Search results for: solution parameters

Authors: L. Chiriatti, H. Hafid, H. R. Mercado-Mendoza, K. L. Apedo, C. Fond, F. Feugeas

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Substituting natural aggregate with recycled aggregate coming from concrete demolition represents a promising alternative to face the issues of both the depletion of natural resources and the congestion of waste storage facilities. However, the crushing process of concrete demolition waste, currently in use to produce recycled concrete aggregate, does not allow the complete separation of natural aggregate from a variable amount of adhered mortar. Given the physicochemical characteristics of the latter, the introduction of recycled concrete aggregate into a concrete mix modifies, to a certain extent, both fresh and hardened concrete properties. As a consequence, the behavior of recycled reinforced concrete members could likely be influenced by the specificities of recycled concrete aggregates. Beyond the mechanical properties of concrete, and as a result of the composite character of reinforced concrete, the bond characteristics at the rebar/concrete interface have to be taken into account in an attempt to describe accurately the mechanical response of recycled reinforced concrete members. Hence, a comparative experimental campaign, including 16 pull-out tests, was carried out. Four concrete mixes with different recycled concrete aggregate content were tested. The main mechanical properties (compressive strength, tensile strength, Young’s modulus) of each concrete mix were measured through standard procedures. A single 14-mm-diameter ribbed rebar, representative of the diameters commonly used in the domain of civil engineering, was embedded into a 200-mm-side concrete cube. The resulting concrete cover is intended to ensure a pull-out type failure (i.e. exceedance of the rebar/concrete interface shear strength). A pull-out test carried out on the 100% recycled concrete specimen was enriched with exploratory acoustic emission measurements. Acoustic event location was performed by means of eight piezoelectric transducers distributed over the whole surface of the specimen. The resulting map was compared to existing data related to natural aggregate concrete. Damage distribution around the reinforcement and main features of the characteristic bond stress/free-end slip curve appeared to be similar to previous results obtained through comparable studies carried out on natural aggregate concrete. This seems to show that the usual bond mechanism sequence (‘chemical adhesion’, mechanical interlocking and friction) remains unchanged despite the addition of recycled concrete aggregate. However, the results also suggest that bond efficiency seems somewhat improved through the use of recycled concrete aggregate. This observation appears to be counter-intuitive with regard to the diminution of the main concrete mechanical properties with the recycled concrete aggregate content. As a consequence, the impact of recycled concrete aggregate content on bond characteristics seemingly represents an important factor which should be taken into account and likely to be further explored in order to determine flexural parameters such as deflection or crack distribution.

Keywords: acoustic emission monitoring, high-bond steel rebar, pull-out test, recycled aggregate concrete

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Authors: Samart Porncharoen, Nisa Pakvilai

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The elevated levels of air pollutants in regional atmospheric environments is a significant problem that affects human health in Thailand, particularly in the Chao Phraya River Basin. Of concern are issues surrounding ambient air pollution such as particulate matter, gaseous pollutants and more specifically concerning air pollution along the river. Therefore, the spatio-temporal study of air pollution in this real environment can gain more accurate air quality data for making formalized environmental policy in river basins. In order to inform such a policy, a study was conducted over a period of January –December, 2015 to continually collect measurements of various pollutants in both urban and regional locations in the Chao Phraya River Basin. This study investigated the air pollutants in many diverse environments along the Chao Phraya River Basin, Thailand in 2015. Multivariate Analysis Techniques such as Principle Component Analysis (PCA) and Path analysis were utilised to classify air pollution in the surveyed location. Measurements were collected in both urban and rural areas to see if significant differences existed between the two locations in terms of air pollution levels. The meteorological parameters of various particulates were collected continually from a Thai pollution control department monitoring station over a period of January –December, 2015. Of interest to this study were the readings of SO2, CO, NOx, O3, and PM10. Results showed a daily arithmetic mean concentration of SO2, CO, NOx, O3, PM10 reading at 3±1 ppb, 0.5± 0.5 ppm, 30±21 ppb, 19±16 ppb, and 40±20 ug/m3 in urban locations (Bangkok). During the same time period, the readings for the same measurements in rural areas, Ayutthaya (were 1±0.5 ppb, 0.1± 0.05 ppm, 25±17 ppb, 30±21 ppb, and 35±10 ug/m3respectively. This show that Bangkok were located in highly polluted environments that are dominated source emitted from vehicles. Further, results were analysed to ascertain if significant seasonal variation existed in the measurements. It was found that levels of both gaseous pollutants and particle matter in dry season were higher than the wet season. More broadly, the results show that levels of pollutants were measured highest in locations along the Chao Phraya. River Basin known to have a large number of vehicles and biomass burning. This correlation suggests that the principle pollutants were from these anthropogenic sources. This study contributes to the body of knowledge surrounding ambient air pollution such as particulate matter, gaseous pollutants and more specifically concerning air pollution along the Chao Phraya River Basin. Further, this study is one of the first to utilise continuous mobile monitoring along a river in order to gain accurate measurements during a data collection period. Overall, the results of this study can be used for making formalized environmental policy in river basins in order to reduce the physical effects on human health.

Keywords: air pollution, Chao Phraya river basin, meteorology, seasonal variation, principal component analysis

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Authors: Anna Słupik, Krzysztof Jaworski, Anna Mosiołek, Dariusz Białoszewski

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Background: Maintaining the correct body balance is essential in the prevention of falls in the elderly, which is especially important for women because of postmenopausal osteoporosis and the serious consequences of falls. One of the exercise methods which is very popular among adults, and which may affect body balance in a positive way is the pilates method. The aim of the study was to evaluate the effect of regular pilates exercises on the ability to maintain body balance in static conditions in adult healthy women. Material and methods: The study group consisted of 20 healthy women attending pilates twice a week for at least 1 year. The control group consisted of 20 healthy women physically inactive. Women in the age range from 35 to 50 years old without pain in musculoskeletal system or other pain were only qualified to the groups. Body balance was assessed using MatScan VersaTek platform with Sway Analysis Module based on Matscan Clinical 6.7 software. The balance was evaluated under the following conditions: standing on both feet with eyes open, standing on both feet with eyes closed, one-leg standing (separately on the right and left foot) with eyes open. Each test lasted 30 seconds. The following parameters were calculated: estimated size of the ellipse of 95% confidence, the distance covered by the Center of Gravity (COG), the size of the maximum shift in the sagittal and frontal planes and load distribution between the left and right foot, as well as between rear- and forefoot. Results: It was found that there is significant difference between the groups in favor of the study group in the size of the confidence ellipse and maximum shifts of COG in the sagittal plane during standing on both feet, both with the eyes open and closed (p < 0.05). While standing on one leg both on the right and left leg, with eyes opened there was a significant difference in favor of the study group, in terms of the size of confidence ellipse, the size of the maximum shifts in the sagittal and in the frontal plane (p < 0.05). There were no differences between the distribution of load between the right and left foot (standing with both feet), nor between fore- and rear foot (in standing with both feet or one-leg). Conclusions: 1. Static balance in women exercising regularly by pilates method is better than in inactive women, which may in the future prevent falls and their consequences. 2. The observed differences in maintaining balance in frontal plane in one-leg standing may indicate a positive impact of pilates exercises on the ability to maintain global balance in terms of the reduced support surface. 3. Pilates method can be used as a form preventive therapy for all people who are expected to have problems with body balance in the future, for example in chronic neurological disorders or vestibular problems. 4. The results have shown that further prospective randomized research on a larger and more representative group is needed.

Keywords: balance exercises, body balance, pilates, pressure distribution, women

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Authors: Aliasghar Mehdizadeh Dastjerdi, Francisco Camara Pereira

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The increasing complexity and demand of transport services strains transportation systems especially in urban areas with limited possibilities for building new infrastructure. The solution to this challenge requires changes of travel behavior. One of the proposed means to induce such change is multimodal travel apps. This paper describes a study of the intention to use a real-time multi-modal travel app aimed at motivating travel behavior change in the Greater Copenhagen Region (Denmark) toward promoting sustainable transport options. The proposed app is a multi-faceted smartphone app including both travel information and persuasive strategies such as health and environmental feedback, tailoring travel options, self-monitoring, tunneling users toward green behavior, social networking, nudging and gamification elements. The prospective for mobility management travel apps to stimulate sustainable mobility rests not only on the original and proper employment of the behavior change strategies, but also on explicitly anchoring it on established theoretical constructs from behavioral theories. The theoretical foundation is important because it positively and significantly influences the effectiveness of the system. However, there is a gap in current knowledge regarding the study of mobility-management travel app with support in behavioral theories, which should be explored further. This study addresses this gap by a social cognitive theory‐based examination. However, compare to conventional method in technology adoption research, this study adopts a reverse approach in which the associations between theoretical constructs are explored by Max-Min Hill-Climbing (MMHC) algorithm as a hybrid causal discovery method. A technology-use preference survey was designed to collect data. The survey elicited different groups of variables including (1) three groups of user’s motives for using the app including gain motives (e.g., saving travel time and cost), hedonic motives (e.g., enjoyment) and normative motives (e.g., less travel-related CO2 production), (2) technology-related self-concepts (i.e. technophile attitude) and (3) use Intention of the travel app. The questionnaire items led to the formulation of causal relationships discovery to learn the causal structure of the data. Causal relationships discovery from observational data is a critical challenge and it has applications in different research fields. The estimated causal structure shows that the two constructs of gain motives and technophilia have a causal effect on adoption intention. Likewise, there is a causal relationship from technophilia to both gain and hedonic motives. In line with the findings of the prior studies, it highlights the importance of functional value of the travel app as well as technology self-concept as two important variables for adoption intention. Furthermore, the results indicate the effect of technophile attitude on developing gain and hedonic motives. The causal structure shows hierarchical associations between the three groups of user’s motive. They can be explained by “frustration-regression” principle according to Alderfer's ERG (Existence, Relatedness and Growth) theory of needs meaning that a higher level need remains unfulfilled, a person may regress to lower level needs that appear easier to satisfy. To conclude, this study shows the capability of causal discovery methods to learn the causal structure of theoretical model, and accordingly interpret established associations.

