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

Authors: Cedric Leong, Parth Desai, Parth Patel

Abstract:

The purpose of this project was to develop a neural network based on qualitative team data to predict alliance scores to determine winners of matches in the FIRST Robotics Competition (FRC). The game for the competition changes every year with different objectives and game objects, however the idea was to create a prediction system which can be reused year by year using some of the statistics that are constant through different games, making our system adaptable to future games as well. Aerial Assist is the FRC game for 2014, and is played in alliances of 3 teams going against one another, namely the Red and Blue alliances. This application takes any 6 teams paired into 2 alliances of 3 teams and generates the prediction for the final score between them.

Keywords: artifical neural network, prediction system, qualitative team data, FIRST Robotics Competition (FRC)

Procedia PDF Downloads 513

Authors: Sara Bahariderakhshan, Morteza Ahmadifar

Abstract:

Leakage is one of the most important problems that water distribution networks face which first reason is high-pressure existence. There are many approaches to control this excess pressure, which using pressure reducing valves (PRVs) or reducing pipe diameter are ones. In the other hand, Pumps are using electricity or fossil fuels to supply needed pressure in distribution networks but excess pressure are made in some branches due to topology problems and water networks’ variables therefore using pressure valves will be inevitable. Although using PRVs is inevitable but it leads to waste electricity or fuels used by pumps because PRVs just waste excess hydraulic pressure to lower it. Pumps working in reverse or Pumps as Turbine (called PaT in this article) are easily available and also effective sources of reducing the equipment cost in small hydropower plants. Urban areas of developing countries are facing increasing in area and maybe water scarcity in near future. These cities need wider water networks which make it hard to predict, control and have a better operation in the urban water cycle. Using more energy and, therefore, more pollution, slower repairing services, more user dissatisfaction and more leakage are these networks’ serious problems. Therefore, more effective systems are needed to monitor and act in these complicated networks than what is used now. In this article a new approach is proposed and evaluated: Using PAT to produce enough energy for remote valves and sensors in the water network. These sensors can be used to determine the discharge, pressure, water quality and other important network characteristics. With the help of remote valves pipeline discharge can be controlled so Instead of wasting excess hydraulic pressure which may be destructive in some cases, obtaining extra pressure from pipeline and producing clean electricity used by remote instruments is this articles’ goal. Furthermore due to increasing the area of the network there is unwanted high pressure in some critical points which is not destructive but lowering the pressure results to longer lifetime for pipeline networks without users’ dissatisfaction. This strategy proposed in this article, leads to use PaT widely for pressure containment and producing energy needed for remote valves and sensors like what happens in supervisory control and data acquisition (SCADA) systems which make it easy for us to monitor, receive data from urban water cycle and make any needed changes in discharge and pressure of pipelines easily and remotely. This is a clean project of energy production without significant environmental impacts and can be used in urban drinking water networks, without any problem for consumers which leads to a stable and dynamic network which lowers leakage and pollution.

Keywords: new energies, pump as turbine, drinking water, distribution network, remote control equipments

Procedia PDF Downloads 463

Authors: Rayhaneh Amooaghaie, Masoomeh Nabaei

Abstract:

In this study, 200 µM Cd reduced relative seed germination, root elongation tolerance and seed germination tolerance index of Catharanthus roseus. The melatonin improved seed germination, germination velocity, seedling length and vigor index under Cd stress in a dose-dependent manner and the maximum biological responses obtained by 100 μM melatonin. However, 200-400 μM melatonin and 400 μM SNP had negative effects that evidenced as lower germination indices and poor establishment of seedlings. The cadmium suppressed amylase activity and contents of soluble and reducing sugars in germinating seeds, thereby reduced seed germination and subsequent seedling growth whereas increased electrolyte leakage. These Cd-induced inhibitory effects were ameliorated by melatonin.

Keywords: cadmium, Catharanthus roseus, melatonin, seed germination

Procedia PDF Downloads 177

Authors: Sina Moradi, Sanly Liu, Christopher W. K. Chow, John Van Leeuwen, David Cook, Mary Drikas, Soha Habibi, Rose Amal

Abstract:

In recent years, chloramine has been widely used for both primary and secondary disinfection. However, a major concern with the use of chloramine as a secondary disinfectant is the decay of chloramine and nitrification occurrence. The management of chloramine decay and the prevention of nitrification are critical for water utilities managing chloraminated drinking water distribution systems. The detection and monitoring of nitrification episodes is usually carried out through measuring certain water quality parameters, which are commonly referred to as indicators of nitrification. The approach taken in this study was to collect water samples from different sites throughout a drinking water distribution systems, Tailem Bend – Keith (TBK) in South Australia, and analyse the samples by high performance size exclusion chromatography (HPSEC). We investigated potential association between the water qualities from HPSEC analysis with chloramine decay and/or nitrification occurrence. MATLAB 8.4 was used for data processing of HPSEC data and chloramine decay. An increase in the absorbance signal of HPSEC profiles at λ=230 nm between apparent molecular weights of 200 to 1000 Da was observed at sampling sites that experienced rapid chloramine decay and nitrification while its absorbance signal of HPSEC profiles at λ=254 nm decreased. An increase in absorbance at λ=230 nm and AMW < 500 Da was detected for Raukkan CT (R.C.T), a location that experienced nitrification and had significantly lower chloramine residual (<0.1 mg/L). This increase in absorbance was not detected in other sites that did not experience nitrification. Moreover, the UV absorbance at 254 nm of the HPSEC spectra was lower at R.C.T. than other sites. In this study, a chloramine residual index (C.R.I) was introduced as a new indicator of chloramine decay and nitrification occurrence, and is defined based on the ratio of area underneath the HPSEC spectra at two different wavelengths of 230 and 254 nm. The C.R.I index is able to indicate DS sites that experienced nitrification and rapid chloramine loss. This index could be useful for water treatment and distribution system managers to know if nitrification is occurring at a specific location in water distribution systems.

