Search results for: carbon nanotubes network

Authors: Abdulrahman Abdulhamid Arabo, Raji Arabi Bamanga, Mujiburrahman Fadilu, Musa Abubakar, Fatima Abdullahi Shehu, Hafeez Muhammad Yakasai, Nasiru Abdullahi

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The aim of this study was to isolate and identify biosurfactant-producing and diesel alkanes degrading bacteria. For this reason, bacteria isolated from the diesel-contaminated site were screened for their potential to produce biosurfactants and degrade diesel alkanes. Primary selection of diesel degraders was carried out by using the conventional enrichment culture technique, where 12 bacterial strains were isolated based on their ability to grow on minimal media supplemented with diesel as the sole carbon source, which was followed by qualitative screening methods for potential biosurfactant production. Isolate B11 was the only candidate that showed positive signs for drop collapse, foaming, hemolytic test, oil displacement of more than 22 ± 0.05 mm, and emulsification (E24) of 14 ± 0.30%. The effect of various culture parameters (incubation time, diesel concentration, nitrogen source, pH and temperature) on the biodegradation of diesel was evaluated. The optimum incubation time was confirmed to be 120 days for isolate B11, and the optimum PH was confirmed as 8.0 for the isolate; similarly, the optimum temperature was confirmed as 35oC. In addition, diesel oil was used as the sole carbon source for the isolates. The favorable diesel concentration was 12.5 % (v/v) for the isolate. The isolate has shown degradative ability towards Tridecane (C13), dodecane, 2, 6, 10-trimethyl- (C15), Tetradecane (C14), 2,6,10-Trimethyltridecane (C16), Pentadecane (C15). It degraded between 0.27% - 9.65% of individual diesel oil alkanes. The strain has exhibited the potential of degrading diesel oil n-alkanes and was identified as Alcaligenes species strain B11 (MZ027604) using the 16S rRNA. Sequencing.

Keywords: diesel oil, biosurfactant, Alcaligenes sp, biodegradation

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Authors: Anil Kumar Pandey

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A compact planar monopole antenna with dual-band operation suitable for wireless local area network (WLAN) application is presented in this paper. The antenna occupies an overall area of 18 ×12 mm². The antenna is fed by a coplanar waveguide (CPW) transmission line and it combines two folded strips, which radiates at 2.4 and 5.2 GHz. In the proposed antenna, by optimally selecting the antenna dimensions, dual-band resonant modes with a much wider impedance matching at the higher band can be produced. Prototypes of the obtained optimized design have been simulated using EM solver. The simulated results explore good dual-band operation with -10 dB impedance bandwidths of 50 MHz and 2400 MHz at bands of 2.4 and 5.2 GHz, respectively, which cover the 2.4/5.2/5.8 GHz WLAN operating bands. Good antenna performances such as radiation patterns and antenna gains over the operating bands have also been observed. The antenna with a compact size of 18×12×1.6 mm³ is designed on an FR4 substrate with a dielectric constant of 4.4.

Keywords: CPW antenna, dual-band, electromagnetic simulation, wireless local area network (WLAN)

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Authors: Jinhoa Lee

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The relationships between environmental quality, energy use and economic output have created growing attention over the past decades among researchers and policy makers. Focusing on the empirical aspects of the role of CO2 emissions and energy use in affecting the economic output, this paper is an effort to fulfil the gap in a comprehensive case study at a country level using modern econometric techniques. To achieve the goal, this country-specific study examines the short-run and long-run relationships among energy consumption, carbon dioxide (CO2) emissions and gross domestic product (GDP) for Oman using time series analysis from the year 1980-2010. To investigate the relationships between the variables, this paper employs the Augmented Dickey Fuller (ADF) test for stationary, Johansen maximum likelihood method for co-integration and a Vector Error Correction Model (VECM) for both short- and long-run causality among the research variables for the sample. All the variables in this study show very strong significant effects on GDP in the country for the long term. The long-run equilibrium in the VECM suggests positive long-run causalities from CO2 emissions to GDP. Conversely, negative impacts of energy consumption on GDP are found to be significant in Oman during the period. In the short run, there exist negative unidirectional causalities among GDP, CO2 emissions and energy consumption running from GDP to CO2 emissions and from energy consumption to CO2 emissions. Overall, the results support arguments that there are relationships among environmental quality, energy use and economic output in Oman over of period 1980-2010.

