining 20 ACMAs were used for testing the ANN, ANFIS and SVM models. The performance of the ANN, ANFIS and SVM models are compared in the training and test process. The average percentage errors (APE) regarding the computed resonant frequencies for training of the ANN, ANFIS and SVM were obtained as 0.457%, 0.399% and 0.600%, respectively. The constructed models were then tested and APE values as 0.601% for ANN, 0.744% for ANFIS and 0.623% for SVM were achieved. The results obtained here show that ANN, ANFIS and SVM methods can be successfully applied to compute the resonant frequency of ACMAs, since they are useful and versatile methods that yield accurate results.

Keywords: a-shaped compact microstrip antenna, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM)

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Authors: Manfred Szerencsits, Christine Weinberger, Maximilian Kuderna, Franz Feichtinger, Eva Erhart, Stephan Maier

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Cover or catch crops have beneficial effects for soil, water, erosion, etc. If harvested, they also provide feedstock for biogas without competition for arable land in regions, where only one main crop can be produced per year. On average gross energy yields of approx. 1300 m³ methane (CH₄) ha^-1 can be expected from 4.5 tonnes (t) of cover crop dry matter (DM) in Austria. Considering the total energy invested from cultivation to compression for biofuel use a net energy yield of about 1000 m³ CH₄ ha^-1 is remaining. With the straw of grain maize or Corn Cob Mix (CCM) similar energy yields can be achieved. In comparison to catch crops remaining on the field as green manure or to complete fallow between main crops the effects on soil, water and climate can be improved if cover crops are harvested without soil compaction and digestate is returned to the field in an amount equivalent to cover crop removal. In this way, the risk of nitrate leaching can be reduced approx. by 25% in comparison to full fallow. The risk of nitrous oxide emissions may be reduced up to 50% by contrast with cover crops serving as green manure. The effects on humus content and erosion are similar or better than those of cover crops used as green manure when the same amount of biomass was produced. With higher biomass production the positive effects increase even if cover crops are harvested and the only digestate is brought back to the fields. The ecological footprint of arable farming can be reduced by approx. 50% considering the substitution of natural gas with CH₄ produced from cover crops.

Keywords: biogas, cover crops, catch crops, land use competition, sustainable agriculture

Procedia PDF Downloads 542

Authors: Yakin Hajlaoui, Richard Labib, Jean-François Plante, Michel Gamache

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This study introduces the Multi-Layer Inverse Distance Weighting Model (ML-IDW), inspired by the mathematical formulation of both multi-layer neural networks (ML-NNs) and Inverse Distance Weighting model (IDW). ML-IDW leverages ML-NNs' processing capabilities, characterized by compositions of learnable non-linear functions applied to input features, and incorporates IDW's ability to learn anisotropic spatial dependencies, presenting a promising solution for nonlinear spatial interpolation and learning from complex spatial data. it employ gradient descent and backpropagation to train ML-IDW, comparing its performance against conventional spatial interpolation models such as Kriging and standard IDW on regression and classification tasks using simulated spatial datasets of varying complexity. the results highlight the efficacy of ML-IDW, particularly in handling complex spatial datasets, exhibiting lower mean square error in regression and higher F1 score in classification.

Keywords: deep learning, multi-layer neural networks, gradient descent, spatial interpolation, inverse distance weighting

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Authors: Aisultan Shoiynbek, Darkhan Kuanyshbay, Paulo Menezes, Akbayan Bekarystankyzy, Assylbek Mukhametzhanov, Temirlan Shoiynbek

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Speech emotion recognition (SER) has received increasing research interest in recent years. It is a common practice to utilize emotional speech collected under controlled conditions recorded by actors imitating and artificially producing emotions in front of a microphone. There are four issues related to that approach: emotions are not natural, meaning that machines are learning to recognize fake emotions; emotions are very limited in quantity and poor in variety of speaking; there is some language dependency in SER; consequently, each time researchers want to start work with SER, they need to find a good emotional database in their language. This paper proposes an approach to create an automatic tool for speech emotion extraction based on facial emotion recognition and describes the sequence of actions involved in the proposed approach. One of the first objectives in the sequence of actions is the speech detection issue. The paper provides a detailed description of the speech detection model based on a fully connected deep neural network for Kazakh and Russian. Despite the high results in speech detection for Kazakh and Russian, the described process is suitable for any language. To investigate the working capacity of the developed model, an analysis of speech detection and extraction from real tasks has been performed.