Keywords: travel app, behavior change, persuasive technology, travel information, causality

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Authors: Camille Aouinaït, Danilo Christen, Christoph Carlen

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Introduction: Small and Medium-sized Enterprises (SMEs) are facing different challenges such as pressures on environmental resources, the rise of downstream power, and trade liberalization. Remaining competitive by implementing innovations and engaging in collaborations could be a strategic solution. In Switzerland, the Federal Institute for Research in Agriculture (Agroscope), the Federal schools of technology (EPFL and ETHZ), Cantonal universities and Universities of Applied Sciences (UAS) can provide substantial inputs. UAS were developed with specific missions to match the labor markets and society needs. Research projects produce patents, publications and improved networks of scientific expertise. The study’s goal is to measure the contribution of UAS and research organization to innovation and the impact of collaborations with partners in the non-academic environment in Swiss traditional fruit production. Materials and methods: The European projects Traditional Food Network to improve the transfer of knowledge for innovation (TRAFOON) and Social Impact Assessment of Productive Interactions between science and society (SIAMPI) frame the present study. The former aims to fill the gap between the needs of traditional food producing SMEs and innovations implemented following European projects. The latter developed a method to assess the impacts of scientific research. On one side, interviews with market players have been performed to make an inventory of needs of Swiss SMEs producing apricots and berries. The participative method allowed matching the current needs and the existing innovations coming from past European projects. Swiss stakeholders (e.g. producers, retailers, an inter-branch organization of fruits and vegetables) directly rated the needs on a five-Likert scale. To transfer the knowledge to SMEs, training workshops have been organized for apricot and berries actors separately, on specific topics. On the other hand, a mapping of a social network is drawn to characterize the links between actors, with a focus on the Swiss canton of Valais and UAS Valais Wallis. Type and frequency of interactions among actors have identified thanks to interviews. Preliminary results: A list of 369 SMEs needs grouped in 22 categories was produced with 37 fulfilled questionnaires. Swiss stakeholders rated 31 needs very important. Training workshops on apricot are focusing on varietal innovations, storage, disease (bacterial blight), pest (Drosophila suzukii), sorting and rootstocks. Entrepreneurship was targeted through trademark discussions in berry production. The UAS Valais Wallis collaborated on a few projects with Agroscope along with industries, at European and national levels. Political and public bodies interfere with the central area of agricultural vulgarization that induces close relationships between the research and the practical side. Conclusions: The needs identified by Swiss stakeholders are becoming part of training workshops to incentivize innovations. The UAS Valais Wallis takes part in collaboration projects with the research environment and market players that bring innovations helping SMEs in their contextual environment. Then, a Strategic Research and Innovation Agenda will be created in order to pursue research and answer the issues facing by SMEs.

Keywords: agriculture, innovation, knowledge transfer, university and research collaboration

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Authors: Evgeniya Orlova, Dmitriy Feoktistov, Geniy Kuznetsov

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Spreading of a droplet over a solid substrate is a key phenomenon observed in the following engineering applications: thin film coating, oil extraction, inkjet printing, and spray cooling of heated surfaces. Droplet cooling systems are known to be more effective than film or rivulet cooling systems. It is caused by the greater evaporation surface area of droplets compared with the film of the same mass and wetting surface. And the greater surface area of droplets is connected with the curvature of the interface. Location of the droplets on the cooling surface influences on the heat transfer conditions. The close distance between the droplets provides intensive heat removal, but there is a possibility of their coalescence in the liquid film. The long distance leads to overheating of the local areas of the cooling surface and the occurrence of thermal stresses. To control the location of droplets is possible by changing the roughness, structure and chemical composition of the surface. Thus, control of spreading can be implemented. The most important characteristic of spreading of droplets on solid surfaces is a dynamic contact angle, which is a function of the contact line speed or capillary number. However, there is currently no universal equation, which would describe the relationship between these parameters. This paper presents the results of the experimental studies of water droplet spreading on metal substrates with different surface roughness. The effect of the droplet growth rate and the surface roughness on spreading characteristics was studied at low capillary numbers. The shadow method using high speed video cameras recording up to 10,000 frames per seconds was implemented. A droplet profile was analyzed by Axisymmetric Drop Shape Analyses techniques. According to change of the dynamic contact angle and the contact line speed three sequential spreading stages were observed: rapid increase in the dynamic contact angle; monotonous decrease in the contact angle and the contact line speed; and form of the equilibrium contact angle at constant contact line. At low droplet growth rate, the dynamic contact angle of the droplet spreading on the surfaces with the maximum roughness is found to increase throughout the spreading time. It is due to the fact that the friction force on such surfaces is significantly greater than the inertia force; and the contact line is pinned on microasperities of a relief. At high droplet growth rate the contact angle decreases during the second stage even on the surfaces with the maximum roughness, as in this case, the liquid does not fill the microcavities, and the droplet moves over the “air cushion”, i.e. the interface is a liquid/gas/solid system. Also at such growth rates pulsation of liquid flow was detected; and the droplet oscillates during the spreading. Thus, obtained results allow to conclude that it is possible to control spreading by using the surface roughness and the growth rate of droplets on surfaces as varied factors. Also, the research findings may be used for analyzing heat transfer in rivulet and drop cooling systems of high energy equipment.

Keywords: contact line speed, droplet growth rate, dynamic contact angle, shadow system, spreading

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Authors: Stephen L. Green, Alexander N. Gorban, Ivan Y. Tyukin

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Within the past decade, using Convolutional Neural Networks (CNN)’s to create Deep Learning systems capable of translating Sign Language into text has been a breakthrough in breaking the communication barrier for deaf-mute people. Conventional research on this subject has been concerned with training the network to recognize the fingerspelling gestures of a given language and produce their corresponding alphanumerics. One of the problems with the current developing technology is that images are scarce, with little variations in the gestures being presented to the recognition program, often skewed towards single skin tones and hand sizes that makes a percentage of the population’s fingerspelling harder to detect. Along with this, current gesture detection programs are only trained on one finger spelling language despite there being one hundred and forty-two known variants so far. All of this presents a limitation for traditional exploitation for the state of current technologies such as CNN’s, due to their large number of required parameters. This work aims to present a technology that aims to resolve this issue by combining a pretrained legacy AI system for a generic object recognition task with a corrector method to uptrain the legacy network. This is a computationally efficient procedure that does not require large volumes of data even when covering a broad range of sign languages such as American Sign Language, British Sign Language and Chinese Sign Language (Pinyin). Implementing recent results on method concentration, namely the stochastic separation theorem, an AI system is supposed as an operate mapping an input present in the set of images u ∈ U to an output that exists in a set of predicted class labels q ∈ Q of the alphanumeric that q represents and the language it comes from. These inputs and outputs, along with the interval variables z ∈ Z represent the system’s current state which implies a mapping that assigns an element x ∈ ℝⁿ to the triple (u, z, q). As all xi are i.i.d vectors drawn from a product mean distribution, over a period of time the AI generates a large set of measurements xi called S that are grouped into two categories: the correct predictions M and the incorrect predictions Y. Once the network has made its predictions, a corrector can then be applied through centering S and Y by subtracting their means. The data is then regularized by applying the Kaiser rule to the resulting eigenmatrix and then whitened before being split into pairwise, positively correlated clusters. Each of these clusters produces a unique hyperplane and if any element x falls outside the region bounded by these lines then it is reported as an error. As a result of this methodology, a self-correcting recognition process is created that can identify fingerspelling from a variety of sign language and successfully identify the corresponding alphanumeric and what language the gesture originates from which no other neural network has been able to replicate.