Keywords: CO2 emissions, energy consumption, GDP, Oman, time series analysis

Procedia PDF Downloads 463

Authors: Tsega Asresa, Getahun Tigistu, Melaku Bayih

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Computer vision is a subfield of artificial intelligence that allows computers and systems to extract meaning from digital images and video. It is used in a wide range of fields of study, including self-driving cars, video surveillance, medical diagnosis, manufacturing, law, agriculture, quality control, health care, facial recognition, and military applications. Aromatic medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, essential oils, decoration, cleaning, and other natural health products for therapeutic and Aromatic culinary purposes. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs but also going to export for valuable foreign currency exchange. In Ethiopia, there is a lack of technologies for the classification and identification of Aromatic medicinal plant parts and disease type cured by aromatic medicinal plants. Farmers, industry personnel, academicians, and pharmacists find it difficult to identify plant parts and disease types cured by plants before ingredient extraction in the laboratory. Manual plant identification is a time-consuming, labor-intensive, and lengthy process. To alleviate these challenges, few studies have been conducted in the area to address these issues. One way to overcome these problems is to develop a deep learning model for efficient identification of Aromatic medicinal plant parts with their corresponding disease type. The objective of the proposed study is to identify the aromatic medicinal plant parts and their disease type classification using computer vision technology. Therefore, this research initiated a model for the classification of aromatic medicinal plant parts and their disease type by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides roots, flowers, fruits, and latex. For this study, the researcher used RGB leaf images with a size of 128x128 x3. In this study, the researchers trained five cutting-edge models: convolutional neural network, Inception V3, Residual Neural Network, Mobile Network, and Visual Geometry Group. Those models were chosen after a comprehensive review of the best-performing models. The 80/20 percentage split is used to evaluate the model, and classification metrics are used to compare models. The pre-trained Inception V3 model outperforms well, with training and validation accuracy of 99.8% and 98.7%, respectively.

Keywords: aromatic medicinal plant, computer vision, convolutional neural network, deep learning, plant classification, residual neural network

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Authors: Mohamed Mokhtar, Mostafa F. Shaaban

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Among the challenges facing solar photovoltaic (PV) systems in the United Arab Emirates (UAE), dust accumulation on solar panels is considered the most severe problem that faces the growth of solar power plants. The accumulation of dust on the solar panels significantly degrades output from these panels. Hence, solar PV panels have to be cleaned manually or using costly automated cleaning methods. This paper focuses on initiating cleaning actions when required to reduce maintenance costs. The cleaning actions are triggered only when the dust level exceeds a threshold value. The amount of dust accumulated on the PV panels is estimated using an artificial neural network (ANN). Experiments are conducted to collect the required data, which are used in the training of the ANN model. Then, this ANN model will be fed by the output power from solar panels, ambient temperature, and solar irradiance, and thus, it will be able to estimate the amount of dust accumulated on solar panels at these conditions. The model was tested on different case studies to confirm the accuracy of the developed model.

Keywords: machine learning, dust, PV panels, renewable energy

Procedia PDF Downloads 146

Authors: Ali Safari, Pejman Hosseiniun, Iman Rahbari, MohamadReza Kalhor

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The semiconductor industry is placing an increased emphasis on emerging materials and devices that may provide improved performance, or provide novel functionality for devices. Recently, graphene, as a true two-dimensional carbon material, has shown fascinating applications in electronics. In this paper detailed discussions are introduced for possible applications of grapheme Transistor in RF and digital devices.

