Search results for: 3D plant data

Authors: Nita Rukminasari

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The aim of this study is to select microalgae strains, which were isolated from agriculture and municipal wastewater drain, Reno, Nevada that has highest growth rate and lipid contents. The experiments in this study were carried out in two consecutive stages. The first stage is aimed at testing the survival capability of all isolated microalgae strains and determining the best candidates to grow in centrate cultivation system. The second stage was targeted at determination the highest growth rate and highest lipid content of the selected top performing algae strain when cultivated on centrate wastewater. 26 microalgae strains, which were isolated from municipal and agriculture waste water, were analyzed using Flow cytometer for FACS of lipid with BODIPY and Nile Red as a lipid dyes and they grew on 96 wells plate for 31 days to determine growth rate as a based line data for growth rate. The result showed that microalgae strains which showed a high mean of fluorescence for BODIPY and Nile Red were F3.BP.1, F3.LV.1, T1.3.1, and T1.3.3. Five microalgae strains which have high growth rate were T1.3.3, T2.4.1. F3.LV.1, T2.12.1 and T3.3.1. In conclusion, microalgae strain which showed the highest starch content was F3.LV.1. T1.3.1 had the highest mean of fluorescence for Nile Red and BODIPY. Microalgae strains were potential for biofuel feedstock such as F3.LV.1 and T1.3.1, those microalgae strains showed a positive correlation between growth rate at stationary phase, biomass and meant of fluorescence for Nile Red and BODIPY.

Keywords: agriculture and municipal wastewater, biofuel, centrate, microalgae

Procedia PDF Downloads 301

Authors: Li Qingjian, Li Ke, He Chun, Huang Yong

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In this paper, the method of combining the Pohl Seidman's deep belief network with the self-organizing neural network is proposed to classify the target. This method is mainly aimed at the high nonlinearity of the hyperspectral image, the high sample dimension and the difficulty in designing the classifier. The main feature of original data is extracted by deep belief network. In the process of extracting features, adding known labels samples to fine tune the network, enriching the main characteristics. Then, the extracted feature vectors are classified into the self-organizing neural network. This method can effectively reduce the dimensions of data in the spectrum dimension in the preservation of large amounts of raw data information, to solve the traditional clustering and the long training time when labeled samples less deep learning algorithm for training problems, improve the classification accuracy and robustness. Through the data simulation, the results show that the proposed network structure can get a higher classification precision in the case of a small number of known label samples.

Keywords: DBN, SOM, pattern classification, hyperspectral, data compression

Procedia PDF Downloads 327

Authors: Masood Varshosaz, Kamyar Hasanpour

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In recent years, we have seen growing interest in recognizing humans in drone images for post-disaster search and rescue operations. Deep learning algorithms have shown great promise in this area, but they often require large amounts of labeled data to train the models. To keep the data acquisition cost low, augmentation techniques can be used to create additional data from existing images. There are many techniques of such that can help generate variations of an original image to improve the performance of deep learning algorithms. While data augmentation is potentially assumed to improve the accuracy and robustness of the models, it is important to ensure that the performance gains are not outweighed by the additional computational cost or complexity of implementing the techniques. To this end, it is important to evaluate the impact of data augmentation on the performance of the deep learning models. In this paper, we evaluated the most currently available 2D data augmentation techniques on a standard convolutional network which was trained for recognizing humans in drone images. The techniques include rotation, scaling, random cropping, flipping, shifting, and their combination. The results showed that the augmented models perform 1-3% better compared to a base network. However, as the augmented images only contain the human parts already visible in the original images, a new data augmentation approach is needed to include the invisible parts of the human body. Thus, we suggest a new method that employs simulated 3D human models to generate new data for training the network.