Keywords: deep neural networks, speech detection, speech emotion recognition, Mel-frequency cepstrum coefficients, collecting speech emotion corpus, collecting speech emotion dataset, Kazakh speech dataset

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Authors: Sneha Sadana

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Human beings are unique and so are their reactions towards varied stimuli. This study focuses on the impact personality has on how one deals with stressful situations. It can be intriguing to know how big of an impact our personality has on the way we react and how it is wired in us to respond to things in a particular manner all because of our personality and the traits which make us who we are. The study was done on 150 college going students, 75 males and 75 females mainly from Ahmedabad, India pursuing a variety of different streams and subjects. The questionnaire consists of two standardized questionnaires which measure stress and personality. The Student Stress Scale by Manju Agarwal evaluates stress of subjects and the big five personality locator by Norman.
The findings showed that there exists a positive relationship between stress and neuroticism and an inverse relationship between stress and sociability, stress and openness, stress and agreeableness and stress and conscientiousness.
And on doing a further comparative analysis on personality types of the same sample it was found out that females were more agreeable, followed by conscientiousness, sociability, openness, and neuroticism. In males, however, it was observed that males were more agreeable, followed by conscientiousness, neuroticism, sociability, and openness

Keywords: college students, personality, stress, theories of personality

Procedia PDF Downloads 336

Authors: Laura O’Halloran, Zhipeng Cao, Clare M. Kelly, Hugh Garavan, Robert Whelan

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Reaction time (RT) variability on cognitive tasks provides the index of the efficiency of executive control processes (e.g. attention and inhibitory control) and is considered to be a hallmark of clinical disorders, such as attention-deficit disorder (ADHD). Increased RT variability is associated with structural and functional brain differences in children and adults with various clinical disorders, as well as poorer task performance accuracy. Furthermore, the strength of functional connectivity across various brain networks, such as the negative relationship between the task-negative default mode network and task-positive attentional networks, has been found to reflect differences in RT variability. Although RT variability may provide an index of attentional efficiency, as well as being a useful indicator of neurological impairment, the brain substrates associated with RT variability remain relatively poorly defined, particularly in a healthy sample. Method: Firstly, we used the intra-individual coefficient of variation (ICV) as an index of RT variability from “Go” responses on the Stop Signal Task. We then examined the functional and structural neural correlates of ICV in a large sample of 14-year old healthy adolescents (n=1719). Of these, a subset had elevated symptoms of ADHD (n=80) and was compared to a matched non-symptomatic control group (n=80). The relationship between brain activity during successful and unsuccessful inhibitions and gray matter volume were compared with the ICV. A mediation analysis was conducted to examine if specific brain regions mediated the relationship between ADHD symptoms and ICV. Lastly, we looked at functional connectivity across various brain networks and quantified both positive and negative correlations during “Go” responses on the Stop Signal Task. Results: The brain data revealed that higher ICV was associated with increased structural and functional brain activation in the precentral gyrus in the whole sample and in adolescents with ADHD symptoms. Lower ICV was associated with lower activation in the anterior cingulate cortex (ACC) and medial frontal gyrus in the whole sample and in the control group. Furthermore, our results indicated that activation in the precentral gyrus (Broadman Area 4) mediated the relationship between ADHD symptoms and behavioural ICV. Conclusion: This is the first study first to investigate the functional and structural correlates of ICV collectively in a large adolescent sample. Our findings demonstrate a concurrent increase in brain structure and function within task-active prefrontal networks as a function of increased RT variability. Furthermore, structural and functional brain activation patterns in the ACC, and medial frontal gyrus plays a role-optimizing top-down control in order to maintain task performance. Our results also evidenced clear differences in brain morphometry between adolescents with symptoms of ADHD but without clinical diagnosis and typically developing controls. Our findings shed light on specific functional and structural brain regions that are implicated in ICV and yield insights into effective cognitive control in healthy individuals and in clinical groups.