Keywords: nitrification, HPSEC, chloramine decay, chloramine residual index

Procedia PDF Downloads 298

Authors: Ghulam Hassan Abbasi, Moazzam Jamil, Ghazala Akhtar, M.Anwar-ul-Haq

Abstract:

Heavy metals are significant pollutants in environment and their toxicity is a problem for survival of living things while salicylic acid (SA) is signaling and ubiquitous bioactive molecule that regulates cellular mechanism in plants under stress condition. Therefore, exogenous application of salicylic acid (SA) under chromium stress in two chickpea varieties were investigated in hydroponic experiment with five treatments comprising of control, 5 µM Cr + 5 mM SA, 5µM Cr + 10 mM SA, 10µM Cr + 5 mM SA, and 10µM Cr + 10 mM SA. Results revealed that treatments of plants with 10 mM SA application under both 5 µM Cr and 10 µM Cr stress resulted in maximum improvement in plant morphological attributes (root and shoot length, root and shoot fresh and dry weight, membrane stability index and relative water contents) relative to 5 mM SA application in both chickpea varieties. Results regarding Cr concentration showed that Cr was more retained in roots followed by shoots and maximum reduction in Cr uptake was observed at 10 mM SA application. Chickpea variety BRC-61 showed maximum growth and least concentration of Cr in root and shoot relative to BRC-390 variety.

Keywords: chromium, Chickpea, salicylic acid, growth

Procedia PDF Downloads 512

Authors: Philipp Ruf, Massiwa Chabbi, Christoph Reich, Djaffar Ould-Abdeslam

Abstract:

In recent years, convolutional neural networks (CNN) have demonstrated high performance in image analysis, but oftentimes, there is only structured data available regarding a specific problem. By interpreting structured data as images, CNNs can effectively learn and extract valuable insights from tabular data, leading to improved predictive accuracy and uncovering hidden patterns that may not be apparent in traditional structured data analysis. In applying a single neural network for analyzing multimodal data, e.g., both structured and unstructured information, significant advantages in terms of time complexity and energy efficiency can be achieved. Converting structured data into images and merging them with existing visual material offers a promising solution for applying CNN in multimodal datasets, as they often occur in a medical context. By employing suitable preprocessing techniques, structured data is transformed into image representations, where the respective features are expressed as different formations of colors and shapes. In an additional step, these representations are fused with existing images to incorporate both types of information. This final image is finally analyzed using a CNN.

Keywords: CNN, image processing, tabular data, mixed dataset, data transformation, multimodal fusion

Procedia PDF Downloads 123

Authors: Marwa Ahmed Abd El Hamied

Abstract:

In this paper In this paper, a new technique based on Artificial Immunity System (AIS) technique has been developed to track Maximum Power Point (MPP). AIS system is implemented in a photovoltaic system that is subjected to variable temperature and insulation condition. The proposed novel is simulated using Mat Lab program. The results of simulation have been compared to those who are generated from Observation Controller. The proposed model shows promising results as it provide better accuracy comparing to classical model.

Keywords: component, artificial immunity technique, solar energy, perturbation and observation, power based methods

Procedia PDF Downloads 427

Authors: Morteza Ahmadifar, Sarah Bahari Derakhshan

Abstract:

Leakage is one of the most important problems that water distribution networks face which first reason is high-pressure existence. There are many approaches to control this excess pressure, which using pressure reducing valves (PRVs) or reducing pipe diameter are ones. On the other hand, Pumps are using electricity or fossil fuels to supply needed pressure in distribution networks but excess pressure are made in some branches due to topology problems and water networks’ variables, therefore using pressure valves will be inevitable. Although using PRVs is inevitable but it leads to waste electricity or fuels used by pumps because PRVs just waste excess hydraulic pressure to lower it. Pumps working in reverse or Pumps as Turbine (called PAT in this article) are easily available and also effective sources of reducing the equipment cost in small hydropower plants. Urban areas of developing countries are facing increasing in area and maybe water scarcity in near future. These cities need wider water networks which make it hard to predict, control and have a better operation in the urban water cycle. Using more energy and therefore more pollution, slower repairing services, more user dissatisfaction and more leakage are these networks’ serious problems. Therefore, more effective systems are needed to monitor and act in these complicated networks than what is used now. In this article a new approach is proposed and evaluated: Using PAT to produce enough energy for remote valves and sensors in the water network. These sensors can be used to determine the discharge, pressure, water quality and other important network characteristics. With the help of remote valves pipeline discharge can be controlled so Instead of wasting excess hydraulic pressure which may be destructive in some cases, obtaining extra pressure from pipeline and producing clean electricity used by remote instruments is this articles’ goal. Furthermore, due to increasing the area of network there is unwanted high pressure in some critical points which is not destructive but lowering the pressure results to longer lifetime for pipeline networks without users’ dissatisfaction. This strategy proposed in this article, leads to use PAT widely for pressure containment and producing energy needed for remote valves and sensors like what happens in supervisory control and data acquisition (SCADA) systems which make it easy for us to monitor, receive data from urban water cycle and make any needed changes in discharge and pressure of pipelines easily and remotely. This is a clean project of energy production without significant environmental impacts and can be used in urban drinking water networks, without any problem for consumers which leads to a stable and dynamic network which lowers leakage and pollution.