Keywords: human recognition, deep learning, drones, disaster mitigation

Procedia PDF Downloads 76

Authors: Anjum-Zubair, Muhammad, Muhammad Shoaib Alam, Muhammad Samee Mubarik, Iftikhar Ahmad

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The aim of present research was to evaluate the effect of dietary protein (soybean) formulated feed on the growth performance of carp fish seed (Rohu, Mori, Grass, and Gulfam) in ponds under polyculture system. Keeping in view the protein requirements of these four carps, they were fed with formulated feed contains 30% of crude protein. The fingerlings were fed once on daily basis at 5% of their wet body weight. A 90 days experiment was conducted in two cemented ponds situated at Fish Seed Hatchery and Research Centre, Rawal Town, Islamabad, Pakistan. Pond1 contain major carps i.e. Rohu and Mori while pond 2 was stocked with Chinese carps i.e. Grass carp and Gulfam. Random sampling of five individuals of each species was done fortnightly to measure the body weight and total body length. Maximum growth was observed in fingerling of Grass carp followed by Mori, Rohu and Gulfam. Total fish production was recorded as Grass 623.45 gm followed by Mori 260.3 gm, Rohu 243.08 gm and Gulfam 181.165 gm respectively. Significantly results were obtained among these four fish species when the corresponding data was subjected to statistical analysis by using two sample t-test. The survival rate was 100%. Study shows that soybean as plant based protein can be easily used as substitute to fish meal without any adverse effect on fish health and fish production.

Keywords: carps, fry growth, poly culture, soybean meal

Procedia PDF Downloads 483

Authors: Emine Akar

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The majority of privacy-related regulation has traditionally focused on concepts that are perceived to be well-understood or easily describable, such as certain categories of data and personal information or images. In the past century, such regulation appeared reasonably suitable for its purposes. However, technologies such as AI, combined with ever-increasing capabilities to collect, process, and store “big data”, not only require calibration of these traditional understandings but may require re-thinking of entire categories of privacy law. In the presentation, it will be explained, against the background of various emerging technologies under the umbrella term “emotional artificial intelligence”, why modern privacy law will need to embrace human emotions as potentially private subject matter. This argument can be made on a jurisprudential level, given that human emotions can plausibly be accommodated within the various concepts that are traditionally regarded as the underlying foundation of privacy protection, such as, for example, dignity, autonomy, and liberal values. However, the practical reasons for regarding human emotions as potentially private subject matter are perhaps more important (and very likely more convincing from the perspective of regulators). In that respect, it should be regarded as alarming that, according to most projections, the usefulness of emotional data to governments and, particularly, private companies will not only lead to radically increased processing and analysing of such data but, concerningly, to an exponential growth in the collection of such data. In light of this, it is also necessity to discuss options for how regulators could address this emerging threat.

Keywords: AI, privacy law, data protection, big data

Procedia PDF Downloads 75

Authors: Reza Mohammadzadeh

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The major challenges in geotechnical engineering in underground spaces arise from uncertainties and different probabilities. The collection, collation, and collaboration of existing data to incorporate them in analysis and design for given prospect evaluation would be a reliable, practical problem solving method under uncertainty. Machine learning (ML) is a subfield of artificial intelligence in statistical science which applies different techniques (e.g., Regression, neural networks, support vector machines, decision trees, random forests, genetic programming, etc.) on data to automatically learn and improve from them without being explicitly programmed and make decisions and predictions. In this paper, a conceptual database schema of geotechnical risks in underground coal mining based on a cloud system architecture has been designed. A new approach of risk assessment using a three-dimensional risk matrix supported by the level of knowledge (LoK) has been proposed in this model. Subsequently, the model workflow methodology stages have been described. In order to train data and LoK models deployment, an ML platform has been implemented. IBM Watson Studio, as a leading data science tool and data-driven cloud integration ML platform, is employed in this study. As a Use case, a data set of geotechnical hazards and risk assessment in underground coal mining were prepared to demonstrate the performance of the model, and accordingly, the results have been outlined.

Keywords: data model, geotechnical risks, machine learning, underground coal mining

Procedia PDF Downloads 254

Authors: Anung Style Bukhori, Ani Dijah Rahajoe

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Poverty poses a significant challenge in Indonesia, requiring an effective analytical approach to understand and address this issue. In this research, we applied the Naïve Bayes classification method to examine and classify poverty data in Indonesia. The main focus is on classifying data using RapidMiner, a powerful data analysis platform. The analysis process involves data splitting to train and test the classification model. First, we collected and prepared a poverty dataset that includes various factors such as education, employment, and health..The experimental results indicate that the Naïve Bayes classification model can provide accurate predictions regarding the risk of poverty. The use of RapidMiner in the analysis process offers flexibility and efficiency in evaluating the model's performance. The classification produces several values to serve as the standard for classifying poverty data in Indonesia using Naive Bayes. The accuracy result obtained is 40.26%, with a moderate recall result of 35.94%, a high recall result of 63.16%, and a low recall result of 38.03%. The precision for the moderate class is 58.97%, for the high class is 17.39%, and for the low class is 58.70%. These results can be seen from the graph below.