Keywords: convolutional neural networks, deep learning, shallow correctors, sign language

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Authors: Moser Stephan, Lukasser Gerald, Weitlaner Robert

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Urban structures will be used more intensively in the future through redensification or new planned districts with high building densities. Especially, to achieve positive energy balances like requested for Positive Energy Districts (PED) the single use of roofs is not sufficient for dense urban areas. However, the increasing share of window significantly reduces the facade area available for use in PV generation. Through the use of PV technology at other building components, such as external venetian blinds, onsite generation can be maximized and standard functionalities of this product can be positively extended. While offering advantages in terms of infrastructure, sustainability in the use of resources and efficiency, these systems require an increased optimization in planning and control strategies of buildings. External venetian blinds with PV technology require an intelligent control concept to meet the required demands such as maximum power generation, glare prevention, high daylight autonomy, avoidance of summer overheating but also use of passive solar gains in wintertime. Today, geometric representation of outdoor spaces and at the building level, three-dimensional geometric information is available for planning with Building Information Modeling (BIM). In a research project, a web application which is called HELLA DECART was developed to provide this data structure to extract the data required for the simulation from the BIM models and to make it usable for the calculations and coupled simulations. The investigated object is uploaded as an IFC file to this web application and includes the object as well as the neighboring buildings and possible remote shading. This tool uses a ray tracing method to determine possible glare from solar reflections of a neighboring building as well as near and far shadows per window on the object. Subsequently, an annual estimate of the sunlight per window is calculated by taking weather data into account. This optimized daylight assessment per window provides the ability to calculate an estimation of the potential power generation at the integrated PV on the venetian blind but also for the daylight and solar entry. As a next step, these results of the calculations as well as all necessary parameters for the thermal simulation can be provided. The overall aim of this workflow is to advance the coordination between the BIM model and coupled building simulation with the resulting shading and daylighting system with the artificial lighting system and maximum power generation in a control system. In the research project Powershade, an AI based control concept for PV integrated façade elements with coupled simulation results is investigated. The developed automated workflow concept in this paper is tested by using an office living lab at the HELLA company.

Keywords: BIPV, building simulation, optimized control strategy, planning tool

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Authors: Hande Yavuz, Grégory Girard, Jinbo Bai

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Manufacturing of hybrid composites requires particular attention to overcome various critical weaknesses that are originated from poor interfacial compatibility. A large number of parameters have to be considered to optimize the interfacial bond strength either to avoid flaw sensitivity or delamination that occurs in composites. For this reason, surface characterization of reinforcement phase is needed in order to provide necessary data to drive an assessment of fiber-matrix interfacial compatibility prior to fabrication of composite structures. Compared to conventional plasma polymerization processes such as radiofrequency and microwave, dielectric barrier discharge assisted plasma polymerization is a promising process that can be utilized to modify the surface properties of carbon fibers in a continuous manner. Finding the most suitable conditions (e.g., plasma power, plasma duration, precursor proportion) for plasma polymerization of pyrrole in post-discharge region either in the presence or in the absence of p-toluene sulfonic acid monohydrate as well as the characterization of plasma polypyrrole coated fibers are the important aspects of this work. Throughout the current investigation, atomic force microscopy (AFM) and thermogravimetric analysis (TGA) are used to characterize plasma treated hybrid fibers (CNT-grafted Toray T700-12K carbon fibers, referred as T700/CNT). TGA results show the trend in the change of decomposition process of deposited polymer on fibers as a function of temperature up to 900 °C. Within the same period of time, all plasma pyrrole treated samples began to lose weight with relatively fast rate up to 400 °C which suggests the loss of polymeric structures. The weight loss between 300 and 600 °C is attributed to evolution of CO2 due to decomposition of functional groups (e.g. carboxyl compounds). With keeping in mind the surface chemical structure, the higher the amount of carbonyl, alcohols, and ether compounds, the lower the stability of deposited polymer. Thus, the highest weight loss is observed in 1400 W 45 s pyrrole+pTSA.H2O plasma treated sample probably because of the presence of less stable polymer than that of other plasma treated samples. Comparison of the AFM images for untreated and plasma treated samples shows that the surface topography may change on a microscopic scale. The AFM image of 1800 W 45 s treated T700/CNT fiber possesses the most significant increase in roughening compared to untreated T700/CNT fiber. Namely, the fiber surface became rougher with ~3.6 fold that of the T700/CNT fiber. The increase observed in surface roughness compared to untreated T700/CNT fiber may provide more contact points between fiber and matrix due to increased surface area. It is believed to be beneficial for their application as reinforcement in composites.

Keywords: hybrid fibers, surface characterization, surface roughness, thermal stability

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Authors: Nikola Góral, Piotr Mikołajczuk, Paweł Buczyński

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Much scientific research in the last 20 years has focused on the influence of global warming on the distribution and phenology of living organisms. Three potential responses to climate change are predicted: individual species may become extinct, adapt to new conditions in their existing range or change their range by migrating to places where climatic conditions are more favourable. It means not only migration to areas in other latitudes, but also different altitudes. In the case of dragonflies (Odonata), monitoring in Western Europe has shown that in response to global warming, dragonflies tend to change their range to a more northern one. The strongest response to global warming is observed in arctic and alpine species, as well as in species capable of migrating over long distances. The aim of the research was to assess whether the fauna of aquatic insects in high-mountain habitats has changed as a result of climate change and, if so, how big and what type these changes are. Dragonflies were chosen as a model organism because of their fast reaction to changes in the environment: they have high migration abilities and short life cycle. The state of the populations of boreal-mountain species and the extent to which lowland species entered high altitudes was assessed. The research was carried out on 20 sites in Western Sudetes, Southern Poland. They were located at an altitude of between 850 and 1250 m. The selected sites were representative of many types of valuable alpine habitats (subalpine raised bog, transitional spring bog, habitats associated with rivers and mountain streams). Several sites of anthropogenic origin were also selected. Thanks to this selection, a wide characterization of the fauna of the Karkonosze was made and it was compared whether the studied processes proceeded differently, depending on whether the habitat is primary or secondary. Both imagines and larvae were examined (by taking hydrobiological samples with a kick-net), and exuviae were also collected. Individual species dragonflies were characterized in terms of their reproductive, territorial and foraging behaviour. During each inspection, the basic physicochemical parameters of the water were measured. The population of the high-mountain dragonfly Somatochlora alpestris turned out to be in a good condition. This species was noted at several sites. Some of those sites were situated relatively low (995 m AMSL), which proves that the thermal conditions at the lower altitudes might be still optimal for this species. The protected by polish law species Somatochlora arctica, Aeshna subarctica and Leucorrhinia albifrons, as well as strongly associated with bogs Leucorrhinia dubia and Aeshna juncea bogs were observed. However, they were more frequent and more numerous in habitats of anthropogenic origin, which may suggest minor changes in the habitat preferences of dragonflies. The subject requires further research and observations over a longer time scale.

Keywords: alpine species, bioindication, global warming, habitat preferences, population dynamics

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

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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: Javier Carroquino, Nieves García-Casarejos, Pilar Gargallo, F. Javier García-Ramos

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The energy used in agriculture is a source of global emissions of greenhouse gases. The two main types of this energy are electricity for pumping and diesel for agricultural machinery. In order to reduce these emissions, the European project LIFE REWIND addresses the supply of this demand from renewable sources. First of all, comprehensive data on energy demand and available renewable resources have been obtained in several case studies. Secondly, a set of simulations and optimizations have been performed, in search of the best configuration and sizing, both from an economic and emission reduction point of view. For this purpose, it was used software based on genetic algorithms. Thirdly, a prototype has been designed and installed, that it is being used for the validation in a real case. Finally, throughout a year of operation, various technical and economic parameters are being measured for further analysis. The prototype is not connected to the utility grid, avoiding the cost and environmental impact of a grid extension. The system includes three kinds of photovoltaic fields. One is located on a fixed structure on the terrain. Another one is floating on an irrigation raft. The last one is mounted on a two axis solar tracker. Each has its own solar inverter. The total amount of nominal power is 44 kW. A lead acid battery with 120 kWh of capacity carries out the energy storage. Three isolated inverters support a three phase, 400 V 50 Hz micro-grid, the same characteristics of the utility grid. An advanced control subsystem has been constructed, using free hardware and software. The electricity produced feeds a set of seven pumps used for purification, elevation and pressurization of water in a drip irrigation system located in a vineyard. Since the irrigation season does not include the whole year, as well as a small oversize of the generator, there is an amount of surplus energy. With this surplus, a hydrolyser produces on site hydrogen by electrolysis of water. An off-road vehicle with fuel cell feeds on that hydrogen and carries people in the vineyard. The only emission of the process is high purity water. On the one hand, the results show the technical and economic feasibility of stand-alone renewable energy systems to feed seasonal pumping. In this way, the economic costs, the environmental impacts and the landscape impacts of grid extensions are avoided. The use of diesel gensets and their associated emissions are also avoided. On the other hand, it is shown that it is possible to replace diesel in agricultural machinery, substituting it for electricity or hydrogen of 100% renewable origin and produced on the farm itself, without any external energy input. In addition, it is expected to obtain positive effects on the rural economy and employment, which will be quantified through interviews.