Keywords: ADHD, fMRI, reaction-time variability, default mode, functional connectivity

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Authors: Adian Fatchur Rochim, Abdul Muis, Riri Fitri Sari

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H-index has been widely used as a performance indicator of researchers around the world especially in Indonesia. The Government uses Scopus and Google scholar as indexing references in providing recognition and appreciation. However, those two indexing services yield to different H-index values. For that purpose, this paper evaluates the difference of the H-index from those services. Researchers indexed by Webometrics, are used as reference’s data in this paper. Currently, Webometrics only uses H-index from Google Scholar. This paper observed and compared corresponding researchers’ data from Scopus to get their H-index score. Subsequently, some researchers with huge differences in score are observed in more detail on their paper’s publisher. This paper shows that the H-index of researchers in Google Scholar is approximately 2.45 times of their Scopus H-Index. Most difference exists due to the existence of uncertified publishers, which is considered in Google Scholar but not in Scopus.

Keywords: Google Scholar, H-index, Scopus, performance indicator

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Authors: Pouriya Darabiyan, Hadis Nazari, Kourosh Zarea, Saeed Ghanbari, Zeinab Raiesifar, Morteza Khafaie, Hanna Tuvesson

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Introduction Since the beginning of mobile phone addiction, alexithymia, depression, anxiety and stress have been stated as risk factors for Internet addiction, so this study was conducted with the aim of investigating the relationship between Alexithymia and Mobile phone addiction along with the mediating role of anxiety, stress and depression. Materials and methods In this descriptive-analytical and cross-sectional study in 2022, 412 students School of Nursing & Midwifery of Ahvaz Jundishapur University of Medical Sciences were included in the study using available sampling method. Data collection tools were: Demographic Information Questionnaire, Toronto Alexithymia Scale (TAS-20), Depression, Anxiety, Stress Scale (DASS-21) and Mobile Phone Addiction Index (MPAI). Frequency, Pearson correlation coefficient test and linear regression were used to describe and analyze the data. Also, structural equation models and path analysis method were used to investigate the direct and indirect effects as well as the total effect of each dimension of Alexithymia on Mobile phone addiction with the mediating role of stress, depression and anxiety. Statistical analysis was done by SPSS version 22 and Amos version 16 software. Results Alexithymia was a predictive factor for mobile phone addiction. Also, Alexithymia had a positive and significant effect on depression, anxiety and stress. Depression, anxiety and stress had a positive and significant effect on mobile phone addiction. Depression, anxiety and stress variables played the role of a relative mediating variable between Alexithymia and mobile phone addiction. Alexithymia through depression, anxiety and stress also has an indirect effect on Internet addiction. Conclusion Alexithymia is a predictive factor for mobile phone addiction; And the variables of depression, anxiety and stress play the role of a relative mediating variable between Alexithymia and mobile phone addiction.

Keywords: alexithymia, mobile phone, depression, anxiety, stress

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Authors: Parisa Golbayani, Dan Wang, Ionut¸ Florescu

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In this work we implement machine learning techniques to financial statement reports in order to asses company’s credit rating. Specifically, the work analyzes the performance of four neural network architectures (MLP, CNN, CNN2D, LSTM) in predicting corporate credit rating as issued by Standard and Poor’s. The paper focuses on companies from the energy, financial, and healthcare sectors in the US. The goal of this analysis is to improve application of machine learning algorithms to credit assessment. To accomplish this, the study investigates three questions. First, we investigate if the algorithms perform better when using a selected subset of important features or whether better performance is obtained by allowing the algorithms to select features themselves. Second, we address the temporal aspect inherent in financial data and study whether it is important for the results obtained by a machine learning algorithm. Third, we aim to answer if one of the four particular neural network architectures considered consistently outperforms the others, and if so under which conditions. This work frames the problem as several case studies to answer these questions and analyze the results using ANOVA and multiple comparison testing procedures.

Keywords: convolutional neural network, long short term memory, multilayer perceptron, credit rating

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Authors: André Smith, Amr Abdel-Dayem

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The stereo matching problem, while having been present for several decades, continues to be an active area of research. The goal of this research is to find correspondences between elements found in a set of stereoscopic images. With these pairings, it is possible to infer the distance of objects within a scene, relative to the observer. Advancements in this field have led to experimentations with various techniques, from graph-cut energy minimization to artificial neural networks. At the basis of these techniques is a cost function, which is used to evaluate the likelihood of a particular match between points in each image. While at its core, the cost is based on comparing the image pixel data; there is a general lack of consistency as to what image data representation to use. This paper presents an experimental analysis to compare the effectiveness of more common image data representations. The goal is to determine the effectiveness of these data representations to reduce the cost for the correct correspondence relative to other possible matches.