Keywords: poverty, classification, naïve bayes, Indonesia

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Authors: Anna Martin, Raffael Osen

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The demand for fish from aquaculture is constantly growing. Concurrently, due to a shortage of fishmeal caused by extensive overfishing, fishmeal substitution by plant proteins is getting increasingly important for the production of sustainable aquafeed. Several research studies evaluated the impact of plant protein meals, concentrates or isolates on fish health and fish feed quality. However, these protein raw materials often require elaborate and expensive manufacturing and their availability is limited. Rapeseed press cake (RPC) – a side product of de-oiling processes – exhibits a high potential as a plant-based fishmeal alternative in fish feed for carnivorous species due to its availability, low costs and protein content. In order to produce aquafeed with RPC, it is important to systematically assess i) inclusion levels of RPC with similar pellet qualities compared to fishmeal containing formulations and ii) how extrusion parameters can be adjusted to achieve targeted pellet qualities. However, the effect of RPC on extrusion system parameters and pellet quality has only scarcely been investigated. Therefore, the aim of this study was to evaluate the impact of feed formulation, extruder barrel temperature (90, 100, 110 °C) and screw speed (200, 300, 400 rpm) on extrusion system parameters and the physical properties of fish feed pellets. A co-rotating pilot-scale twin screw extruder was used to produce five iso-nitrogenous feed formulations: a fish meal based reference formulation including 16 g/100g fishmeal and four formulations in which fishmeal was substituted by RPC to 25, 50, 75 or 100 %. Extrusion system parameters, being product temperature, pressure at the die, specific mechanical energy (SME) and torque, were monitored while samples were taken. After drying, pellets were analyzed regarding to optical appearance, sectional and longitudinal expansion, sinking velocity, bulk density, water stability, durability and specific hardness. In our study, the addition of minor amounts of RPC already had high impact on pellet quality parameters, especially on expansion but only marginally affected extrusion system parameters. Increasing amounts of RPC reduced sectional expansion, sinking velocity, bulk density and specific hardness and increased longitudinal expansion compared to a reference formulation without RPC. Water stability and durability were almost not affected by RPC addition. Moreover, pellets with rapeseed components showed a more coarse structure than pellets containing only fishmeal. When the adjustment of barrel temperature and screw speed was investigated, it could be seen that the increase of extruder barrel temperature led to a slight decrease of SME and die pressure and an increased sectional expansion of the reference pellets but did almost not affect rapeseed containing fish feed pellets. Also changes in screw speed had little effects on the physical properties of pellets however with raised screw speed the SME and the product temperature increased. In summary, a one-to-one substitution of fishmeal with RPC without the adjustment of extrusion process parameters does not result in fish feed of a designated quality. Therefore, a deeper knowledge of raw materials and their behavior under thermal and mechanical stresses as applied during extrusion is required.

Keywords: extrusion, fish feed, press cake, rapeseed

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Authors: Takahiro Nishigaki, Takashi Onoda

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In recent years, the number of document data has been increasing since the spread of the Internet. Many methods have been studied for extracting topics from large document data. We proposed Independent Topic Analysis (ITA) to extract topics independent of each other from large document data such as newspaper data. ITA is a method for extracting the independent topics from the document data by using the Independent Component Analysis. The topic represented by ITA is represented by a set of words. However, the set of words is quite different from the topics the user imagines. For example, the top five words with high independence of a topic are as follows. Topic1 = {"scor", "game", "lead", "quarter", "rebound"}. This Topic 1 is considered to represent the topic of "SPORTS". This topic name "SPORTS" has to be attached by the user. ITA cannot name topics. Therefore, in this research, we propose a method to obtain topics easy for people to understand by using the web search engine, topics given by the set of words given by independent topic analysis. In particular, we search a set of topical words, and the title of the homepage of the search result is taken as the topic name. And we also use the proposed method for some data and verify its effectiveness.