Keywords: clean energies, pump as turbine, remote control, urban water distribution network

Procedia PDF Downloads 393

Authors: T. S. Yang, J. H. Liang

Abstract:

This study applies the finite element method (FEM) to predict maximum forging load, effective stress distribution, effective strain distribution, workpiece temperature temperature in spiral bevel gear forging of AISI 1015. Maximum forging load, effective stress, effective strain, workpiece temperature are determined for different process parameters, such as modules, number of teeth, helical angle and workpiece temperature of the spiral bevel gear hot forging, using the FEM. Finally, the prediction of the power requirement for the spiral bevel gear hot forging of AISI 1015 is determined.

Keywords: spiral bevel gear, hot forging, finite element method

Procedia PDF Downloads 478

Authors: Alisa Salimova, Jiane Zuo, Christopher Homer

Abstract:

The objective of this study is to analyse the North Canal riparian zone land use with the help of remote sensing analysis in ArcGis using 30 cloudless Landsat8 open-source satellite images from May to August of 2013 and 2017. Land cover, urban construction, heat island effect, vegetation cover, and water system change were chosen as the main parameters and further analysed to evaluate its impact on the North Canal water quality. The methodology involved the following steps: firstly, 30 cloudless satellite images were collected from the Landsat TM image open-source database. The visual interpretation method was used to determine different land types in a catchment area. After primary and secondary classification, 28 land cover types in total were classified. Visual interpretation method was used with the help ArcGIS for the grassland monitoring, US Landsat TM remote sensing image processing with a resolution of 30 meters was used to analyse the vegetation cover. The water system was analysed using the visual interpretation method on the GIS software platform to decode the target area, water use and coverage. Monthly measurements of water temperature, pH, BOD, COD, ammonia nitrogen, total nitrogen and total phosphorus in 2013 and 2017 were taken from three locations of the North Canal in Tongzhou district. These parameters were used for water quality index calculation and compared to land-use changes. The results of this research were promising. The vegetation coverage of North Canal riparian zone in 2017 was higher than the vegetation coverage in 2013. The surface brightness temperature value was positively correlated with the vegetation coverage density and the distance from the surface of the water bodies. This indicates that the vegetation coverage and water system have a great effect on temperature regulation and urban heat island effect. Surface temperature in 2017 was higher than in 2013, indicating a global warming effect. The water volume in the river area has been partially reduced, indicating the potential water scarcity risk in North Canal watershed. Between 2013 and 2017, urban residential, industrial and mining storage land areas significantly increased compared to other land use types; however, water quality has significantly improved in 2017 compared to 2013. This observation indicates that the Tongzhou Water Restoration Plan showed positive results and water management of Tongzhou district had been improved.

Keywords: North Canal, land use, riparian vegetation, river ecology, remote sensing

Procedia PDF Downloads 113

Authors: Juhi Faridi, Mohd. Ajmal Kafeel

Abstract:

The advancements in the field of Artificial Intelligence (AI) and technology has led to an evolution of an intelligent era. Neural networks, having the computational power and learning ability similar to the brain is one of the key AI technologies. Neuromorphic computing system (NCS) consists of the synaptic device, neuronal circuit, and neuromorphic architecture. Memristor are a promising candidate for neuromorphic computing systems, but when it comes to neuromorphic computing, the conductance behavior of the synaptic memristor or neuronal memristor needs to be studied thoroughly in order to fathom the neuroscience or computer science. Furthermore, there is a need of more simulation work for utilizing the existing device properties and providing guidance to the development of future devices for different performance requirements. Hence, development of NCS needs more simulation work to make use of existing device properties. This work aims to provide an insight to build neuronal circuits using memristors to achieve a Memristor based NCS.  Here we throw a light on the research conducted in the field of memristors for building analog and digital circuits in order to motivate the research in the field of NCS by building memristor based neural circuits for advanced AI applications. This literature is a step in the direction where we describe the various Key findings about memristors and its analog and digital circuits implemented over the years which can be further utilized in implementing the neuronal circuits in the NCS. This work aims to help the electronic circuit designers to understand how the research progressed in memristors and how these findings can be used in implementing the neuronal circuits meant for the recent progress in the NCS.