Keywords: colour data, local stereo matching, stereo correspondence, disparity map

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Authors: Intissar Farid

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Groundwater quality assessment is crucial for sustainable water use, especially in semi-arid regions like the Jeloula basin in Tunisia, where groundwater is essential for domestic and agricultural needs. The present research aims to characterize the suitability of groundwater for irrigational purposes by considering various parameters: total salt concentration as measured by Electrical Conductivity EC, relative proportions of Na⁺ as expressed by %Na and SAR, Kelly’s ratio, Permeability Index, Magnesium hazard and Residual Sodium chloride. Chemical data indicate that the percent sodium (%Na) in the study area ranged from 26.3 to 45.3%. According to the Wilcox diagram, the quality classification of irrigation water suggests that analyzed groundwaters are suitable for irrigation purposes. The SAR values vary between 2.1 and 5. Most of the groundwater samples plot in the Richards’C3S1 water class and indicate little danger from sodium content to soil and plant growth. The Kelly’s ratio of the analyzed samples ranged from 0.3 to 0.8. These values indicate that the waters are fit for agricultural purposes. Magnesium hazard (MH) values range from 27.5 to 52.6, with an average of 38.9 in the analyzed waters. Hence, the Mg²⁺ content of the groundwater from the shallow aquifer cannot cause any problem to the soil permeability. Permeability index (PI) values computed for the area ranged from 33.6 to 52.7%. The above result, therefore, suggests that most of the water samples fall within class I of the Doneen chart and can be categorized as good irrigation water. The groundwaters collected from the Jeloula shallow aquifer were found to be within the safe limits and thus suitable for irrigation purposes.

Keywords: Kelly's ratio, magnesium hazard, permeability index, residual sodium chloride

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Authors: Abdul Walusansa

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The aim of this study was to assess the level of microbial pollution along Nakibiso stream. The study was carried out in polluted waters of Nakibiso stream, originating from Mbale municipality and running through ADRA Estates to Namatala Wetlands in Eastern Uganda. Four sites along the stream were selected basing on the activities of their vicinity. A total of 120 samples were collected in sterile bottles from the four sampling locations of the stream during the wet and dry seasons of the year 2011. The samples were taken to the National water and Sewerage Cooperation Laboratory for Analysis. Membrane filter technique was used to test for Erischerichia coli. Nitrogen, Phosphorus, pH, dissolved oxygen, electrical conductivity, total suspended solids, turbidity and temperature were also measured. Results for Nitrogen and Phosphorus for sites; 1, 2, 3 and 4 were 1.8, 8.8, 7.7 and 13.8 NH4-N mg/L; and 1.8, 2.1, 1.8 and 2.3 PO4-P mg/L respectively. Basing on these results, it was estimated that farmers use 115 and 24 Kg/acre of Nitrogen and Phosphorus respectively per month. Taking results for Nitrogen, the same amount of Nutrients in artificial fertilizers would cost $ 88. This shows that reuse of wastewater has a potential in terms of nutrients. The results for E. coli for sites 1, 2, 3 and 4 were 1.1 X 107, 9.1 X 105, 7.4 X 105, and 3.4 X 105 respectively. E. coli hence decreased downstream with statistically significant variations between sites 1 and 4. Site 1 had the highest mean E.coli counts. The bacterial contamination was significantly higher during the dry season when more water was needed for irrigation. Although the water had the potential for reuse in farming, bacterial contamination during both seasons was higher than 103 FC/100ml recommended by WHO for unrestricted Agriculture.

Keywords: E. coli, nitrogen, phosphorus, water reuse, waste water

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Authors: D. Pugazhenthi, B. Sree Vidya