Keywords: independent topic analysis, topic extraction, topic naming, web search engine

Procedia PDF Downloads 106

Authors: Ziad Abdeldayem, Jakub Markiewicz, Kunal Kansara, Laura Edwards

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Airborne Laser Scanning (ALS) is one of the main technologies for generating high-resolution digital terrain models (DTMs). DTMs are crucial to several applications, such as topographic mapping, flood zone delineation, geographic information systems (GIS), hydrological modelling, spatial analysis, etc. Laser scanning system generates irregularly spaced three-dimensional cloud of points. Raw ALS data are mainly ground points (that represent the bare earth) and non-ground points (that represent buildings, trees, cars, etc.). Removing all the non-ground points from the raw data is referred to as filtering. Filtering heavily forested areas is considered a difficult and challenging task as the canopy stops laser pulses from reaching the terrain surface. This research presents an approach for removing non-ground points from raw ALS data in densely forested areas. Smoothing splines are exploited to interpolate and fit the noisy ALS data. The presented filter utilizes a weight function to allocate weights for each point of the data. Furthermore, unlike most of the methods, the presented filtering algorithm is designed to be automatic. Three different forested areas in the United Kingdom are used to assess the performance of the algorithm. The results show that the generated DTMs from the filtered data are accurate (when compared against reference terrain data) and the performance of the method is stable for all the heavily forested data samples. The average root mean square error (RMSE) value is 0.35 m.

Keywords: airborne laser scanning, digital terrain models, filtering, forested areas

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Authors: Saleem Z. Ramadan

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In this paper, a model is proposed to determine the life distribution parameters of the useful life region for the PV system utilizing a combination of non-parametric and linear regression analysis for the failure data of these systems. Results showed that this method is dependable for analyzing failure time data for such reliable systems when the data is scarce.

Keywords: masking, bathtub model, reliability, non-parametric analysis, useful life

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Authors: Lenka Jesonkova, Frantisek Bozek

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Trihalogenmethanes (THMs) are disinfection byproducts with non-carcinogenic and genotoxic effects. The contamination of 6 sites close to the water treatment plant has been monitored in second largest city of the Czech Republic. Health risk assessment including both non-carcinogenic and genotoxic risk for long term exposition was realized using the critical concentrations. Concentrations of trihalogenmethanes met national standards in all samples. Risk assessment proved that health risks from trihalogenmethanes are acceptable on each site.

Keywords: drinking water, health risk assessment, trihalogenmethanes, water pollution

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Authors: Sibusiso Trevor Tshabalala, Samuel Lubbe, Vince Vuledzani Ndou

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Dependency on chemicals has had many disadvantages in pest management control strategies. Such genetic rodenticide resistance and secondary exposure risk are what is currently being experienced. Emphasis on integrated pest management suggests that to control future pests, early intervention and economic threshold development are key starting points in crop production. The significance of this research project is to help establish a relationship between Giant African Land Snail (Achatina Fulica) solution extract, its shell chemical properties, and farmer’s perceptions of biological control in eThekwini Municipality Agri-hubs. A mixed design approach to collecting data will be explored using a trial layout in the field and through interviews. The experimental area will be explored using a split-plot design that will be replicated and arranged in a randomised complete block design. The split-plot will have 0, 10, 20 and 30 liters of water to one liter of snail solution extract. Plots were 50 m² each with a spacing of 12 m between each plot and a plant spacing of 0.5 m (inter-row) ‘and 0.5 m (intra-row). Trials will be irrigated using sprinkler irrigation, with objective two being added to the mix every 4-5 days. The expected outcome will be improved soil fertility and micro-organisms population proliferation.