Keywords: analog circuits, digital circuits, memristors, neuromorphic computing systems

Procedia PDF Downloads 174

Authors: Preeti Verma, P. Chellapandi, N.C. Santhi Srinivas, Vakil Singh

Abstract:

Modified 9Cr-1Mo steel is widely used for structural components like heat exchangers, pressure vessels and steam generator in the nuclear reactors. It is also found to be a candidate material for future metallic fuel sodium cooled fast breeder reactor because of its high thermal conductivity, lower thermal expansion coefficient, micro structural stability, high irradiation void swelling resistance and higher resistance to stress corrosion cracking in water-steam systems compared to austenitic stainless steels. The components of steam generators that operate at elevated temperatures are often subjected to repeated thermal stresses as a result of temperature gradients which occur on heating and cooling during start-ups and shutdowns or during variations in operating conditions of a reactor. These transient thermal stresses give rise to LCF damage. In the present investigation strain controlled low cycle fatigue tests were conducted at room temperature and 300 °C in normalized and tempered condition using total strain amplitudes in the range from ±0.25% to ±0.5% at strain rate of 10-2 s-1. Cyclic Stress response at high strain amplitudes (±0.31% to ±0.5%) showed initial softening followed by hardening upto a few cycles and subsequent softening till failure. The extent of softening increased with increase in strain amplitude and temperature. Depends on the strain amplitude of the test the stress strain hysteresis loops displayed Masing behaviour at higher strain amplitudes and non-Masing at lower strain amplitudes at both the temperatures. It is quite opposite to the usual Masing and Non-Masing behaviour reported earlier for different materials. Low cycle fatigue damage was evaluated in terms of plastic strain and plastic strain energy approach at room temperature and 300 °C. It was observed that the plastic strain energy approach was found to be more closely matches with the experimental fatigue lives particularly, at 300 °C where dynamic strain aging was observed.

Keywords: Modified 9Cr-mo steel, low cycle fatigue, Masing behavior, cyclic softening

Procedia PDF Downloads 443

Authors: F. A. Olutoge, O.R. Olulope, M. O. Odelola

Abstract:

This study investigates the strength potentials of groundnut husk ash as partial cement replacement in mortar and also develops a predictive model using Artificial Neural Network. Groundnut husks sourced from Ogbomoso, Nigeria, was sun dried, calcined to ash in a furnace at a controlled temperature of 600⁰ C for a period of 6 hours, and sieved through the 75 microns. The ash was subjected to chemical analysis and setting time test. Fine aggregate (sand) for the mortar was sourced from Ado Ekiti, Nigeria. The cement: GHA constituents were blended in ratios 100:0, 95:5, 90:10, 85:15 and 80:20 %. The sum of SiO₂, Al₂O₃, and Fe₂O₃ content in GHA is 26.98%. The compressive strength for mortars PC, GHA5, GHA10, GHA15, and GHA20 ranged from 6.3-10.2 N/mm² at 7days, 7.5-12.3 N/mm² at 14 days, 9.31-13.7 N/mm² at 28 days, 10.4-16.7 N/mm² at 56days and 13.35- 22.3 N/mm² at 90 days respectively, PC, GHA5 and GHA10 had competitive values up to 28 days, but GHA10 gave the highest values at 56 and 90 days while GHA20 had the lowest values at all ages due to dilution effect. Flexural strengths values at 28 days ranged from 1.08 to 1.87 N/mm² and increased to a range of 1.53-4.10 N/mm² at 90 days. The ANN model gave good prediction for compressive strength of the mortars. This study has shown that groundnut husk ash as partial cement replacement improves the strength properties of mortar.

Keywords: compressive strength, groundnut husk ash, mortar, pozzolanic index

Procedia PDF Downloads 154

Authors: Chayonnat Thanamun, Kreangkrai Maneeintr

Abstract:

For petroleum industry, polymer flooding is the one of the main methods in enhanced oil recovery (EOR) that is used water-soluble polymer such as partially hydrolyzed polyacrylamide (HPAM) to increase oil production. It is added to the flooding water to improve the mobility ratio in the flooding process. During the polymer flooding process, water is produced as a by-product along with oil and gas production. This produced water is a mixture of inorganic and organic compound. Moreover, produced water is more difficult to treat than that from water flooding. In this work, the effect of HPAM residue on the wastewater treatment from polymer flooding is studied. Polyaluminium chloride (PAC) is selected to use as a flocculant. Therefore, the objective of this study is to evaluate the effect of polymer residues in produced water on the wastewater treatment by using PAC. The operating parameters of this study are flocculant dosage ranging from 300,400 and 500 mg/L temperature from 30-50 Celsius degree and HPAM concentrations from 500, 1000 and 2000 mg/L. Furthermore, the turbidity, as well as total suspended solids (TSS), are also studied. The results indicated that with an increase in HPAM concentration, the TSS and turbidity increase gradually with the increasing of coagulant dosage under the same temperature. Also, the coagulation-flocculation performance is improved with the increasing temperature. This can be applied to use in the wastewater treatment from oil production before this water can be injected back to the reservoir.

Keywords: wastewater treatment, petroleum production, polyaluminium chloride, polyacrylamide

Procedia PDF Downloads 153

Authors: Djeddi Khaled, Houssou Hind, Miloudi Abdellatif, Rabah Siham

Abstract:

In the field of veterinary medicine, there is a growing application of artificial intelligence (AI) for diagnosing bovine mastitis, a prevalent inflammatory disease in dairy cattle. AI technologies, such as automated milking systems, have streamlined the assessment of key metrics crucial for managing cow health during milking and identifying prevalent diseases, including mastitis. These automated milking systems empower farmers to implement automatic mastitis detection by analyzing indicators like milk yield, electrical conductivity, fat, protein, lactose, blood content in the milk, and milk flow rate. Furthermore, reports highlight the integration of somatic cell count (SCC), thermal infrared thermography, and diverse systems utilizing statistical models and machine learning techniques, including artificial neural networks, to enhance the overall efficiency and accuracy of mastitis detection. According to a review of 15 publications, machine learning technology can predict the risk and detect mastitis in cattle with an accuracy ranging from 87.62% to 98.10% and sensitivity and specificity ranging from 84.62% to 99.4% and 81.25% to 98.8%, respectively. Additionally, machine learning algorithms and microarray meta-analysis are utilized to identify mastitis genes in dairy cattle, providing insights into the underlying functional modules of mastitis disease. Moreover, AI applications can assist in developing predictive models that anticipate the likelihood of mastitis outbreaks based on factors such as environmental conditions, herd management practices, and animal health history. This proactive approach supports farmers in implementing preventive measures and optimizing herd health. By harnessing the power of artificial intelligence, the diagnosis of bovine mastitis can be significantly improved, enabling more effective management strategies and ultimately enhancing the health and productivity of dairy cattle. The integration of artificial intelligence presents valuable opportunities for the precise and early detection of mastitis, providing substantial benefits to the dairy industry.