Keywords: graphene, GFET, RF, digital

Procedia PDF Downloads 362

Authors: Mathieu Bourdeau, Philippe Basset, Julien Waeytens, Elyes Nefzaoui

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As residential buildings account for a third of the overall energy consumption and greenhouse gas emissions in Europe, building energy modeling is an essential tool to reach energy efficiency goals. In the energy modeling process, calibration is a mandatory step to obtain accurate and reliable energy simulations. Nevertheless, the comparison between simulation results and the actual building energy behavior often highlights a significant performance gap. The literature discusses different origins of energy performance gaps, from building design to building operation. Then, building operation description in energy models, especially energy usages and users’ behavior, plays an important role in the reliability of simulations but is also the most accessible target for post-occupancy energy management and optimization. Therefore, the present study aims to discuss results on the calibration ofresidential building energy models using real operation data. Data are collected through a sensor network of more than 180 sensors and advanced energy meters deployed in three collective residential buildings undergoing major retrofit actions. The sensor network is implemented at building scale and in an eight-apartment sample. Data are collected for over one year and half and coverbuilding energy behavior – thermal and electricity, indoor environment, inhabitants’ comfort, occupancy, occupants behavior and energy uses, and local weather. Building energy simulations are performed using a physics-based building energy modeling software (Pleaides software), where the buildings’features are implemented according to the buildingsthermal regulation code compliance study and the retrofit project technical files. Sensitivity analyses are performed to highlight the most energy-driving building features regarding each end-use. These features are then compared with the collected post-occupancy data. Energy-driving features are progressively replaced with field data for a step-by-step calibration of the energy model. Results of this study provide an analysis of energy performance gap on an existing residential case study under deep retrofit actions. It highlights the impact of the different building features on the energy behavior and the performance gap in this context, such as temperature setpoints, indoor occupancy, the building envelopeproperties but also domestic hot water usage or heat gains from electric appliances. The benefits of inputting field data from an extensive instrumentation campaign instead of standardized scenarios are also described. Finally, the exhaustive instrumentation solution provides useful insights on the needs, advantages, and shortcomings of the implemented sensor network for its replicability on a larger scale and for different use cases.

Keywords: calibration, building energy modeling, performance gap, sensor network

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Authors: Daniel Olshansky, Ramiro Rodrıguez Colmeiro

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Relay Mining presents a scalable solution employing probabilistic mechanisms and crypto-economic incentives to estimate RPC volume usage, facilitating decentralized multitenant rate limiting. Network traffic from individual applications can be concurrently serviced by multiple RPC service providers, with costs, rewards, and rate limiting governed by a native cryptocurrency on a distributed ledger. Building upon established research in token bucket algorithms and distributed rate-limiting penalty models, our approach harnesses a feedback loop control mechanism to adjust the difficulty of mining relay rewards, dynamically scaling with network usage growth. By leveraging crypto-economic incentives, we reduce coordination overhead costs and introduce a mechanism for providing RPC services that are both geopolitically and geographically distributed.

Keywords: remote procedure call, crypto-economic, commit-reveal, decentralization, scalability, blockchain, rate limiting, token bucket

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Authors: Amin Ranjbar, Amir Arsalan Jalili Zolfaghari, Amir Abolfazl Suratgar, Mehrdad Khajavi

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This paper describes two stages of learning-based fault detection procedure in alternators. The procedure consists of three states of machine condition namely shortened brush, high impedance relay and maintaining a healthy condition in the alternator. The fault detection algorithm uses Wigner-Ville distribution as a feature extractor and also appropriate feature classifier. In this work, ANN (Artificial Neural Network) and also SVM (support vector machine) were compared to determine more suitable performance evaluated by the mean squared of errors criteria. Modules work together to detect possible faulty conditions of machines working. To test the method performance, a signal database is prepared by making different conditions on a laboratory setup. Therefore, it seems by implementing this method, satisfactory results are achieved.