Keywords: drip irrigation, greenhouse gases, hydrogen, renewable energy, vineyard

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Authors: Mathias U. Bonet, Pericles Pilidis, Georgios Doulgeris

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A gas turbine-powered cruise Liner is scheduled to transport pilgrim passengers from Lagos-Nigeria to the Islamic port city of Jeddah in Saudi Arabia. Since the gas turbine is an air breathing machine, changes in the density and/or mass flow at the compressor inlet due to an encounter with variations in weather conditions induce negative effects on the performance of the power plant during the voyage. In practice, all deviations from the reference atmospheric conditions of 15 ^oC and 1.103 bar tend to affect the power output and other thermodynamic parameters of the gas turbine cycle. Therefore, this paper seeks to evaluate how a simple cycle marine gas turbine power plant would react under a variety of scenarios that may be encountered during a voyage as the ship sails across the Atlantic Ocean and the Mediterranean Sea before arriving at its designated port of discharge. It is also an assessment that focuses on the effect of varying aerodynamic and hydrodynamic conditions which deteriorate the efficient operation of the propulsion system due to an increase in resistance that results from some projected levels of the ship hull fouling. The investigated passenger ship is designed to run at a service speed of 22 knots and cover a distance of 5787 nautical miles. The performance evaluation consists of three separate voyages that cover a variety of weather conditions in winter, spring and summer seasons. Real-time daily temperatures and the sea states for the selected transit route were obtained and used to simulate the voyage under the aforementioned operating conditions. Changes in engine firing temperature, power output as well as the total fuel consumed per voyage including other performance variables were separately predicted under both calm and adverse weather conditions. The collated data were obtained online from the UK Meteorological Office as well as the UK Hydrographic Office websites, while adopting the Beaufort scale for determining the magnitude of sea waves resulting from rough weather situations. The simulation of the gas turbine performance and voyage analysis was effected through the use of an integrated Cranfield-University-developed computer code known as ‘Turbomatch’ and ‘Poseidon’. It is a project that is aimed at developing a method for predicting the off design behavior of the marine gas turbine when installed and operated as the main prime mover for both propulsion and powering of all other auxiliary services onboard a passenger cruise liner. Furthermore, it is a techno-economic and environmental assessment that seeks to enable the forecast of the marine gas turbine part and full load performance as it relates to the fuel requirement for a complete voyage.

Keywords: cruise ship, gas turbine, hull fouling, performance, propulsion, weather

Procedia PDF Downloads 150

Authors: K. M. S. Weerasinghe, A. H. K. Balasooriya, S. L. Ransingha, G. D. Krishantha, R. S. Brhakamanagae, L. C. Wijethilke

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Tomato (Lycopersicon esculentum Mill.) is a major horticultural crop with an estimated global production of over 120 million metric tons and ranks first as a processing crop. The average tomato productivity in Sri Lanka (11 metric tons/ha) is much lower than the world average (24 metric tons/ha).To meet the tomato demand for the increasing population the productivity has to be intensified through the agronomic-techniques. Nutrition is one of the main factors which govern the growth and yield of tomato and the main nutrient source soil affect the plant growth and quality of the produce. Continuous cropping, improper fertilizer usage etc., cause widespread nutrient deficiencies. Therefore synthetic fertilizers and organic manures were introduced to enhance plant growth and maximize the crop yields. In this study, effects of macro and micronutrient supplementations on improvement of growth and yield of tomato were investigated. Selected tomato variety is Maheshi and plants were grown in Regional Agricultural and Research Centre Makadura under the Department of Agriculture recommended (DOA) macro nutrients and various combination of Ontario recommended dosages of secondary and micro fertilizer supplementations. There were six treatments in this experiment and each treatment was replicated in three times and each replicate consisted of six plants. Other than the DOA recommendation, five combinations of Ontario recommended dosage of secondary and micronutrients for tomato were also used as treatments. The treatments were arranged in a Randomized Complete Block Design. All cultural practices were carried out according to the DOA recommendations. The mean data was subjected to the statistical analysis using SAS package and mean separation (Duncan’s Multiple Range test at 5% probability level) procedures. Secondary and micronutrients containing treatments significantly increased most of the growth parameters. Plant height, plant girth, number of leaves, leaf area index etc. Fruits harvested from pots amended with macro, secondary and micronutrients performed best in terms of total yield; yield quality; to pots amended with DOA recommended dosage of fertilizer for tomato. It could be due to the application of all essential macro and micro nutrients that rise in photosynthetic activity, efficient translocation and utilization of photosynthates causing rapid cell elongation and cell division in actively growing region of the plant leading to stimulation of growth and yield were caused. The experiment revealed and highlighted the requirements of essential macro, secondary and micro nutrient fertilizer supplementations for tomato farming. The study indicated that, macro and micro nutrient supplementation practices can influence growth and yield performances of tomato fruits and it is a promising approach to get potential tomato yields.

Keywords: macro and micronutrients, tomato, SAS package, photosynthates

Procedia PDF Downloads 442

Authors: Junkal Gutierrez, Hernane S. Barud, Sidney J. L. Ribeiro, Agnieszka Tercjak

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Nowadays the interest on green nanocomposites and on the development of more environmental friendly products has been increased. Bacterial cellulose has been recently investigated as an attractive environmentally friendly material for the preparation of low-cost nanocomposites. The formation of cellulose by laboratory bacterial cultures is an interesting and attractive biomimetic access to obtain pure cellulose with excellent properties. Additionally, properties as molar mass, molar mass distribution, and the supramolecular structure could be control using different bacterial strain, culture mediums and conditions, including the incorporation of different additives. This kind of cellulose is a natural nanomaterial, and therefore, it has a high surface-to-volume ratio which is highly advantageous in composites production. Such property combined with good biocompatibility, high tensile strength, and high crystallinity makes bacterial cellulose a potential material for applications in different fields. The aim of this investigation work was the fabrication of novel hybrid inorganic-organic composites based on bacterial cellulose, cultivated in our laboratory, as a template. This kind of biohybrid nanocomposites gathers together excellent properties of bacterial cellulose with the ones displayed by typical inorganic nanoparticles like optical, magnetic and electrical properties, luminescence, ionic conductivity and selectivity, as well as chemical or biochemical activity. In addition, the functionalization of cellulose with inorganic materials opens new pathways for the fabrication of novel multifunctional hybrid materials with promising properties for a wide range of applications namely electronic paper, flexible displays, solar cells, sensors, among others. In this work, different pathways for fabrication of multifunctional biohybrid nanopapers with tunable properties based on BC modified with amphiphilic poly(ethylene oxide-b-propylene oxide-b-ethylene oxide) (EPE) block copolymer, sol-gel synthesized nanoparticles (titanium, vanadium and a mixture of both oxides) and functionalized iron oxide nanoparticles will be presented. In situ (biosynthesized) and ex situ (at post-production level) approaches were successfully used to modify BC membranes. Bacterial cellulose based biocomposites modified with different EPE block copolymer contents were developed by in situ technique. Thus, BC growth conditions were manipulated to fabricate EPE/BC nanocomposite during the biosynthesis. Additionally, hybrid inorganic/organic nanocomposites based on BC membranes and inorganic nanoparticles were designed via ex-situ method, by immersion of never-dried BC membranes into different nanoparticle solutions. On the one hand, sol-gel synthesized nanoparticles (titanium, vanadium and a mixture of both oxides) and on the other hand superparamagnetic iron oxide nanoparticles (SPION), Fe2O3-PEO solution. The morphology of designed novel bionanocomposites hybrid materials was investigated by atomic force microscopy (AFM) and scanning electron microscopy (SEM). In order to characterized obtained materials from the point of view of future applications different techniques were employed. On the one hand, optical properties were analyzed by UV-vis spectroscopy and spectrofluorimetry and on the other hand electrical properties were studied at nano and macroscale using electric force microscopy (EFM), tunneling atomic force microscopy (TUNA) and Keithley semiconductor analyzer, respectively. Magnetic properties were measured by means of magnetic force microscopy (MFM). Additionally, mechanical properties were also analyzed.