Keywords: artificial insemination, automatic milking system, cattle, machine learning, mastitis

Procedia PDF Downloads 65

Authors: Pascal François Faye, Baye Mor Sall, Bineta Dembele, Jeanne Ana Awa Faye

Abstract:

In agriculture, estimating crop yields is key to improving productivity and decision-making processes such as financial market forecasting and addressing food security issues. The main objective of this paper is to have tools to predict and improve the accuracy of crop yield forecasts using machine learning (ML) algorithms such as CART , KNN and SVM . We developed a mobile app and a web app that uses these algorithms for practical use by farmers. The tests show that our system (collection and deployment architecture, web application and mobile application) is operational and validates empirical knowledge on agro-climatic parameters in addition to proactive decision-making support. The experimental results obtained on the agricultural data, the performance of the ML algorithms are compared using cross-validation in order to identify the most effective ones following the agricultural data. The proposed applications demonstrate that the proposed approach is effective in predicting crop yields and provides timely and accurate responses to farmers for decision support.

Keywords: prediction, machine learning, artificial intelligence, digital agriculture

Procedia PDF Downloads 80

Authors: Qitao Xie, Qingquan Zhang, Xiaofei Zhang, Di Tian, Ruixuan Wen, Ting Zhu, Ping Yi, Xin Li

Abstract:

Inspired by the recent movement of digital currency, we are building a question answering system concerning the subject of cryptocurrency using Bidirectional Encoder Representations from Transformers (BERT). The motivation behind this work is to properly assist digital currency investors by directing them to the corresponding knowledge bases that can offer them help and increase the querying speed. BERT, one of newest language models in natural language processing, was investigated to improve the quality of generated responses. We studied different combinations of hyperparameters of the BERT model to obtain the best fit responses. Further, we created an intelligent chatbot for cryptocurrency using BERT. A chatbot using BERT shows great potential for the further advancement of a cryptocurrency market tool. We show that the BERT neural networks generalize well to other tasks by applying it successfully to cryptocurrency.

Keywords: bidirectional encoder representations from transformers, BERT, chatbot, cryptocurrency, deep learning

Procedia PDF Downloads 147

Authors: Vijay Pal Singh, Yogesh Chandra Kotiyal

Abstract:

Structures are a combination of various load carrying members which transfer the loads to the foundation from the superstructure safely. At the design stage, the loading of the structure is defined and appropriate material choices are made based upon their properties, mainly related to strength. The strength of materials kept on reducing with time because of many factors like environmental exposure and deformation caused by unpredictable external loads. Hence, to predict the strength of materials used in structures, various techniques are used. Among these techniques, Non-Destructive Techniques (NDT) are the one that can be used to predict the strength without damaging the structure. In the present study, the compressive strength of concrete has been predicted using Artificial Neural Network (ANN). The predicted strength was compared with the experimentally obtained actual compressive strength of concrete and equations were developed for different models. A good co-relation has been obtained between the predicted strength by these models and experimental values. Further, the co-relation has been developed using two NDT techniques for prediction of strength by regression analysis. It was found that the percentage error has been reduced between the predicted strength by using combined techniques in place of single techniques.

Keywords: rebound, ultra-sonic pulse, penetration, ANN, NDT, regression

Procedia PDF Downloads 428

Authors: Hillary Onyeka Abugu, Valentine Chinakwugwo Ezea, Janefrances Ngozi Ihedioha, Nwachukwu Romanus Ekere

Abstract:

The study assessed the spring water quality in Igbo-Etiti, Nigeria, for drinking and irrigation application using Physico-chemical parameters, water quality index, mineral and trace elements, pollution indices and risk assessment. Standard methods were used to determine the physicochemical properties of the spring water in rainy and dry seasons. Trace metals such as Pb, Cd, Zn and Cu were determined with atomic absorption spectrophotometer. The results showed that most of the physicochemical properties studied were within the guideline values set by Nigeria Standard for Drinking Water Quality (NSDWQ), WHO and US EPA for drinking water purposes. However, pH of all the spring water (4.27- 4.73; and 4.95- 5.73), lead (Pb) (0.01-1.08 mg/L) and cadmium (Cd) (0.01-0.15 mg/L) concentrations were above the guideline values in both seasons. This could be attributed to the lithography of the study area, which is the Nsukka formation. Leaching of lead and sulphides from the embedded coal deposits could have led to the increased lead levels and made the water acidic. Two-way ANOVA showed significant differences in most of the parameters studied in dry and rainy seasons. Pearson correlation analysis and cluster analysis showed strong significant positive and negative correlations in some of the parameters studied in both seasons. The water quality index showed that none of the spring water had excellent water status. However, one spring (Iyi Ase) had poor water status in dry season and is considered unsafe for drinking. Iyi Ase was also considered not suitable for irrigation application as predicted by most of the pollution indices, while others were generally considered suitable for irrigation application. Probable cancer and non-cancer risk assessment revealed a probable risk associated with the consumption of the spring in the Igbo-Ettiti area, Nigeria.