Keywords: giant african land snail, integrated pest management, photosynthesis, genetic rodenticide resistance, control future pests, shell chemical properties

Procedia PDF Downloads 86

Authors: Seyong Jeong, Jinmo Park, Jinhyoun Park, Boram Kim, Kyoungsoo Ahn

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Energy management in the maritime industry is being required by economics and in conformity with new legislative actions taken by the International Maritime Organization (IMO) and the European Union (EU). In response, the various performance monitoring methodologies and data collection practices have been examined by different stakeholders. While many assorted advancements in operation and technology are applicable, their adoption in the shipping industry stays small. This slow uptake can be considered due to many different barriers such as data analysis problems, misreported data, and feedback problems, etc. This study presents a conceptual design of an energy management system (EMS) and proposes the methodology to resolve the limitations (e.g., data normalization using naval architectural evaluation, management of misrepresented data, and feedback from shore to ship through management of performance analysis history). We expect this system to make even short-term charterers assess the ship performance properly and implement sustainable fleet control.

Keywords: data normalization, energy management system, naval architectural evaluation, ship performance analysis

Procedia PDF Downloads 436

Authors: Kadi-Hanifi Halima

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Stipa tenacissima is a plant with a big ecological value (against desertification) and economical stake (paper industry). It is important by its pastoral value due to the inflorescence. It occupied large areas between the Tellian atlas and the Saharian atlas, at the present, these areas of alfa have regressed a lot. This regression is estimated at 1% per year. The principal cause is a human responsibility. The drought is just an aggravating circumstance. The eradication of such a kind of species will have serious consequences upon the equilibrium of all the steppic ecosystem. Thus, we have thought necessary and urgent to know the alfa ecosystem, under all its aspects (climatic, floristic, and edaphic), this diagnostic could direct the fight actions against desertification

Keywords: desertification, anthropic action, soils, Stipa tenacissima

Procedia PDF Downloads 297

Authors: Benaliouche Hana, Abdessemed Djamal, Meniai Abdessalem, Lesage Geoffroy, Heran Marc

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This paper presents a dynamic mathematical model of activated sludge which is able to predict the formation and degradation kinetics of SMP (Soluble microbial products) in membrane bioreactor systems. The model is based on a calibrated version of ASM1 with the theory of production and degradation of SMP. The model was calibrated on the experimental data from MBR (Mathematical modeling Membrane bioreactor) pilot plant. Analytical expressions have been developed, describing the concentrations of the main state variables present in the sludge matrix, with the inclusion of only six additional linear differential equations. The objective is to present a new dynamic mathematical model of activated sludge capable of predicting the formation and degradation kinetics of SMP (UAP and BAP) from the submerged membrane bioreactor (BRMI), operating at low organic load (C / N = 3.5), for two sludge retention times (SRT) fixed at 40 days and 60 days, to study their impact on membrane fouling, The modeling study was carried out under the steady-state condition. Analytical expressions were then validated by comparing their results with those obtained by simulations using GPS-X-Hydromantis software. These equations made it possible, by means of modeling approaches (ASM1), to identify the operating and kinetic parameters and help to predict membrane fouling.

Keywords: Activated Sludge Model No. 1 (ASM1), mathematical modeling membrane bioreactor, soluble microbial products, UAP, BAP, Modeling SMP, MBR, heterotrophic biomass

Procedia PDF Downloads 269

Authors: Shyam Parhi

Abstract:

Airports GIS program collects Airports data, validate and verify it, and stores it in specific database. Airports GIS allows authorized users to submit changes to airport data. The verified data is used to develop several engineering applications. One of these applications is electronic Airport Layout Plan (eALP) whose primary aim is to move from paper to digital form of ALP. The first phase of development of eALP was completed recently and it was tested for a few pilot program airports across different regions. We conducted gap analysis and noticed that a lot of development work is needed to fine tune at least six mandatory sheets of eALP. It is important to note that significant amount of programming is needed to move from out-of-box ArcGIS to a much customized ArcGIS which will be discussed. The ArcGIS viewer capability to display essential features like runway or taxiway or the perpendicular distance between them will be discussed. An enterprise level workflow which incorporates coordination process among different lines of business will be highlighted.