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Cloud computing is one of the emerging technologies that enables end users to use the services of cloud on ‘pay per usage’ strategy. This technology grows in a fast pace and so is its security threat. One among the various services provided by cloud is storage. In this service, security plays a vital factor for both authenticating legitimate users and protection of information. This paper brings in efficient ways of authenticating users as well as securing information on the cloud. Initial phase proposed in this paper deals with an authentication technique using multi-factor and multi-dimensional authentication system with multi-level security. Unique identification and slow intrusive formulates an advanced reliability on user-behaviour based biometrics than conventional means of password authentication. By biometric systems, the accounts are accessed only by a legitimate user and not by a nonentity. The biometric templates employed here do not include single trait but multiple, viz., iris and finger prints. The coordinating stage of the authentication system functions on Ensemble Support Vector Machine (SVM) and optimization by assembling weights of base SVMs for SVM ensemble after individual SVM of ensemble is trained by the Artificial Fish Swarm Algorithm (AFSA). Thus it helps in generating a user-specific secure cryptographic key of the multimodal biometric template by fusion process. Data security problem is averted and enhanced security architecture is proposed using encryption and decryption system with double key cryptography based on Fuzzy Neural Network (FNN) for data storing and retrieval in cloud computing . The proposing scheme aims to protect the records from hackers by arresting the breaking of cipher text to original text. This improves the authentication performance that the proposed double cryptographic key scheme is capable of providing better user authentication and better security which distinguish between the genuine and fake users. Thus, there are three important modules in this proposed work such as 1) Feature extraction, 2) Multimodal biometric template generation and 3) Cryptographic key generation. The extraction of the feature and texture properties from the respective fingerprint and iris images has been done initially. Finally, with the help of fuzzy neural network and symmetric cryptography algorithm, the technique of double key encryption technique has been developed. As the proposed approach is based on neural networks, it has the advantage of not being decrypted by the hacker even though the data were hacked already. The results prove that authentication process is optimal and stored information is secured.

Keywords: artificial fish swarm algorithm (AFSA), biometric authentication, decryption, encryption, fingerprint, fusion, fuzzy neural network (FNN), iris, multi-modal, support vector machine classification

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Authors: Salim Ouchtati, Aissa Belmeguenai, Mouldi Bedda

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In this study, we propose an offline system for the recognition of the fragmented handwritten numeric chains. Firstly, we realized a recognition system of the isolated handwritten digits, in this part; the study is based mainly on the evaluation of neural network performances, trained with the gradient backpropagation algorithm. The used parameters to form the input vector of the neural network are extracted from the binary images of the isolated handwritten digit by several methods: the distribution sequence, sondes application, the Barr features, and the centered moments of the different projections and profiles. Secondly, the study is extended for the reading of the fragmented handwritten numeric chains constituted of a variable number of digits. The vertical projection was used to segment the numeric chain at isolated digits and every digit (or segment) was presented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits).

Keywords: features extraction, handwritten numeric chains, image processing, neural networks

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Authors: Abdulrazzaq Hammal

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In this study, Syrian sand was used to create ceramic membranes. The process of preparing the membranes involved several steps, starting with the purification of the studied sand using hydrochloric acid, sorting according to granular size, and mixing the sand with liquid sodium silicates as a binder. Next, the effects of binder ratio, pressure formation, treatment temperature, and sand grain size were studied. Further, nanoparticles of silver were added to the formed membranes to improve their ability to purify bacterially polluted water. Prepared membranes were quite successful in removing bacteria and chemicals from water, and the water's requirements were brought up to level with Syrian drinking water standards.

Keywords: ceramic, membrane, water, wastewater

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Authors: Deepa T., Inchara S. R., Venkatesh S. V., Seema Tharannum

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Concrete is the most widely used structural material. It is usually made using locally available materials. However, concrete has low tensile strength and may crack in the early days with exothermic hydration, for which water is essential. To address the increased construction water demand, treated greywater may be used. Bacillus subtilis bacteria that form endospores is the biological agent considered in this study for biomineralization or Microbially Induced Calcite Precipitation (MICP) technique to heal cracks. Treated grey water which is obtained from STP of PES University, opted in place of Potable water, which had qualities within the standard range as per codal provisions. In this work, M30 grade conventional concrete is designed using OPC 53-grade cement, manufactured sand, natural coarse aggregates, and potable water. Conventional concrete (CC), bacterial concrete with potable water (BS), and treated grey water concrete (TGWBS) are the three different concrete specimens cast. Experimental studies such as the strength test and the surface hardness test are performed on conventional and bacterial concrete samples after 7, 28, and 56 days of curing. Concrete cubes are subjected to a temperature of 50° C to investigate the effect of higher temperature. Cracked cube specimens are observed for self-healing -as well as microstructure analysis with Scanning Electron Microscope (SEM), Energy Dispersive X-Ray Analysis (EDAX), and X-Ray Diffraction Analysis (XRD). Noticeable calcium salt deposition is observed on the surface of the BS and TGWBS cracked specimen. Surface hardness and the EDAX test gave promising results on the advantage of using spore-forming bacteria in concrete. This is followed by the strength gained in compression and flexure. Results also indicate that treated grey water can be a substitute for potable water in concrete.