Keywords: water quality, pollution index, risk assessment, physico-chemical parameters

Procedia PDF Downloads 167

Authors: A. Majeed, P. Ali

Abstract:

Before designing an electrical system, the estimation of load is necessary for unit sizing and demand-generation balancing. The system could be a stand-alone system for a village or grid connected or integrated renewable energy to grid connection, especially as there are non–electrified villages in developing countries. In the classical model, the energy demand was found by estimating the household appliances multiplied with the amount of their rating and the duration of their operation, but in this paper, information exists for electrified villages could be used to predict the demand, as villages almost have the same life style. This paper describes a method used to predict the average energy consumed in each two months for every consumer living in a village by Artificial Neural Network (ANN). The input data are collected using a regional survey for samples of consumers representing typical types of different living, household appliances and energy consumption by a list of information, and the output data are collected from administration office of Piramagrun for each corresponding consumer. The result of this study shows that the average demand for different consumers from four villages in different months throughout the year is approximately 12 kWh/day, this model estimates the average demand/day for every consumer with a mean absolute percent error of 11.8%, and MathWorks software package MATLAB version 7.6.0 that contains and facilitate Neural Network Toolbox was used.

Keywords: artificial neural network, load estimation, regional survey, rural electrification

Procedia PDF Downloads 123

Authors: Eman M. El-Sayed, Sahar A. Abdelaziz, Maha M. Saber Abd El Latif

Abstract:

Neural tube defects (NTD) are important causes of infant mortality. Poor nutrition was essential factor for central nervous system deformation. Mothers gave NTD offspring had abnormal serum levels of micronutrients. The present research was designed to study the effect of maternal micronutrient levels and oxidative stress on the incidence of NTD in offspring. The study included forty mothers; twenty of them of 30.9+7.28 years had conceived fetuses with NTD were considered as cases; and twenty mothers of 28.2 + 7.82 years with healthy neonates. We determined serum vitamin B12 and folic acid by using radioimmunoassays. Also, serum zinc was assessed using atomic absorption spectrophotometry. While serum copper and iron were measured colorimetrically and serum ceruloplasmin was analyzed by radialimmunodiffusion. Cases showed significantly lower levels of folic acid, vitamin B12 and zinc (P< 0.0005, 0.01, 0.01 respectively) than that of the control. Concentrations of copper, ceruloplasmin, and iron were markedly increased in cases as compared to controls (P < 0.01, 0.01, and 0.05 respectively). In conclusion, the current study clearly indicated the etiology of NTD cannot be explained with one strict etiologic mechanism, on the contrary, an interaction among maternal nutritional factors and oxidative stress would explain these anomalies. Vitamin B12, folic acid, and zinc supplementations should be considered for further decrease in the occurrence of NTD. Preventing excess iron during pregnancy favors better pregnancy outcomes.

Keywords: ceruloplasmin, copper, folic acid, iron, neural tube defects, oxidative stress, vitamin b12, zinc

Procedia PDF Downloads 221

Authors: Christina Adly, Meena Abdelmeseeh, Tamer Basha

Abstract:

This paper proposes a system for hand movement recognition using multichannel surface EMG(sEMG) signals obtained from 40 subjects using 40 different exercises, which are available on the Ninapro(Non-Invasive Adaptive Prosthetics) database. First, we applied processing methods to the raw sEMG signals to convert them to their amplitudes. Second, we used deep learning methods to solve our problem by passing the preprocessed signals to Fully connected neural networks(FCNN) and recurrent neural networks(RNN) with Long Short Term Memory(LSTM). Using intrasubject evaluation, The accuracy using the FCNN is 72%, with a processing time for training around 76 minutes, and for RNN's accuracy is 79.9%, with 8 minutes and 22 seconds processing time. Third, we applied some postprocessing methods to improve the accuracy, like majority voting(MV) and Movement Error Rate(MER). The accuracy after applying MV is 75% and 86% for FCNN and RNN, respectively. The MER value has an inverse relationship with the prediction delay while varying the window length for measuring the MV. The different part uses the RNN with the intersubject evaluation. The experimental results showed that to get a good accuracy for testing with reasonable processing time, we should use around 20 subjects.

Keywords: hand movement recognition, recurrent neural network, movement error rate, intrasubject evaluation, intersubject evaluation

Procedia PDF Downloads 142

Authors: Bryan Crompton, Daniel Giger, Tanay Mehta, Apurva Mody

Abstract:

The task of classifying radio frequency (RF) signals has seen recent success in employing deep neural network models. In this work, we present a combined signal processing and machine learning approach to signal classification for cochannel anomalous signals. The power spectral density and cyclostationary signal processing features of a captured signal are computed and fed into a neural net to produce a classification decision. Our combined signal preprocessing and machine learning approach allows for simpler neural networks with fast training times and small computational resource requirements for inference with longer preprocessing time.