Keywords: bacterial cellulose, block copolymer, advanced characterization techniques, nanoparticles

Procedia PDF Downloads 205

Authors: Claudio Loconsole, Daniele Leonardis, Antonio Brunetti, Gianpaolo Francesco Trotta, Nicholas Caporusso, Vitoantonio Bevilacqua

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Unfortunately, many people still do not have access to communication, with specific regard to reading and writing. Among them, people who are blind or visually impaired, have several difficulties in getting access to the world, compared to the sighted. Indeed, despite technology advancement and cost reduction, nowadays assistive devices are still expensive such as Braille-based input/output systems which enable reading and writing texts (e.g., personal notes, documents). As a consequence, assistive technology affordability is fundamental in supporting the visually impaired in communication, learning, and social inclusion. This, in turn, has serious consequences in terms of equal access to opportunities, freedom of expression, and actual and independent participation to a society designed for the sighted. Moreover, the visually impaired experience difficulties in recognizing objects and interacting with devices in any activities of daily living. It is not a case that Braille indications are commonly reported only on medicine boxes and elevator keypads. Several software applications for the automatic translation of written text into speech (e.g., Text-To-Speech - TTS) enable reading pieces of documents. However, apart from simple tasks, in many circumstances TTS software is not suitable for understanding very complicated pieces of text requiring to dwell more on specific portions (e.g., mathematical formulas or Greek text). In addition, the experience of reading\writing text is completely different both in terms of engagement, and from an educational perspective. Statistics on the employment rate of blind people show that learning to read and write provides the visually impaired with up to 80% more opportunities of finding a job. Especially in higher educational levels, where the ability to digest very complex text is key, accessibility and availability of Braille plays a fundamental role in reducing drop-out rate of the visually impaired, thus affecting the effectiveness of the constitutional right to get access to education. In this context, the Braille Lab project aims at overcoming these social needs by including affordability in designing and developing assistive tools for visually impaired people. In detail, our awarded project focuses on a technology innovation of the operation principle of existing assistive tools for the visually impaired leaving the Human-Machine Interface unchanged. This can result in a significant reduction of the production costs and consequently of tool selling prices, thus representing an important opportunity for social entrepreneurship. The first two assistive tools designed within the Braille Lab project following the proposed approach aims to provide the possibility to personally print documents and handouts and to read texts written in Braille using refreshable Braille display, respectively. The former, named ‘Braille Cartridge’, represents an alternative solution for printing in Braille and consists in the realization of an electronic-controlled dispenser printing (cartridge) which can be integrated within traditional ink-jet printers, in order to leverage the efficiency and cost of the device mechanical structure which are already being used. The latter, named ‘Braille Cursor’, is an innovative Braille display featuring a substantial technology innovation by means of a unique cursor virtualizing Braille cells, thus limiting the number of active pins needed for Braille characters.

Keywords: Human rights, social challenges and technology innovations, visually impaired, affordability, assistive tools

Procedia PDF Downloads 246

Authors: Hamed Delrobai Ghoochan Atigh

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Androgenic alopecia (AGA) is a common form of hair loss, impacting approximately 50% of females, which leads to reduced self-esteem and quality of life. It causes progressive follicular miniaturization in genetically predisposed individuals. Mesotherapy -- a minimally invasive procedure, topical 2% minoxidil, and oral finasteride have emerged as popular treatment options in the realm of cosmetics. However, the efficacy of mesotherapy compared to other options remains unclear. This study aims to assess the effectiveness of mesotherapy when it is added to topical 2% minoxidil treatment on female androgenic alopecia. Mesotherapy, also known as intradermotherapy, is a technique that entails administering multiple intradermal injections of a carefully composed mixture of compounds in low doses, applied at various points in close proximity to or directly over the affected areas. This study involves a randomized controlled trial with 100 female participants diagnosed with androgenic alopecia. The subjects were randomly assigned to two groups: Group A used topical 2% minoxidil twice daily and took Finastride oral tablet. For Group B, 10 mesotherapy sessions were added to the prior treatment. The injections were administered every week in the first month of treatment, every two weeks in the second month, and after that the injections were applied monthly for four consecutive months. The response assessment was made at baseline, the 4th session, and finally after 6 months when the treatment was complete. Clinical photographs, 7-point Likert scale patient self-evaluation, and 7-point Likert scale assessment tool were used to measure the effectiveness of the treatment. During this evaluation, a significant and visible improvement in hair density and thickness was observed. The study demonstrated a significant increase in treatment efficacy in Group B compared to Group A post-treatment, with no adverse effects. Based on the findings, it appears that mesotherapy offers a significant improvement in female AGA over minoxidil. Hair loss was stopped in Group B after one month and improvement in density and thickness of hair was observed after the third month. The findings from this study provide valuable insights into the efficacy of mesotherapy in treating female androgenic alopecia. Our evaluation offers a detailed assessment of hair growth parameters, enabling a better understanding of the treatments' effectiveness. The potential of this promising technique is significantly enhanced when carried out in a medical facility, guided by appropriate indications and skillful execution. An interesting observation in our study is that in areas where the hair had turned grey, the newly regrown hair does not retain its original grey color; instead, it becomes darker. The results contribute to evidence-based decision-making in dermatological practice and offer different insights into the treatment of female pattern hair loss.

Keywords: androgenic alopecia, female hair loss, mesotherapy, topical 2% minoxidil

Procedia PDF Downloads 76

Authors: Venencia Albert, Arulselvi Subramanian, Hara Prasad Pati, Asok K. Mukhophadhyay

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Acute traumatic coagulopathy in an endogenous dysregulation of the intrinsic coagulation system in response to the injury, associated with three-fold risk of poor outcome, and is more amenable to corrective interventions, subsequent to early identification and management. Multiple definitions for stratification of the patients' risk for early acute coagulopathy have been proposed, with considerable variations in the defining criteria, including several trauma-scoring systems based on prehospital data. We aimed to develop a clinically relevant definition for acute coagulopathy of trauma based on conventional coagulation assays and to assess its efficacy in comparison to recently established prehospital prediction models. Methodology: Retrospective data of all trauma patients (n = 490) presented to our level I trauma center, in 2014, was extracted. Receiver operating characteristic curve analysis was done to establish cut-offs for conventional coagulation assays for identification of patients with acute traumatic coagulopathy was done. Prospectively data of (n = 100) adult trauma patients was collected and cohort was stratified by the established definition and classified as "coagulopathic" or "non-coagulopathic" and correlated with the Prediction of acute coagulopathy of trauma score and Trauma-Induced Coagulopathy Clinical Score for identifying trauma coagulopathy and subsequent risk for mortality. Results: Data of 490 trauma patients (average age 31.85±9.04; 86.7% males) was extracted. 53.3% had head injury, 26.6% had fractures, 7.5% had chest and abdominal injury. Acute traumatic coagulopathy was defined as international normalized ratio ≥ 1.19; prothrombin time ≥ 15.5 s; activated partial thromboplastin time ≥ 29 s. Of the 100 adult trauma patients (average age 36.5±14.2; 94% males), 63% had early coagulopathy based on our conventional coagulation assay definition. Overall prediction of acute coagulopathy of trauma score was 118.7±58.5 and trauma-induced coagulopathy clinical score was 3(0-8). Both the scores were higher in coagulopathic than non-coagulopathic patients (prediction of acute coagulopathy of trauma score 123.2±8.3 vs. 110.9±6.8, p-value = 0.31; trauma-induced coagulopathy clinical score 4(3-8) vs. 3(0-8), p-value = 0.89), but not statistically significant. Overall mortality was 41%. Mortality rate was significantly higher in coagulopathic than non-coagulopathic patients (75.5% vs. 54.2%, p-value = 0.04). High prediction of acute coagulopathy of trauma score also significantly associated with mortality (134.2±9.95 vs. 107.8±6.82, p-value = 0.02), whereas trauma-induced coagulopathy clinical score did not vary be survivors and non-survivors. Conclusion: Early coagulopathy was seen in 63% of trauma patients, which was significantly associated with mortality. Acute traumatic coagulopathy defined by conventional coagulation assays (international normalized ratio ≥ 1.19; prothrombin time ≥ 15.5 s; activated partial thromboplastin time ≥ 29 s) demonstrated good ability to identify coagulopathy and subsequent mortality, in comparison to the prehospital parameter-based scoring systems. Prediction of acute coagulopathy of trauma score may be more suited for predicting mortality rather than early coagulopathy. In emergency trauma situations, where immediate corrective measures need to be taken, complex multivariable scoring algorithms may cause delay, whereas coagulation parameters and conventional coagulation tests will give highly specific results.