Keywords: alternator, artificial neural network, support vector machine, time-frequency analysis, Wigner-Ville distribution

Procedia PDF Downloads 375

Authors: In-Lee Choi, Sung Mi Hong, Min Jae Jeong, Jun Pill Baek, Ho-Min Kang

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Gomchwi (Ligularia fischeri) is grown in the wetland of the deep mountains in Korea and East Asia and has properties that are, inflammation control, whitening, antimutagenic and antigenotoxic. Jangajji is a type of pickle in Korean fermented food which is made by pickling or marinating vegetables in a sauce, such as soy sauce, chili pepper paste, soybean paste, or diluted vinegar for a long period of time. Handaeri-gomchwi jangajii is generally packed a film that has very low or no gas permeability in the Korean domestic market, so packages have a risk of swelling or bursting as a result of internal gas generation during storage or sale This study was conducted to improve secure distribution of Handaeri-gomchwi (Ligularia fischeri var. spiciformis Nakai) Jangajji using laser ablation OTR (oxygen transmission rate) films. Handaeri-gomchwi cultivated in Yangu, Gangwon province, Republic of Korea (Ligularia fischeri var. spiciformis Nakai) was processed in to Jangajji using soy sauce. They were packed by different OTR films, and were stored for 90 days in 7℃(10,000 cc, 20,000 cc, 40,000 cc and 80,000 cc O2/m²• day • atm), 20 days in 20℃ (10,000 cc, 30,000 cc, 70,000 cc and 100,000 cc) and compared with the control film(PP film, 1,300cc). The fresh weight loss, carbon dioxide, oxygen, and ethylene concentrations of Handaeri–gomchwi packages were measured during storage. On the final day of storage, incidence rate of fungi, pH, salinity, firmness, and off-flavor were measured. The fresh weight loss rate of Handaeri–gomchwi was less than 2.0% in 10,000cc OTR films at two different storage periods and temperatures. At 80,000cc(7℃) and 100,000cc(20℃), carbon dioxide contents were 2.0% and 6.4% respectively, whereas the control treatment had the highest concentration. Which was 35%(20℃) and 15%(7℃) , that resulted the packages to swell during storage. The control treatment Showed the lowest oxygen concentration at 2.5% in 7℃ and 0.8% in 20℃. Packages in 7℃ (0.3-1.7μL/L) showed very lower ethylene concentration than in 20℃(10-25μL/L), they also had no significant relation. On the final storage day, fungi were found in every film at both temperatures, except the 10,000cc, as oxygen permeability increased so did the pH, while the salinity decreased. Firmness and off-flavor Showed the best results at 10,000cc in both temperatures best result at 10,000cc in both temperature. Following the results, 10,000cc film is the most reasonable treat in storing Handaeri–gomchwi. For it had a suitable oxygen transmission rate, which prevents billowing, and maintained good qualities in both temperatures.

Keywords: carbon dioxide, Korean pickle, marketable, oxygen

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Authors: Y. Muto, S. Araki, H. Yamamoto