Keywords: signal processing, machine learning, cyclostationary signal processing, signal classification

Procedia PDF Downloads 107

Authors: M. S. Gadala, Khaled Ahmed, Elasadig Mahdi

Abstract:

In this paper, we introduced a gradient-based inverse solver to obtain the missing boundary conditions based on the readings of internal thermocouples. The results show that the method is very sensitive to measurement errors, and becomes unstable when small time steps are used. The artificial neural networks are shown to be capable of capturing the whole thermal history on the run-out table, but are not very effective in restoring the detailed behavior of the boundary conditions. Also, they behave poorly in nonlinear cases and where the boundary condition profile is different. GA and PSO are more effective in finding a detailed representation of the time-varying boundary conditions, as well as in nonlinear cases. However, their convergence takes longer. A variation of the basic PSO, called CRPSO, showed the best performance among the three versions. Also, PSO proved to be effective in handling noisy data, especially when its performance parameters were tuned. An increase in the self-confidence parameter was also found to be effective, as it increased the global search capabilities of the algorithm. RPSO was the most effective variation in dealing with noise, closely followed by CRPSO. The latter variation is recommended for inverse heat conduction problems, as it combines the efficiency and effectiveness required by these problems.

Keywords: inverse analysis, function specification, neural net works, particle swarm, run-out table

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Authors: H. M. N. L. Handagiripathira

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The distributions of naturally occurring and anthropogenic radioactive materials were determined in surface sediments taken at 27 different locations along the bank of Negombo Lagoon in Sri Lanka. Hydrographic parameters of lagoon water and the grain size analyses of the sediment samples were also carried out for this study. The conductivity of the adjacent water was varied from 13.6 mS/cm to 55.4 mS/cm near to the southern end and the northern end of the lagoon, respectively, and equally salinity levels varied from 7.2 psu to 32.1 psu. The average pH in the water was 7.6 and average water temperature was 28.7 °C. The grain size analysis emphasized the mass fractions of the samples as sand (60.9%), fine sand (30.6%) and fine silt+clay (1.3%) in the sampling locations. The surface sediment samples of wet weight, 1 kg each from upper 5-10 cm layer, were oven dried at 105 °C for 24 hours to get a constant weight, homogenized and sieved through a 2 mm sieve (IAEA technical series no. 295). The radioactivity concentrations were determined using gamma spectrometry technique. Ultra Low Background Broad Energy High Purity Ge Detector, BEGe (Model BE5030, Canberra) was used for radioactivity measurement with Canberra Industries' Laboratory Source-less Calibration Software (LabSOCS) mathematical efficiency calibration approach and Geometry composer software. The mean activity concentration was found to be 24 ± 4, 67 ± 9, 181 ± 10, 59 ± 8, 3.5 ± 0.4 and 0.47 ± 0.08 Bq/kg for 238U, 232Th, 40K, 210Pb, 235U and 137Cs respectively. The mean absorbed dose rate in air, radium equivalent activity, external hazard index, annual gonadal dose equivalent and annual effective dose equivalent were 60.8 nGy/h, 137.3 Bq/kg, 0.4, 425.3 mSv/year and 74.6 mSv/year, respectively. The results of this study will provide baseline information on the natural and artificial radioactive isotopes and environmental pollution associated with information on radiological risk.

Keywords: gamma spectrometry, lagoon, radioactivity, sediments

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Authors: Zahra Abdolkarimi, Naser Zourikalatehsamad,

Abstract:

Up to 40 years ago, after recognition of epilepsy, it was generally believed that these attacks occurred randomly and suddenly. However, thanks to the advance of mathematics and engineering, such attacks can be predicted within a few minutes or hours. In this way, various algorithms for long-term prediction of the time and frequency of the first attack are presented. In this paper, by considering the nonlinear nature of brain signals and dynamic recorded brain signals, ANFIS model is presented to predict the brain signals, since according to physiologic structure of the onset of attacks, more complex neural structures can better model the signal during attacks. Contribution of this work is the co-evolution algorithm for optimization of ANFIS network parameters. Our objective is to predict brain signals based on time series obtained from brain signals of the people suffering from epilepsy using ANFIS. Results reveal that compared to other methods, this method has less sensitivity to uncertainties such as presence of noise and interruption in recorded signals of the brain as well as more accuracy. Long-term prediction capacity of the model illustrates the usage of planted systems for warning medication and preventing brain signals.

Keywords: co-evolution algorithms, brain signals, time series, neural networks, ANFIS model, physiologic structure, time prediction, epilepsy suffering, illustrates model

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Authors: K. Benyounes, A. Benmounah

Abstract:

Cement-based grouts has been used successfully to repair cracks in many concrete structures such as bridges, tunnels, buildings and to consolidate soils or rock foundations. In the present study, the rheological characterization of cement grout with water/binder ratio (W/B) is fixed at 0.5. The effect of the replacement of cement by bentonite (2 to 10 % wt) in presence of superplasticizer (0.5 % wt) was investigated. Several rheological tests were carried out by using controlled-stress rheometer equipped with vane geometry in temperature of 20°C. To highlight the influence of bentonite and superplasticizer on the rheological behavior of grout cement, various flow tests in a range of shear rate from 0 to 200 s-1 were observed. Cement grout showed a non-Newtonian viscosity behavior at all concentrations of bentonite. Three parameter model Herschel-Bulkley was chosen for fitting of experimental data. Based on the values of correlation coefficients of the estimated parameters, The Herschel-Bulkley law model well described the rheological behavior of the grouts. Test results showed that the dosage of bentonite increases the viscosity and yield stress of the system and introduces more thixotropy. While the addition of both bentonite and superplasticizer with cement grout improve significantly the fluidity and reduced the yield stress due to the action of dispersion of SP.