Keywords: trauma, coagulopathy, prediction, model

Procedia PDF Downloads 160

Authors: Aritra Majumdar

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While organizations strive to expand their new customer base, retaining existing relationships is a key aspect of improving overall profitability and also showcasing how successful an organization is in holding on to its customers. It is an experimentally proven fact that the lion’s share of profit always comes from existing customers. Hence seamless management of renewal journeys across different channels goes a long way in improving trust in the brand. From a quality assurance standpoint, time travel testing provides an approach to both business and technology teams to enhance the customer experience when they look to extend their partnership with the organization for a defined phase of time. This whitepaper will focus on key pillars of time travel testing: time travel planning, time travel data preparation, and enterprise automation. Along with that, it will call out some of the best practices and common accelerator implementation ideas which are generic across verticals like healthcare, insurance, etc. In this abstract document, a high-level snapshot of these pillars will be provided. Time Travel Planning: The first step of setting up a time travel testing roadmap is appropriate planning. Planning will include identifying the impacted systems that need to be time traveled backward or forward depending on the business requirement, aligning time travel with other releases, frequency of time travel testing, preparedness for handling renewal issues in production after time travel testing is done and most importantly planning for test automation testing during time travel testing. Time Travel Data Preparation: One of the most complex areas in time travel testing is test data coverage. Aligning test data to cover required customer segments and narrowing it down to multiple offer sequencing based on defined parameters are keys for successful time travel testing. Another aspect is the availability of sufficient data for similar combinations to support activities like defect retesting, regression testing, post-production testing (if required), etc. This section will talk about the necessary steps for suitable data coverage and sufficient data availability from a time travel testing perspective. Enterprise Automation: Time travel testing is never restricted to a single application. The workflow needs to be validated in the downstream applications to ensure consistency across the board. Along with that, the correctness of offers across different digital channels needs to be checked in order to ensure a smooth customer experience. This section will talk about the focus areas of enterprise automation and how automation testing can be leveraged to improve the overall quality without compromising on the project schedule. Along with the above-mentioned items, the white paper will elaborate on the best practices that need to be followed during time travel testing and some ideas pertaining to accelerator implementation. To sum it up, this paper will be written based on the real-time experience author had on time travel testing. While actual customer names and program-related details will not be disclosed, the paper will highlight the key learnings which will help other teams to implement time travel testing successfully.

Keywords: time travel planning, time travel data preparation, enterprise automation, best practices, accelerator implementation ideas

Procedia PDF Downloads 131

Authors: Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Kensuke Nakamura, Dongeun Choi, Byung-Woo Hong

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Artefacts are commonly encountered in the imaging process of clinical computed tomography (CT) where the artefact refers to any systematic discrepancy between the reconstructed observation and the true attenuation coefficient of the object. It is known that CT images are inherently more prone to artefacts due to its image formation process where a large number of independent detectors are involved, and they are assumed to yield consistent measurements. There are a number of different artefact types including noise, beam hardening, scatter, pseudo-enhancement, motion, helical, ring, and metal artefacts, which cause serious difficulties in reading images. Thus, it is desired to remove nuisance factors from the degraded image leaving the fundamental intrinsic information that can provide better interpretation of the anatomical and pathological characteristics. However, it is considered as a difficult task due to the high dimensionality and variability of data to be recovered, which naturally motivates the use of machine learning techniques. We propose an image restoration algorithm based on the deep neural network framework where the denoising auto-encoders are stacked building multiple layers. The denoising auto-encoder is a variant of a classical auto-encoder that takes an input data and maps it to a hidden representation through a deterministic mapping using a non-linear activation function. The latent representation is then mapped back into a reconstruction the size of which is the same as the size of the input data. The reconstruction error can be measured by the traditional squared error assuming the residual follows a normal distribution. In addition to the designed loss function, an effective regularization scheme using residual-driven dropout determined based on the gradient at each layer. The optimal weights are computed by the classical stochastic gradient descent algorithm combined with the back-propagation algorithm. In our algorithm, we initially decompose an input image into its intrinsic representation and the nuisance factors including artefacts based on the classical Total Variation problem that can be efficiently optimized by the convex optimization algorithm such as primal-dual method. The intrinsic forms of the input images are provided to the deep denosing auto-encoders with their original forms in the training phase. In the testing phase, a given image is first decomposed into the intrinsic form and then provided to the trained network to obtain its reconstruction. We apply our algorithm to the restoration of the corrupted CT images by the artefacts. It is shown that our algorithm improves the readability and enhances the anatomical and pathological properties of the object. The quantitative evaluation is performed in terms of the PSNR, and the qualitative evaluation provides significant improvement in reading images despite degrading artefacts. The experimental results indicate the potential of our algorithm as a prior solution to the image interpretation tasks in a variety of medical imaging applications. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: auto-encoder neural network, CT image artefact, deep learning, intrinsic image representation, noise reduction, total variation

Procedia PDF Downloads 170

Authors: D. Courault, L. Hossard, V. Demarez, E. Ndikumana, D. Ho Tong Minh, N. Baghdadi, F. Ruget

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In the global change context, efficient management of the available resources has become one of the most important topics, particularly for sustainable crop development. Timely assessment with high precision is crucial for water resource and pest management. Rice cultivated in Southern France in the Camargue region must face a challenge, reduction of the soil salinity by flooding and at the same time reduce the number of herbicides impacting negatively the environment. This context has lead farmers to diversify crop rotation and their agricultural practices. The objective of this study was to evaluate this crop diversity both in crop systems and in agricultural practices applied to rice paddy in order to quantify the impact on the environment and on the crop production. The proposed method is based on the combined use of crop models and multispectral data acquired from the recent Sentinel 2 satellite sensors launched by the European Space Agency (ESA) within the homework of the Copernicus program. More than 40 images at fine spatial resolution (10m in the optical range) were processed for 2016 and 2017 (with a revisit time of 5 days) to map crop types using random forest method and to estimate biophysical variables (LAI) retrieved by inversion of the PROSAIL canopy radiative transfer model. Thanks to the high revisit time of Sentinel 2 data, it was possible to monitor the soil labor before flooding and the second sowing made by some farmers to better control weeds. The temporal trajectories of remote sensing data were analyzed for various rice cultivars for defining the main parameters describing the phenological stages useful to calibrate two crop models (STICS and SAFY). Results were compared to surveys conducted with 10 farms. A large variability of LAI has been observed at farm scale (up to 2-3m²/m²) which induced a significant variability in the yields simulated (up to 2 ton/ha). Observations on more than 300 fields have also been collected on land use. Various maps were elaborated, land use, LAI, flooding and sowing, and harvest dates. All these maps allow proposing a new typology to classify these paddy crop systems. Key phenological dates can be estimated from inverse procedures and were validated against ground surveys. The proposed approach allowed to compare the years and to detect anomalies. The methods proposed here can be applied at different crops in various contexts and confirm the potential of remote sensing acquired at fine resolution such as the Sentinel2 system for agriculture applications and environment monitoring. This study was supported by the French national center of spatial studies (CNES, funded by the TOSCA).

Keywords: agricultural practices, remote sensing, rice, yield

Procedia PDF Downloads 258

Authors: Roberta Martino, Viviana Ventre

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Every day we face choices whose consequences are deferred in time. These types of choices are the intertemporal choices and play an important role in the social, economic, and financial world. The Discounted Utility Model is the mathematical model of reference to calculate the utility of intertemporal prospects. The discount rate is the main element of the model as it describes how the individual perceives the indeterminacy of subsequent periods. Empirical evidence has shown a discrepancy between the behavior expected from the predictions of the model and the effective choices made from the decision makers. In particular, the term temporal inconsistency indicates those choices that do not remain optimal with the passage of time. This phenomenon has been described with hyperbolic models of the discount rate which, unlike the linear or exponential nature assumed by the discounted utility model, is not constant over time. This paper explores the problem of inconsistency by tracing the decision-making process through the concept of impatience. The degree of impatience and the degree of decrease of impatience are two parameters that allow to quantify the weight of emotional factors and cognitive limitations during the evaluation and selection of alternatives. In fact, although the theory assumes perfectly rational decision makers, behavioral finance and cognitive psychology have made it possible to understand that distortions in the decision-making process and emotional influence have an inevitable impact on the decision-making process. The degree to which impatience is diminished is the focus of the first part of the study. By comparing consistent and inconsistent preferences over time, it was possible to verify that some anomalies in the discounted utility model are a result of the combination of cognitive bias and emotional factors. In particular: the delay effect and the interval effect are compared through the concept of misperception of time; starting from psychological considerations, a criterion is proposed to identify the causes of the magnitude effect that considers the differences in outcomes rather than their ratio; the sign effect is analyzed by integrating in the evaluation of prospects with negative outcomes the psychological aspects of loss aversion provided by Prospect Theory. An experiment implemented confirms three findings: the greatest variation in the degree of decrease in impatience corresponds to shorter intervals close to the present; the greatest variation in the degree of impatience occurs for outcomes of lower magnitude; the variation in the degree of impatience is greatest for negative outcomes. The experimental phase was implemented with the construction of the hyperbolic factor through the administration of questionnaires constructed for each anomaly. This work formalizes the underlying causes of the discrepancy between the discounted utility model and the empirical evidence of preference reversal.