Keywords: Bacillus subtilis concrete, microstructure, temperature, treated greywater

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Authors: Hülya Torun, Ondřej Novák, Jaromír Mikulík, Miroslav Strnad, Faik A. Ayaz

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World climate is changing. Millions of people in the world still face chronic undernourishment for conducting a healthy life and the world’s population is growing steadily. To meet this growing demand, agriculture and food systems must adapt to the adverse effects of climate change and become more resilient, productive and sustainable. From this perspective, to determine tolerant cultivars for undesirable environmental conditions will be necessary food production for sustainable development. Among abiotic stresses, soil salinity is one of the most detrimental global fact restricting plant sources. Development of salt-tolerant lines is required in order to increase the crop productivity and quality in salt-treated lands. Therefore, the objective of this study was to investigate the morphological and physiological responses of barley cultivars accessions to salinity stress by NaCl. For this purpose, it was aimed to determine the crosstalk between some endogenous phytohormones and exogenous salicylic acid (SA) in two different vegetative parts (leaves and roots) of barley (Hordeum vulgare L.; Poaceae; 2n=14; Ince-04) which is detected salt-tolerant. The effects of SA on growth parameters, leaf relative water content (RWC), endogenous phytohormones; including indole-3-acetic acid (IAA), cytokinins (CKs), abscisic acid (ABA), jasmonic acid (JA) and ethylene were investigated in barley cultivars under salinity stress. SA was applied to 17-day-old seedlings of barley in two different ways including before (pre-treated for 24 h) and simultaneously with NaCl stress treatment. NaCl (0, 150, 300 mM) exposure in the hydrophonic system was associated with a rapid decrease in growth parameters and RWC, which is an indicator of plant water status, resulted in a strong up-regulation of ABA as a stress indicator. Roots were more dramatically affected than leaves. Water conservation in 150 mM NaCl treated-barley plants did not change, but decreased in 300 mM NaCl treated plants. Pre- and simultaneously treatment of SA did not significantly alter growth parameters and RWC. ABA, JA and ethylene are known to be related with stress. In the present work, ethylene also increased, similarly to ABA, but not with the same intensity. While ABA and ethylene increased by the increment of salt concentrations, JA levels rapidly decreased especially in roots. Both pre- and simultaneously SA applications alleviated salt-induced decreases in 300 mM NaCl resulted in the increment of ABA levels. CKs and IAA are related to cell growth and development. At high salinity (300 mM NaCl), CKs (cZ+cZR) contents increased in both vegetative organs while IAA levels stayed at the same level with control groups. However, IAA increased and cZ+cZR rapidly decreased in leaves of barley plants with SA treatments before salt applications (in pre- SA treated groups). Simultaneously application of SA decreased CKs levels in both leaves and roots of the cultivar. Due to increasing concentrations of NaCl in association with decreasing ABA, JA and ethylene content and increments in CKs and IAA were recorded with SA treatments. As results of the study, in view of all the phytohormones that we tested, exogenous SA induced greater tolerance to salinity particularly when applied before salinity stress.

Keywords: Barley, Hordeum vulgare, phytohormones, salicylic acid, salinity

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Authors: M. R. Noor El-Din, Marwa R. Mishrif, R. E. Morsi, E. A. El-Sharaky, M. E. Haseeb, Rania T. M. Ghanem

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This paper studies the physical and rheological behaviours of water/in/diesel fuel nanoemulsions prepared by modified low energy method. Twenty of water/in/diesel fuel nanoemulsions were prepared using mixed nonionic surfactants of sorbitan monooleate and polyoxyethylene sorbitan trioleate (MTS) at Hydrophilic-Lipophilic Balance (HLB) value of 10 and a working temperature of 20°C. The influence of the prepared nanoemulsions on the physical properties such as kinematic viscosity, density, and calorific value was studied. Also, nanoemulsion systems were subjected to rheological evaluation. The effect of water loading percentage (5, 6, 7, 8, 9 and 10 wt.%) on rheology was assessed at temperatures range from 20 to 60°C with temperature interval of 10 for time lapse 0, 1, 2 and 3 months, respectively. Results show that all of the sets nanoemulsions exhibited a Newtonian flow character of low-shear viscosity in the range of 132 up to 191 1/s, and followed by a shear-thinning region with yield value (Non-Newtonian behaviour) at high shear rate for all water ratios (5 to 10 wt.%) and at all test temperatures (20 to 60°C) for time ageing up to 3 months. Also, the viscosity/temperature relationship of all nanoemulsions fitted well Arrhenius equation with high correlation coefficients that ascertain their Newtonian behavior.