Keywords: rheology, cement grout, bentonite, superplasticizer, viscosity, yield stress

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Authors: Ahmed M. Al-Mahbashi, Mosleh A. Al-Shamrani, Muawia Dafalla

Abstract:

Mixtures of sand and clay are frequently used to serve for specific purposes in several engineering practices. In environmental engineering, liner layers and cover layers are common for controlling waste disposal facilities. These layers are exposed to moisture and temperature fluctuation specially when existing in unsaturated condition. The relationship between soil suction and water content for these materials is essential for understanding their unsaturated behavior and properties such as retention capacity and unsaturated follow (hydraulic conductivity). This study is aimed at investigating retention capacity for two sand-natural expansive clay mixtures (15% (C15) and 30% (C30) expansive clay) at two ambient temperatures within the range of 5 -50 °C. Soil water retention curves (SWRC) for these materials were determined at these two ambient temperatures using different salt solutions for a wide range of suction (up to 200MPa). The results indicate that retention capacity of C15 mixture underwent significant changes due to temperature variations. This effect tends to be less visible when the clay fraction is doubled (C30). In addition, the overall volume change is marginally affected by high temperature within the range considered in this study.

Keywords: soil water retention curve, sand-expansive clay liner, suction, temperature

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Authors: Shahrzad Zolfagharnassab, Abdul Rashid Mohamed Shariff, Siti Khairunniza Bejo

Abstract:

Conventional methods for nutrient soil mapping are based on laboratory tests of samples that are obtained from surveys. The time and cost involved in gathering and analyzing soil samples are the reasons that researchers use Predictive Soil Mapping (PSM). PSM can be defined as the development of a numerical or statistical model of the relationship among environmental variables and soil properties, which is then applied to a geographic database to create a predictive map. Kriging is a group of geostatistical techniques to spatially interpolate point values at an unobserved location from observations of values at nearby locations. The main problem with using kriging as an interpolator is that it is excessively data-dependent and requires a large number of closely spaced data points. Hence, there is a need to minimize the number of data points without sacrificing the accuracy of the results. In this paper, an Artificial Neural Networks (ANN) scheme was used to predict macronutrient values at un-sampled points. ANN has become a popular tool for prediction as it eliminates certain difficulties in soil property prediction, such as non-linear relationships and non-normality. Back-propagation multilayer feed-forward network structures were used to predict nitrogen, phosphorous and potassium values in the soil of the study area. A limited number of samples were used in the training, validation and testing phases of ANN (pattern reconstruction structures) to classify soil properties and the trained network was used for prediction. The soil analysis results of samples collected from the soil survey of block C of Sawah Sempadan, Tanjung Karang rice irrigation project at Selangor of Malaysia were used. Soil maps were produced by the Kriging method using 236 samples (or values) that were a combination of actual values (obtained from real samples) and virtual values (neural network predicted values). For each macronutrient element, three types of maps were generated with 118 actual and 118 virtual values, 59 actual and 177 virtual values, and 30 actual and 206 virtual values, respectively. To evaluate the performance of the proposed method, for each macronutrient element, a base map using 236 actual samples and test maps using 118, 59 and 30 actual samples respectively produced by the Kriging method. A set of parameters was defined to measure the similarity of the maps that were generated with the proposed method, termed the sample reduction method. The results show that the maps that were generated through the sample reduction method were more accurate than the corresponding base maps produced through a smaller number of real samples. For example, nitrogen maps that were produced from 118, 59 and 30 real samples have 78%, 62%, 41% similarity, respectively with the base map (236 samples) and the sample reduction method increased similarity to 87%, 77%, 71%, respectively. Hence, this method can reduce the number of real samples and substitute ANN predictive samples to achieve the specified level of accuracy.

Keywords: artificial neural network, kriging, macro nutrient, pattern recognition, precision farming, soil mapping

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Authors: Mimi Roy, Sriroop Chaudhuri

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Sustainable water supply is among the basic needs for human development, especially in the rural areas of the developing nations where safe water supply and basic sanitation infrastructure is direly needed. In light of the above, we propose a simple methodology to develop a composite water sustainability index (WSI) to assess the collective performance of the existing rural water supply services (RWSS) in India over time. The WSI will be computed by summarizing the details of all the different varieties of water supply schemes presently available in India comprising of 40 liters per capita per day (lpcd), 55 lpcd, and piped water supply (PWS) per household. The WSI will be computed annually, between 2010 and 2016, to elucidate changes in holistic RWSS performances. Results will be integrated within a robust geospatial framework to identify the ‘hotspots’ (states/districts) which have persistent issues over adequate RWSS coverage and warrant spatially-optimized policy reforms in future to address sustainable human development. Dataset will be obtained from the National Rural Drinking Water Program (NRDWP), operating under the aegis of the Ministry of Drinking Water and Sanitation (MoDWS), at state/district/block levels to offer the authorities a cross-sectional view of RWSS at different levels of administrative hierarchy. Due to simplistic design, complemented by spatio-temporal cartograms, similar approaches can also be adopted in other parts of the world where RWSS need a thorough appraisal.

Keywords: rural water supply services, piped water supply, sustainability, composite index, spatial, drinking water

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