Keywords: geospatial data, geology, geographic information systems, aviation

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Authors: Jianwei Ma, Diriba Gemechu

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In seismic data processing, attenuation of random noise is the basic step to improve quality of data for further application of seismic data in exploration and development in different gas and oil industries. The signal-to-noise ratio of the data also highly determines quality of seismic data. This factor affects the reliability as well as the accuracy of seismic signal during interpretation for different purposes in different companies. To use seismic data for further application and interpretation, we need to improve the signal-to-noise ration while attenuating random noise effectively. To improve the signal-to-noise ration and attenuating seismic random noise by preserving important features and information about seismic signals, we introduce the concept of anisotropic total fractional order denoising algorithm. The anisotropic total fractional order variation model defined in fractional order bounded variation is proposed as a regularization in seismic denoising. The split Bregman algorithm is employed to solve the minimization problem of the anisotropic total fractional order variation model and the corresponding denoising algorithm for the proposed method is derived. We test the effectiveness of theproposed method for synthetic and real seismic data sets and the denoised result is compared with F-X deconvolution and non-local means denoising algorithm.

Keywords: anisotropic total fractional order variation, fractional order bounded variation, seismic random noise attenuation, split Bregman algorithm

Procedia PDF Downloads 197

Authors: Jun Su Ha

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Various advanced technologies will be adopted in Advanced Control Rooms (ACRs) of advanced Nuclear Power Plants (NPPs), which is thought to increase operators’ performance. However, potential human factors issues coupled with digital technologies might be troublesome. Human factors issues in ACRs are identified and strategies (or countermeasures) for evaluating and analyzing each of issues are addressed in this study.

Keywords: advanced control room, human factor issues, human performance, human error, nuclear power plant

Procedia PDF Downloads 453

Authors: Zelikha Labbani

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Since its creation in 1964 by Guha and Maheshwari in India on Datura innoxia Mill, in vitro androgenesis has become the method of choice in the production of doubled haploid in many species. However, in durum wheat, the Doubled haploid plant breeding programs remained limited due to the low production of androgenetic embryos and converting them into fertile green plants. We describe here an efficient method for inducing embryos and regenerating green plants directly from isolated microspores of durum wheat.

Keywords: durum wheat, haploid embryos, on in vitro, pretreatment

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Authors: Xinyan Zhang, Zhipeng Tan, Shan Fan

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The first layer of defense against data loss is the backup data. This paper implements an agent-based network backup system used the backup, server-storage and server-backup agent these tripartite construction, and we realize the snapshot and hierarchical index in the NSBS. It realizes the control command and data flow separation, balances the system load, thereby improving the efficiency of the system backup and recovery. The test results show the agent-based network backup system can effectively improve the task-based concurrency, reasonably allocate network bandwidth, the system backup performance loss costs smaller and improves data recovery efficiency by 20%.

Keywords: agent, network backup system, three architecture model, NSBS

Procedia PDF Downloads 441

Authors: Shelby Simpson, William Stanley, Namir Naba, Xiaodi Wang

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Both t-SNE and UMAP are brand new state of art tools to predominantly preserve the local structure that is to group neighboring data points together, which indeed provides a very informative visualization of heterogeneity in our data. In this research, we develop a t-SNE and UMAP base neural network image classification algorithm to embed the original dataset to a corresponding low dimensional dataset as a preprocessing step, then use this embedded database as input to our specially designed neural network classifier for image classification. We use the fashion MNIST data set, which is a labeled data set of images of clothing objects in our experiments. t-SNE and UMAP are used for dimensionality reduction of the data set and thus produce low dimensional embeddings. Furthermore, we use the embeddings from t-SNE and UMAP to feed into two neural networks. The accuracy of the models from the two neural networks is then compared to a dense neural network that does not use embedding as an input to show which model can classify the images of clothing objects more accurately.

Keywords: t-SNE, UMAP, fashion MNIST, neural networks

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Authors: Duygu Dere, Mert Ergeneci, Kaan Gokcesu

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Google Trends data has gained increasing popularity in the applications of behavioral finance, decision science and risk management. Because of Google’s wide range of use, the Trends statistics provide significant information about the investor sentiment and intention, which can be used as decisive factors for corporate and risk management fields. However, an anomaly, a significant increase or decrease, in a certain query cannot be detected by the state of the art applications of computation due to the random baseline noise of the Trends data, which is modelled as an Additive white Gaussian noise (AWGN). Since through time, the baseline noise power shows a gradual change an adaptive thresholding method is required to track and learn the baseline noise for a correct classification. To this end, we introduce an online method to classify meaningful deviations in Google Trends data. Through extensive experiments, we demonstrate that our method can successfully classify various anomalies for plenty of different data.