Keywords: alternative fuel, nanoemulsion, surfactant, diesel fuel

Procedia PDF Downloads 313

Authors: Seon Tae Kim, Robert Hegner, Goksel Ozuylasi, Panagiotis Stathopoulos, Eberhard Nicke

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High-temperature heat pumps (HTHP) that can supply heat at temperatures above 200°C can enhance the energy efficiency of industrial processes and reduce the CO₂ emissions connected with the heat supply of these processes. In the current work, the thermodynamic performance of 3 different vapor compression cycles, which use R-718 (water) as a working medium, have been evaluated by using a commercial process simulation tool (EBSILON Professional). All considered cycles use two-stage vapor compression with intercooling between stages. The main aim of the study is to compare different intercooling strategies and study possible heat recovery scenarios within the intercooling process. This comparison has been carried out by computing the coefficient of performance (COP), the heat supply temperature level, and the respective mass flow rate of water for all cycle architectures. With increasing temperature difference between the heat source and heat sink, ∆T, the COP values decreased as expected, and the highest COP value was found for the cycle configurations where both compressors have the same pressure ratio (PR). The investigation on the HTHP capacities with optimized PR and exergy analysis has also been carried out. The internal heat exchanger cycle with the inward direction of secondary flow (IHX-in) showed a higher temperature level and exergy efficiency compared to other cycles. Moreover, the available operating range was estimated by considering mechanical limitations.

Keywords: high temperature heat pump, industrial process, vapor compression cycle, R-718 (water), thermodynamic analysis

Procedia PDF Downloads 149

Authors: Kefaya Qaddoum

Abstract:

The aim of this study is to predict breast cancer and to construct a supportive model that will stimulate a more reliable prediction as a factor that is fundamental for public health. In this study, we utilize general regression neural networks (GRNN) to replace the normal predictions with prediction periods to achieve a reasonable percentage of confidence. The mechanism employed here utilises a machine learning system called conformal prediction (CP), in order to assign consistent confidence measures to predictions, which are combined with GRNN. We apply the resulting algorithm to the problem of breast cancer diagnosis. The results show that the prediction constructed by this method is reasonable and could be useful in practice.

Keywords: neural network, conformal prediction, cancer classification, regression

Procedia PDF Downloads 291

Authors: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

Abstract:

Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

Procedia PDF Downloads 119

Authors: Dewi Hardiningtyas Sugiono

Abstract:

Concerning towards the quality of air comfort and safety to pedestrians in the University area should be increased as Indonesia economics booming. Hence, the University management needs guidelines of thermal comfort to innovate a new layout building. The objectives of this study is to investigate and then to evaluate the distribution of thermal comfort which is indicated by predicted mean vote (PMV) index at the University of Brawijaya (UB), Malang. The PMV figures are used to evaluate and to redesign the UB layout. The research is started with study literature and early survey to collect all information of building layout and building shape at the University of Brawijaya. The information is used to create a 3D model in CAD software. The model is simulated by Computational Fluid Dynamic (CFD) software to measure the PMV factors of air temperature, relative humidity and air speed in some locations. Validation is done by comparing between PMV value from observation and PMV value from simulation. The resuls of the research shows the most sensitive of microclimatic factors is air temperature surrounding the UB building. Finally, the research is successfully figure out the UB layout and provides further actions to increase the thermal comfort.