Keywords: decreasing impatience, discount utility model, hyperbolic discount, hyperbolic factor, impatience

Procedia PDF Downloads 87

Authors: Emmanuel O. Ezim, Idowu A. Olayinka, Michael Oladunjoye, Izuchukwu I. Obiadi

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Monitoring of reservoir properties prior to well placements and production is a requirement for optimisation and efficient oil and gas production. This is usually done using well log analyses and 3-D seismic, which are often prone to errors. However, 4-D (Time-lapse) seismic, incorporating numerous 3-D seismic surveys of the same field with the same acquisition parameters, which portrays the transient changes in the reservoir due to production effects over time, could be utilised because it generates better resolution. There is, however dearth of information on the applicability of this approach in the Niger Delta. This study was therefore designed to apply 4-D seismic, well-log and geologic data in monitoring of reservoirs in the EK field of the Niger Delta. It aimed at locating bypassed accumulations and ensuring effective reservoir management. The Field (EK) covers an area of about 1200km2 belonging to the early (18ma) Miocene. Data covering two 4-D vintages acquired over a fifteen-year interval were obtained from oil companies operating in the field. The data were analysed to determine the seismic structures, horizons, Well-to-Seismic Tie (WST), and wavelets. Well, logs and production history data from fifteen selected wells were also collected from the Oil companies. Formation evaluation, petrophysical analysis and inversion alongside geological data were undertaken using Petrel, Shell-nDi, Techlog and Jason Software. Well-to-seismic tie, formation evaluation and saturation monitoring using petrophysical and geological data and software were used to find bypassed hydrocarbon prospects. The seismic vintages were interpreted, and the amounts of change in the reservoir were defined by the differences in Acoustic Impedance (AI) inversions of the base and the monitor seismic. AI rock properties were estimated from all the seismic amplitudes using controlled sparse-spike inversion. The estimated rock properties were used to produce AI maps. The structural analysis showed the dominance of NW-SE trending rollover collapsed-crest anticlines in EK with hydrocarbons trapped northwards. There were good ties in wells EK 27, 39. Analysed wavelets revealed consistent amplitude and phase for the WST; hence, a good match between the inverted impedance and the good data. Evidence of large pay thickness, ranging from 2875ms (11420 TVDSS-ft) to about 2965ms, were found around EK 39 well with good yield properties. The comparison between the base of the AI and the current monitor and the generated AI maps revealed zones of untapped hydrocarbons as well as assisted in determining fluids movement. The inverted sections through EK 27, 39 (within 3101 m - 3695 m), indicated depletion in the reservoirs. The extent of the present non-uniform gas-oil contact and oil-water contact movements were from 3554 to 3575 m. The 4-D seismic approach led to better reservoir characterization, well development and the location of deeper and bypassed hydrocarbon reservoirs.

Keywords: reservoir monitoring, 4-D seismic, well placements, petrophysical analysis, Niger delta basin

Procedia PDF Downloads 100

Authors: Christine F. Boos, Fernando M. Azevedo

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Electroencephalogram (EEG) is a record of the electrical activity of the brain that has many applications, such as monitoring alertness, coma and brain death; locating damaged areas of the brain after head injury, stroke and tumor; monitoring anesthesia depth; researching physiology and sleep disorders; researching epilepsy and localizing the seizure focus. Epilepsy is a chronic condition, or a group of diseases of high prevalence, still poorly explained by science and whose diagnosis is still predominantly clinical. The EEG recording is considered an important test for epilepsy investigation and its visual analysis is very often applied for clinical confirmation of epilepsy diagnosis. Moreover, this EEG analysis can also be used to help define the types of epileptic syndrome, determine epileptiform zone, assist in the planning of drug treatment and provide additional information about the feasibility of surgical intervention. In the context of diagnosis confirmation the analysis is made using long term EEG recordings with at least 24 hours long and acquired by a minimum of 24 electrodes in which the neurophysiologists perform a thorough visual evaluation of EEG screens in search of specific electrographic patterns called epileptiform discharges. Considering that the EEG screens usually display 10 seconds of the recording, the neurophysiologist has to evaluate 360 screens per hour of EEG or a minimum of 8,640 screens per long term EEG recording. Analyzing thousands of EEG screens in search patterns that have a maximum duration of 200 ms is a very time consuming, complex and exhaustive task. Because of this, over the years several studies have proposed automated methodologies that could facilitate the neurophysiologists’ task of identifying epileptiform discharges and a large number of methodologies used neural networks for the pattern classification. One of the differences between all of these methodologies is the type of input stimuli presented to the networks, i.e., how the EEG signal is introduced in the network. Five types of input stimuli have been commonly found in literature: raw EEG signal, morphological descriptors (i.e. parameters related to the signal’s morphology), Fast Fourier Transform (FFT) spectrum, Short-Time Fourier Transform (STFT) spectrograms and Wavelet Transform features. This study evaluates the application of these five types of input stimuli and compares the classification results of neural networks that were implemented using each of these inputs. The performance of using raw signal varied between 43 and 84% efficiency. The results of FFT spectrum and STFT spectrograms were quite similar with average efficiency being 73 and 77%, respectively. The efficiency of Wavelet Transform features varied between 57 and 81% while the descriptors presented efficiency values between 62 and 93%. After simulations we could observe that the best results were achieved when either morphological descriptors or Wavelet features were used as input stimuli.

Keywords: Artificial neural network, electroencephalogram signal, pattern recognition, signal processing

Procedia PDF Downloads 505

Authors: Mitali Saha, Soma Das

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The fabrication of nanoscale materials for use in chemical sensing, biosensing and biological analyses has proven a promising avenue in the last few years. Cholesterol has aroused considerable interest in recent years on account of its being an important parameter in clinical diagnosis. There is a strong positive correlation between high serum cholesterol level and arteriosclerosis, hypertension, and myocardial infarction. Enzyme-based electrochemical biosensors have shown high selectivity and excellent sensitivity, but the enzyme is easily denatured during its immobilization procedure and its activity is also affected by temperature, pH, and toxic chemicals. Besides, the reproducibility of enzyme-based sensors is not very good which further restrict the application of cholesterol biosensor. It has been demonstrated that carbon nanotubes could promote electron transfer with various redox active proteins, ranging from cytochrome c to glucose oxidase with a deeply embedded redox center. In continuation of our earlier work on the synthesis and applications of carbon and metal based nanoparticles, we have reported here the synthesis of carbon nanotubes (CCNT) by burning coconut oil under insufficient flow of air using an oil lamp. The soot was collected from the top portion of the flame, where the temperature was around 6500C which was purified, functionalized and then characterized by SEM, p-XRD and Raman spectroscopy. The SEM micrographs showed the formation of tubular structure of CCNT having diameter below 100 nm. The XRD pattern indicated the presence of two predominant peaks at 25.20 and 43.80, which corresponded to (002) and (100) planes of CCNT respectively. The Raman spectrum (514 nm excitation) showed the presence of 1600 cm-1 (G-band) related to the vibration of sp2-bonded carbon and at 1350 cm-1 (D-band) responsible for the vibrations of sp3-bonded carbon. A nonenzymatic cholesterol biosensor was then fabricated on an insulating Teflon material containing three silver wires at the surface, covered by CCNT, obtained from coconut oil. Here, CCNTs worked as working as well as counter electrodes whereas reference electrode and electric contacts were made of silver. The dimensions of the electrode was 3.5 cm×1.0 cm×0.5 cm (length× width × height) and it is ideal for working with 50 µL volume like the standard screen printed electrodes. The voltammetric behavior of cholesterol at CCNT electrode was investigated by cyclic voltammeter and differential pulse voltammeter using 0.001 M H2SO4 as electrolyte. The influence of the experimental parameters on the peak currents of cholesterol like pH, accumulation time, and scan rates were optimized. Under optimum conditions, the peak current was found to be linear in the cholesterol concentration range from 1 µM to 50 µM with a sensitivity of ~15.31 μAμM−1cm−2 with lower detection limit of 0.017 µM and response time of about 6s. The long-term storage stability of the sensor was tested for 30 days and the current response was found to be ~85% of its initial response after 30 days.