Keywords: adaptive data processing, behavioral finance , convex optimization, online learning, soft minimum thresholding

Procedia PDF Downloads 149

Authors: Leila Tabrizi, Farnaz Dezhaboun

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In order to study the effect of plant nutrient source and different drying methods on physical and phytochemical characteristics of pot marigold (Calendula officinalis L., Asteraceae) flowers, a factorial experiment was conducted based on completely randomized design with three replications in Research Laboratory of University of Tehran in 2010. Different nutrient sources (vermicompost, municipal waste compost, cattle manure, mushroom compost and control) which were applied in a field experiment for flower production and different drying methods including microwave (300, 600 and 900 W), oven (60, 70 and 80oC) and natural-shade drying in room temperature, were tested. Criteria such as drying kinetic, antioxidant activity, total flavonoid content, total phenolic compounds and total carotenoid of flowers were evaluated. Results indicated that organic inputs as nutrient source for flowers had no significant effects on quality criteria of pot marigold except of total flavonoid content, while drying methods significantly affected phytochemical criteria. Application of microwave 300, 600 and 900 W resulted in the highest amount of total flavonoid content, total phenolic compounds and antioxidant activity, respectively, while oven drying caused the lowest amount of phytochemical criteria. Also, interaction effect of nutrient source and drying method significantly affected antioxidant activity in which the highest amount of antioxidant activity was obtained in combination of vermicompost and microwave 900 W. In addition, application of vermicompost combined with oven drying at 60oC caused the lowest amount of antioxidant activity. Based on results of drying trend, microwave drying showed a faster drying rate than those oven and natural-shade drying in which by increasing microwave power and oven temperature, time of flower drying decreased whereas slope of moisture content reduction curve showed accelerated trend.

Keywords: drying kinetic, medicinal plant, organic fertilizer, phytochemical criteria

Procedia PDF Downloads 324

Authors: E. Karakulak, R. Yamanoğlu, M. Zeren

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In the current study, fly ash obtained from a thermal power plant was used as reinforcement in pure magnesium. The composite materials with different fly ash contents were produced with powder metallurgical methods. Powder mixtures were sintered at 540^oC under 30 MPa pressure for 15 minutes in a vacuum assisted hot press. Results showed that increasing ash content continuously increases hardness of the composite. On the other hand, minimum wear damage was obtained at 2 wt. % ash content. Addition of higher level of fly ash results with formation of cracks in the matrix and increases wear damage of the material.

Keywords: Mg composite, fly ash, wear, powder metallurgy

Procedia PDF Downloads 353

Authors: M. Masroor Akhtar Khan, Moin Uddin, Lalit Varshney

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Recently, there has been a rapidly expanding interest in finding applications of natural polymers in view of value addition to agriculture. It is now being realized that radiation processing of natural polysaccharides can be beneficially utilized either to improve the existing methodologies used for processing the natural polymers or to impart value addition to agriculture by converting them into more useful form. Gamma-ray irradiation is employed to degrade and lower the molecular weight of some of the natural polysaccharides like alginates, chitosan and carrageenan into small sized oligomers. When these oligomers are applied to plants as foliar sprays, they elicit various kinds of biological and physiological activities, including promotion of plant growth, seed germination, shoot elongation, root growth, flower production, suppression of heavy metal stress, etc. Furthermore, application of these oligomers can shorten the harvesting period of various crops and help in reducing the use of insecticides and chemical fertilizers. In recent years, the oligomers of sodium alginate obtained by irradiating the latter with gamma-rays at 520 kGy dose are being employed. It was noticed that the oligomers derived from the natural polysaccharides could induce growth, photosynthetic efficiency, enzyme activities and most importantly the production of secondary metabolite in the plants like Artemisia annua, Beta vulgaris, Catharanthus roseus, Chrysopogon zizanioides, Cymbopogon flexuosus, Eucalyptus citriodora, Foeniculum vulgare, Geranium sp., Mentha arvensis, Mentha citrata, Mentha piperita, Mentha virdis, Papaver somniferum and Trigonella foenum-graecum. As a result of the application of these oligomers, the yield and/or contents of the active constituents of the aforesaid plants were significantly enhanced. The productivity, as well as quality of medicinal and aromatic plants, may be ameliorated by this novel technique in an economical way as a very little quantity of these irradiated (depolymerised) polysaccharides is needed. Further, this is a very safe technique, as we did not expose the plants directly to radiation. The radiation was used to depolymerize the polysaccharides into oligomers.