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The separation of oxygen is one key technology to improve the efficiency and to reduce the cost for the processed of the partial oxidation of the methane and the condensation of the carbon dioxide. Particularly, carbon dioxide at high concentration would be obtained by the combustion using pure oxygen separated from air. However, the oxygen separation process occupied the large part of energy consumption. Therefore, it is considered that the membrane technologies enable to separation at lower cost and lower energy consumption than conventional methods. In this study, it is examined that the separation of oxygen using membranes of mixed conductors. Oxygen permeation through the membrane is occurred by the following three processes. At first, the oxygen molecules dissociate into oxygen ion at feed side of the membrane, subsequently, oxygen ions diffuse in the membrane. Finally, oxygen ions recombine to form the oxygen molecule. Therefore, it is expected that the membrane of thickness and material, or catalysts of the dissociation and recombination affect the membrane performance. However, there is little article about catalysts for the dissociation and recombination. We confirmed the performance of La0.6Sr0.4Co1.0O3-δ (LSC) based catalyst which was commonly used as the dissociation and recombination. It is known that the adsorbed amount of oxygen increase with the increase of doped Fe content in B site of LSC. We prepared the catalysts of La0.1Sr0.9Co0.9Fe0.1O3-δ(C9F1), La0.1Sr0.9Co0.5Fe0.5O3-δ(C5F5) and La0.1Sr0.9Co0.3Fe0.7O3-δ(C7F3). Also, we used Pr2NiO4 type mixed conductor as a membrane material. (Pr0.9La0.1)2(Ni0.74Cu0.21Ga0.05)O4+δ(PLNCG) shows the high oxygen permeability and the stability against carbon dioxide. Oxygen permeation experiments were carried out using a homemade apparatus at 850 -975 °C. The membrane was sealed with Pyrex glass at both end of the outside dense alumina tubes. To measure the oxygen permeation rate, air was fed to the film side at 50 ml min-1, helium as the sweep gas and reference gas was fed at 20 ml min-1. The flow rates of the sweep gas and the gas permeated through the membrane were measured using flow meter and the gas concentrations were determined using a gas chromatograph. Then, the permeance of the oxygen was determined using the flow rate and the concentration of the gas on the permeate side of the membrane. The increase of oxygen permeation was observed with increasing temperature. It is considered that this is due to the catalytic activities are increased with increasing temperature. Another reason is the increase of oxygen diffusivity in the bulk of membrane. The oxygen permeation rate is improved by using catalyst of LSC or LSCF. The oxygen permeation rate of membrane with LSCF showed higher than that of membrane with LSC. Furthermore, in LSCF catalysts, oxygen permeation rate increased with the increase of the doped amount of Fe. It is considered that this is caused by the increased of adsorbed amount of oxygen.

Keywords: membrane separation, oxygen permeation, K2NiF4-type structure, mixed conductor

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Authors: Svenja Pieritz, Jakab Pilaszanovich

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Collaborative Problem Solving (CPS) is recognized as being one of the most important skills of the 21st century with having a potential impact on education, job selection, and collaborative systems design. Therefore, CPS has been adopted in several standardized tests, including the Programme for International Student Assessment (PISA) in 2015. A significant challenge of evaluating CPS is the underlying interplay of cognitive and social skills, which requires a more holistic assessment. However, the majority of the existing tests are using a questionnaire-based assessment, which oversimplifies this interplay and undermines ecological validity. Two major difficulties were identified: Firstly, the creation of a controllable, real-time environment allowing natural behaviors and communication between at least two people. Secondly, the development of an appropriate method to collect and synthesize both cognitive and social metrics of collaboration. This paper proposes a more holistic and automated approach to the assessment of CPS. To address these two difficulties, a multiplayer problem-solving game called Sea of Light was developed: An environment allowing students to deploy a variety of measurable collaborative strategies. This controlled environment enables researchers to monitor behavior through the analysis of game actions and chat. The according solution for the statistical model is a combined approach of Natural Language Processing (NLP) and Bayesian network analysis. Social exchanges via the in-game chat are analyzed through NLP and fed into the Bayesian network along with other game actions. This Bayesian network synthesizes evidence to track and update different subdimensions of CPS. Major findings focus on the correlations between the evidences collected through in- game actions, the participants’ chat features and the CPS self- evaluation metrics. These results give an indication of which game mechanics can best describe CPS evaluation. Overall, Sea of Light gives test administrators control over different problem-solving scenarios and difficulties while keeping the student engaged. It enables a more complete assessment based on complex, socio-cognitive information on actions and communication. This tool permits further investigations of the effects of group constellations and personality in collaborative problem-solving.

Keywords: bayesian network, collaborative problem solving, game-based assessment, natural language processing