Keywords: coconut oil, CCNT, cholesterol, biosensor

Procedia PDF Downloads 266

Authors: Ahmad Saadiq, Neeraj Sahu

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

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

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Authors: Sheikh Nawaz Ali, Pratima Pandey, P. Morthekai, Jyotsna Dubey, Md. Firoze Quamar

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The high mountain glaciers are one of the most sensitive recorders of climate changes, because they have the tendency to respond to the combined effect of snow fall and temperature. The Himalayan glaciers have been studied with a good pace during the last decade. However, owing to its large ecological diversity and geographical vividness, major part of the Indian Himalaya is uninvestigated, and hence the palaeoclimatic patterns as well as the chronology of past glaciations in particular remain controversial for the entire Indian Himalayan transect. Although the Himalayan glaciers are nourished by two important climatic systems viz. the southwest summer monsoon and the mid-latitude westerlies, however, the influence of these systems is yet to be understood. Nevertheless, existing chronology (mostly exposure ages) indicate that irrespective of the geographical position, glaciers seem to grow during enhanced Indian summer monsoon (ISM). The Himalayan mountain glaciers are referred to the third pole or water tower of Asia as they form a huge reservoir of the fresh water supplies for the Asian countries. Mountain glaciers are sensitive probes of the local climate, and, thus, they present an opportunity and a challenge to interpret climates of the past as well as to predict future changes. The principle object of all the palaeoclimatic studies is to develop a futuristic models/scenario. However, it has been found that the glacial chronologies bracket the major phases of climatic events only, and other climatic proxies are sparse in Himalaya. This is the reason that compilation of data for rapid climatic change during the Holocene shows major gaps in this region. The sedimentation in proglacial lakes, conversely, is more continuous and, hence, can be used to reconstruct a more complete record of past climatic variability that is modulated by changing ice volume of the valley glacier. The Himalayan region has numerous proglacial lacustrine deposits formed during the late Quaternary period. However, there are only few such deposits which have been studied so far. Therefore, this is the high time when efforts have to be made to systematically map the moraines located in different climatic zones, reconstruct the local and regional moraine stratigraphy and use multiple dating techniques to bracket the events of glaciation. Besides this, emphasis must be given on carrying multiproxy studies on the lacustrine sediments that will provide a high resolution palaeoclimatic data from the alpine region of the Himalaya. Although the Himalayan glaciers fluctuated in accordance with the changing climatic conditions (natural forcing), however, it is too early to arrive at any conclusion. It is very crucial to generate multiproxy data sets covering wider geographical and ecological domains taking into consideration multiple parameters that directly or indirectly influence the glacier mass balance as well as the local climate of a region.

Keywords: glacial chronology, palaeoclimate, multiproxy, Himalaya

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Authors: Andreas Schomburg, Claire Le Bayon, Claire Guenat, Philip Brunner

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Numerous river restoration projects have been established in Switzerland in recent years after decades of human activity in floodplains. The success of restoration projects in terms of biodiversity and ecosystem functions largely depend on the development of the floodplain soil system. Plants and earthworms as ecosystem engineers are known to be able to build up a stable soil structure by incorporating soil organic matter into the soil matrix that creates water stable soil aggregates. Their engineering efficiency however largely depends on changing soil properties and frequent floods along an evolutive floodplain transect. This study, therefore, aims to quantify the effect of flood frequency and duration as well as of physico-chemical soil parameters on plants’ and earthworms’ engineering efficiency. It is furthermore predicted that these influences may have a different impact on one of the engineers that leads to a varying contribution to aggregate formation within the floodplain transect. Ecosystem engineers were sampled and described in three different floodplain habitats differentiated according to the evolutionary stages of the vegetation ranging from pioneer to forest vegetation in a floodplain restored 15 years ago. In addition, the same analyses were performed in an embanked adjacent pasture as a reference for the pre-restored state. Soil aggregates were collected and analyzed for their organic matter quantity and quality using Rock Eval pyrolysis. Water level and discharge measurements dating back until 2008 were used to quantify the return period of major floods. Our results show an increasing amount of water stable aggregates in soil with increasing distance to the river and show largest values in the reference site. A decreasing flood frequency and the proportion of silt and clay in the soil texture explain these findings according to F values from one way ANOVA of a fitted mixed effect model. Significantly larger amounts of labile organic matter signatures were found in soil aggregates in the forest habitat and in the reference site that indicates a larger contribution of plants to soil aggregation in these habitats compared to the pioneer vegetation zone. Earthworms’ contribution to soil aggregation does not show significant differences in the floodplain transect, but their effect could be identified even in the pioneer vegetation with its large proportion of coarse sand in the soil texture and frequent inundations. These findings indicate that ecosystem engineers seem to be able to create soil aggregates even under unfavorable soil conditions and under frequent floods. A restoration success can therefore be expected even in ecosystems with harsh soil properties and frequent external disturbances.

Keywords: ecosystem engineers, flood frequency, floodplains, river restoration, rock eval pyrolysis, soil organic matter incorporation, soil structuration

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Authors: Eashwar V. Somasundaram, Raoul R. Wadhwa, Jacob G. Scott

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The use of radiomics in measuring geometric properties of tumor images such as size, surface area, and volume has been invaluable in assessing cancer diagnosis, treatment, and prognosis. In addition to analyzing geometric properties, radiomics would benefit from measuring topological properties using persistent homology. Intuitively, features uncovered by persistent homology may correlate to tumor structural features. One example is necrotic cavities (corresponding to 2D topological features), which are markers of very aggressive tumors. We develop a data pipeline in R that clusters tumors images based on persistent homology is used to identify meaningful clinical distinctions between tumors and possibly new relationships not captured by established clinical categorizations. A preliminary analysis was performed on 16 Magnetic Resonance Imaging (MRI) breast tissue segments downloaded from the 'Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis' (I-SPY TRIAL or ISPY1) collection in The Cancer Imaging Archive. Each segment represents a patient’s breast tumor prior to treatment. The ISPY1 dataset also provided the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status data. A persistent homology matrix up to 2-dimensional features was calculated for each of the MRI segmentation. Wasserstein distances were then calculated between all pairwise tumor image persistent homology matrices to create a distance matrix for each feature dimension. Since Wasserstein distances were calculated for 0, 1, and 2-dimensional features, three hierarchal clusters were constructed. The adjusted Rand Index was used to see how well the clusters corresponded to the ER/PR/HER2 status of the tumors. Triple-negative cancers (negative status for all three receptors) significantly clustered together in the 2-dimensional features dendrogram (Adjusted Rand Index of .35, p = .031). It is known that having a triple-negative breast tumor is associated with aggressive tumor growth and poor prognosis when compared to non-triple negative breast tumors. The aggressive tumor growth associated with triple-negative tumors may have a unique structure in an MRI segmentation, which persistent homology is able to identify. This preliminary analysis shows promising results in the use of persistent homology on tumor imaging to assess the severity of breast tumors. The next step is to apply this pipeline to other tumor segment images from The Cancer Imaging Archive at different sites such as the lung, kidney, and brain. In addition, whether other clinical parameters, such as overall survival, tumor stage, and tumor genotype data are captured well in persistent homology clusters will be assessed. If analyzing tumor MRI segments using persistent homology consistently identifies clinical relationships, this could enable clinicians to use persistent homology data as a noninvasive way to inform clinical decision making in oncology.

Keywords: cancer biology, oncology, persistent homology, radiomics, topological data analysis, tumor imaging

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Authors: Muhammad Naveed Tahir, Wang Yingkuan, Huang Wenjiang, Raheel Osman

Abstract:

Numerous models used in prediction and decision-making process but most of them are linear in natural environment, and linear models reach their limitations with non-linearity in data. Therefore accurate estimation is difficult. Artificial Neural Networks (ANN) found extensive acceptance to address the modeling of the complex real world for the non-linear environment. ANN’s have more general and flexible functional forms than traditional statistical methods can effectively deal with. The link between information technology and agriculture will become more firm in the near future. Monitoring crop biophysical properties non-destructively can provide a rapid and accurate understanding of its response to various environmental influences. Crop chlorophyll content is an important indicator of crop health and therefore the estimation of crop yield. In recent years, remote sensing has been accepted as a robust tool for site-specific management by detecting crop parameters at both local and large scales. The present research combined the ANN model with satellite-derived chlorophyll indices from LANDSAT 8 imagery for predicting real-time wheat chlorophyll estimation. The cloud-free scenes of LANDSAT 8 were acquired (Feb-March 2016-17) at the same time when ground-truthing campaign was performed for chlorophyll estimation by using SPAD-502. Different vegetation indices were derived from LANDSAT 8 imagery using ERADAS Imagine (v.2014) software for chlorophyll determination. The vegetation indices were including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Chlorophyll Absorbed Ratio Index (CARI), Modified Chlorophyll Absorbed Ratio Index (MCARI) and Transformed Chlorophyll Absorbed Ratio index (TCARI). For ANN modeling, MATLAB and SPSS (ANN) tools were used. Multilayer Perceptron (MLP) in MATLAB provided very satisfactory results. For training purpose of MLP 61.7% of the data, for validation purpose 28.3% of data and rest 10% of data were used to evaluate and validate the ANN model results. For error evaluation, sum of squares error and relative error were used. ANN model summery showed that sum of squares error of 10.786, the average overall relative error was .099. The MCARI and NDVI were revealed to be more sensitive indices for assessing wheat chlorophyll content with the highest coefficient of determination R²=0.93 and 0.90 respectively. The results suggested that use of high spatial resolution satellite imagery for the retrieval of crop chlorophyll content by using ANN model provides accurate, reliable assessment of crop health status at a larger scale which can help in managing crop nutrition requirement in real time.

Keywords: ANN, chlorophyll content, chlorophyll indices, satellite images, wheat

Procedia PDF Downloads 127