Keywords: thermal comfort, heat index (HI), CFD, layout

Procedia PDF Downloads 305

Authors: Turuganti Venkateswarlu, Jagadeesh Anmala

Abstract:

Excessive runoffs from various non-point source land uses, and other point sources are rapidly contaminating the water quality of streams in the Upper Green River watershed, Kentucky, USA. It is essential to maintain the stream water quality as the river basin is one of the major freshwater sources in this province. It is also important to understand the water quality parameters (WQPs) quantitatively and qualitatively along with their important features as stream water is sensitive to climatic events and land-use practices. In this paper, a model was developed for predicting one of the significant WQPs, Fecal Coliform (FC) from precipitation, temperature, urban land use factor (ULUF), agricultural land use factor (ALUF), and forest land-use factor (FLUF) using Random Forest (RF) algorithm. The RF model, a novel ensemble learning algorithm, can even find out advanced feature importance characteristics from the given model inputs for different combinations. This model’s outcomes showed a good correlation between FC and climate events and land use factors (R2 = 0.94) and precipitation and temperature are the primary influencing factors for FC.

Keywords: water quality, land use factors, random forest, fecal coliform

Procedia PDF Downloads 197

Authors: Minto Rattan, Tania Bose, Neeraj Chamoli

Abstract:

The present paper investigates the effect of linear thermal gradient on the steady-state creep behavior of rotating isotropic disc using threshold stress based Sherby’s creep law. The composite discs made of aluminum matrix reinforced with silicon carbide particulate has been taken for analysis. The stress and strain rate distributions have been calculated for discs rotating at linear thermal gradation using von Mises’ yield criterion. The material parameters have been estimated by regression fit of the available experimental data. The results are displayed and compared graphically in designer friendly format for the above said temperature profile with the disc operating under uniform temperature profile. It is observed that radial and tangential stresses show minor variation and the strain rates vary significantly in the presence of thermal gradation as compared to disc having uniform temperature.

Keywords: creep, isotropic, steady-state, thermal gradient

Procedia PDF Downloads 269

Authors: P. Góes, J. Manzi

Abstract:

This paper focuses on the development of an energy efficiency index that will be applied to reactive systems, which is based in the First and Second Law of Thermodynamics, by giving particular consideration to the concept of maximum entropy. Among the requirements of such energy efficiency index, the practical feasibility must be essential. To illustrate the performance of the proposed index, such an index was used as decisive factor of evaluation for the optimization process of an industrial reactor. The results allow the conclusion to be drawn that the energy efficiency index applied to the reactive system is consistent because it extracts the information expected of an efficient indicator, and that it is useful as an analytical tool besides being feasible from a practical standpoint. Furthermore, it has proved to be much simpler to use than tools based on traditional methodologies.

Keywords: energy, efficiency, entropy, reactive

Procedia PDF Downloads 411

Authors: Muhammet Emre Turan, Sait Özçelik, Yavuz Sun

Abstract:

In railway rails, residual stress was measured and the values of residual stress were associated with hardness and micro structure in this study. At first, three rails as one meter long were taken and residual stresses were measured by cutting method according to the EN 13674-1 standardization. In this study, strain gauge that is an electrical apparatus was used. During the cutting, change in resistance in rail gave us residual stress value via computer program. After residual stress measurement, Brinell hardness distribution were performed for head parts of rails. Thus, the relationship between residual stress and hardness were established. In addition to that, micro structure analysis was carried out by optical microscope. The results show that, the micro structure and hardness value was changed with residual stress.

Keywords: residual stress, hardness, micro structure, rail, strain gauge

Procedia PDF Downloads 602

Authors: Mirza Mujtaba Baig

Abstract:

Artificial Intelligence (AI) is evolving rapidly, and one of the areas in which this field has influenced is automation. The automobile, healthcare, education, and robotic industries deploy AI technologies constantly, and the automation of tasks is beneficial to allow time for knowledge-based tasks and also introduce convenience to everyday human endeavors. The paper reviews the challenges faced with the current implementations of autonomous self-driving cars by exploring the machine learning, robotics, and artificial intelligence techniques employed for the development of this innovation. The controversy surrounding the development and deployment of autonomous machines, e.g., vehicles, begs the need for the exploration of the configuration of the programming modules. This paper seeks to add to the body of knowledge of research assisting researchers in decreasing the inconsistencies in current programming modules. Blockchain is a technology of which applications are mostly found within the domains of financial, pharmaceutical, manufacturing, and artificial intelligence. The registering of events in a secured manner as well as applying external algorithms required for the data analytics are especially helpful for integrating, adapting, maintaining, and extending to new domains, especially predictive analytics applications.

Keywords: artificial intelligence, automation, big data, self-driving cars, machine learning, neural networking algorithm, blockchain, business intelligence

Procedia PDF Downloads 119