Keywords: essential oil, medicinal and aromatic plants, plant production, radiation processed polysaccharides, active constituents

Procedia PDF Downloads 432

Authors: Chuanbo Xu, Xinying Li, Gejirifu De, Yunna Wu

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Energy Internet (EI) is a new form that deeply integrates the Internet and the entire energy process from production to consumption. The assessment of energy efficient performance is of vital importance for the long-term sustainable development of EI project. Although the newly proposed fuzzy integrated cloud evaluation algorithm considers the randomness of uncertainty, it relies too much on the experience and knowledge of experts. Fortunately, the enrichment of EI data has enabled the utilization of data-driven methods. Therefore, the main purpose of this work is to assess the energy efficient of park-level EI by using a combination of a data-driven method with the fuzzy integrated cloud evaluation algorithm. Firstly, the indicators for the energy efficient are identified through literature review. Secondly, the artificial neural network (ANN)-based data-driven method is employed to cluster the values of indicators. Thirdly, the energy efficient of EI project is calculated through the fuzzy integrated cloud evaluation algorithm. Finally, the applicability of the proposed method is demonstrated by a case study.

Keywords: energy efficient, energy internet, data-driven, fuzzy integrated evaluation, cloud model

Procedia PDF Downloads 182

Authors: Gabriele Borg, Alexei Debono, Charlie Abela

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There on a constant rise in the availability of high volumes of data gathered from multiple sources, resulting in an abundance of unprocessed information that can be used to monitor patterns and trends in user behaviour. Similarly, year after year, Malta is also constantly experiencing ongoing population growth and an increase in mobilization demand. This research takes advantage of data which is continuously being sourced and converting it into useful information related to the traffic problem on the Maltese roads. The scope of this paper is to provide a methodology to create a custom dataset (MalTra - Malta Traffic) compiled from multiple participants from various locations across the island to identify the most common routes taken to expose the main areas of activity. This use of big data is seen being used in various technologies and is referred to as ITSs (Intelligent Transportation Systems), which has been concluded that there is significant potential in utilising such sources of data on a nationwide scale. Furthermore, a series of traffic prediction graph neural network models are conducted to compare MalTra to large-scale traffic datasets.

Keywords: graph neural networks, traffic management, big data, mobile data patterns

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Authors: Farouk Lawan Gambo, Hamada Mohammad

Abstract:

Data compression shrinks files into fewer bits than their original presentation. It has more advantage on the internet because the smaller a file, the faster it can be transferred but learning most of the concepts in data compression are abstract in nature, therefore, making them difficult to digest by some students (engineers in particular). This paper studies the learning preference of engineering students who tend to have strong, active, sensing, visual and sequential learning preferences, the paper also studies the three shift of technology-aided that learning has experienced, which mobile learning has been considered to be the feature of learning that will integrate other form of the education process. Lastly, we propose a design and implementation of mobile learning application using software engineering methodology that will enhance the traditional teaching and learning of data compression techniques.

Keywords: data compression, learning preference, mobile learning, multimedia

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Authors: Paramjit Mahey, Monica Sharma, Jasbinder Singh

Abstract:

Fiber based access networks can deliver performance that can support the increasing demands for high speed connections. One of the new technologies that have emerged in recent years is Passive Optical Networks. This paper is targeted to show the simultaneous delivery of triple play service (data, voice and video). The comparative investigation and suitability of various data rates is presented. It is demonstrated that as we increase the data rate, number of users to be accommodated decreases due to increase in bit error rate.

Keywords: BER, PON, TDMPON, GPON, CWDM, OLT, ONT

Procedia PDF Downloads 525