Procedia PDF Downloads 133

Authors: Murray Matarab Konzang

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Lae Port Development Project, Four Lane Highway and other development in the extraction industry which have direct road link to Lae City are predicted to have significant impact on its road network system. This paper evaluates Lae roads improvement program with forecast on planning, economic and the installation of bypasses to ease congestion, effective and convenient transport service for bulk goods and reduce travel time. Land-use transportation study and plans for local area traffic management scheme will be considered. City roads are faced with increased number of traffic and some inadequate road pavement width, poor transport plans, and facilities to meet this transportation demand. Lae also has drainage system which might not hold a 100 year flood. Proper evaluation, plan, design and intersection analysis is needed to evaluate road network system thus recommend improvement and estimate future growth. Repetitive and cyclic loading by heavy commercial vehicles with different axle configurations apply on the flexible pavement which weakens and tear the pavement surface thus small cracks occur. Rain water seeps through and overtime it creates potholes. Effective planning starts from experimental research and appropriate design standards to enable firm embankment, proper drains and quality pavement material. This paper will address traffic problems as well as road pavement, capacities of intersections, and pedestrian flow during peak hours. The outcome of this research will be to identify heavily trafficked road sections and recommend treatments to reduce traffic congestions, road classification, and proposal for bypass routes and improvement. First part of this study will describe transport or traffic related problems within the city. Second part would be to identify challenges imposed by traffic and road related problems and thirdly to recommend solutions after the analyzing traffic data that will indicate current capacities of road intersections and finally recommended treatment for improvement and future growth.

Keywords: Lae, road network, highway, vehicle traffic, planning

Procedia PDF Downloads 360

Authors: Oyebade K. Oyedotun

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The cardiotocogram is a technical recording of the heartbeat rate and uterine contractions of a fetus during pregnancy. During pregnancy, several complications can occur to both the mother and the fetus; hence it is very crucial that medical experts are able to find technical means to check the healthiness of the mother and especially the fetus. It is very important that the fetus develops as expected in stages during the pregnancy period; however, the task of monitoring the health status of the fetus is not that which is easily achieved as the fetus is not wholly physically available to medical experts for inspection. Hence, doctors have to resort to some other tests that can give an indication of the status of the fetus. One of such diagnostic test is to obtain cardiotocograms of the fetus. From the analysis of the cardiotocograms, medical experts can determine the status of the fetus, and therefore necessary medical interventions. Generally, medical experts classify examined cardiotocograms into ‘normal’, ‘suspect’, or ‘pathological’. This work presents an artificial neural network based decision support system which can filter cardiotocograms data, producing the corresponding statuses of the fetuses. The capability of artificial neural network to explore the cardiotocogram data and learn features that distinguish one class from the others has been exploited in this research. In this research, feedforward and radial basis neural networks were trained on a publicly available database to classify the processed cardiotocogram data into one of the three classes: ‘normal’, ‘suspect’, or ‘pathological’. Classification accuracies of 87.8% and 89.2% were achieved during the test phase of the trained network for the feedforward and radial basis neural networks respectively. It is the hope that while the system described in this work may not be a complete replacement for a medical expert in fetus status evaluation, it can significantly reinforce the confidence in medical diagnosis reached by experts.

Keywords: decision support, cardiotocogram, classification, neural networks

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Authors: Wennan Long, Yuhao Nie, Yunan Li, Adam Brandt

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To fight against climate change, California government issued the Senate Bill No. 100 (SB-100) in 2018 September, which aims at achieving a target of 100% renewable electricity by the end of 2045. A capacity expansion problem is solved in this case study using a binary quadratic programming model. The optimal locations and capacities of the potential renewable power plants (i.e., solar, wind, biomass, geothermal and hydropower), the phase-out schedule of existing fossil-based (nature gas) power plants and the transmission of electricity across the entire network are determined with the minimal total annualized cost measured by net present value (NPV). The results show that the renewable electricity contribution could increase to 85.9% by 2030 and reach 100% by 2035. Fossil-based power plants will be totally phased out around 2035 and solar and wind will finally become the most dominant renewable energy resource in California electricity mix.

Keywords: 100% renewable electricity, California, capacity expansion, mixed integer non-linear programming

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Authors: Sean T. S. Wei, Joy D. Van Nostrand, Annapoorna Maitrayee Ganeshram, Stephen B. Pointing

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McMurdo Dry Valleys are a largely ice-free polar desert protected by international treaty as an Antarctic special managed area. The terrestrial landscape is dominated by oligotrophic mineral soil with extensive rocky outcrops. Several environmental stresses: low temperature, lack of liquid water, UV exposure and oligotrophic substrates, restrict the major biotic component to microorganisms. The bacterial diversity and the putative physiological capacity of microbial communities of quartz rocks (hypoliths) and soil of a maritime-influenced Dry Valleys were interrogated by two metagenomic approaches: 454 pyro-sequencing and Geochp DNA microarray. The most abundant phylum in hypoliths was Cyanobacteria (46%), whereas in solils Actinobacteria (31%) were most abundant. The Proteobacteria and Bacteriodetes were the only other phyla to comprise >10% of both communities. Carbon fixation was indicated by photoautotrophic and chemoautotrophic pathways for both hypolith and soil communities. The fungi accounted for polymer carbon transformations, particularly for aromatic compounds. The complete nitrogen cycling was observed in both communities. The fungi in particular displayed pathways related to ammonification. Environmental stress response pathways were common among bacteria, whereas the nutrient stress response pathways were more widely present in bacteria, archaea and fungi. The diversity of bacterialphage was also surveyed by Geochip. Data suggested that different substrates supported different viral families: Leviviridae, Myoviridae, Podoviridae and Siphoviridiae were ubiquitous. However, Corticoviridae and Microviridae only occurred in wetter soils.

Keywords: Antarctica, hypolith, soil, dry valleys, geochip, functional diversity, stress response

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Authors: Dan Kanmegne

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Africa will become the most populated continent by the end of the century, with around 4 billion inhabitants. Food security and climate changes will become continental issues since agricultural practices depend on climate but also contribute to global emissions and land degradation. Agroforestry has been identified as a cost-efficient and reliable strategy to address these two issues. It is defined as the integrated management of trees and crops/animals in the same land unit. Agroforestry provides benefits in terms of goods (fruits, medicine, wood, etc.) and services (windbreaks, fertility, etc.), and is acknowledged to have a great potential for carbon sequestration; therefore it can be integrated into reduction mechanisms of carbon emissions. Particularly in sub-Saharan Africa, the constraint stands in the lack of information about both areas under agroforestry and the characterization (composition, structure, and management) of each agroforestry system at the country level. This study describes and quantifies “what is where?”, earliest to the quantification of carbon stock in different systems. Remote sensing (RS) is the most efficient approach to map such a dynamic technology as agroforestry since it gives relatively adequate and consistent information over a large area at nearly no cost. RS data fulfill the good practice guidelines of the Intergovernmental Panel On Climate Change (IPCC) that is to be used in carbon estimation. Satellite data are getting more and more accessible, and the archives are growing exponentially. To retrieve useful information to support decision-making out of this large amount of data, satellite data needs to be organized so to ensure fast processing, quick accessibility, and ease of use. A new solution is a data cube, which can be understood as a multi-dimensional stack (space, time, data type) of spatially aligned pixels and used for efficient access and analysis. A data cube for Burkina Faso has been set up from the cooperation project between the international service provider WASCAL and Germany, which provides an accessible exploitation architecture of multi-temporal satellite data. The aim of this study is to map and characterize agroforestry systems using the Burkina Faso earth observation data cube. The approach in its initial stage is based on an unsupervised image classification of a normalized difference vegetation index (NDVI) time series from 2010 to 2018, to stratify the country based on the vegetation. Fifteen strata were identified, and four samples per location were randomly assigned to define the sampling units. For safety reasons, the northern part will not be part of the fieldwork. A total of 52 locations will be visited by the end of the dry season in February-March 2020. The field campaigns will consist of identifying and describing different agroforestry systems and qualitative interviews. A multi-temporal supervised image classification will be done with a random forest algorithm, and the field data will be used for both training the algorithm and accuracy assessment. The expected outputs are (i) map(s) of agroforestry dynamics, (ii) characteristics of different systems (main species, management, area, etc.); (iii) assessment report of Burkina Faso data cube.

Keywords: agroforestry systems, Burkina Faso, earth observation data cube, multi-temporal image classification

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Authors: Noor Farizah Ibrahim, Christopher Durugbo

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This study aims to investigate how the Twittersphere reacted during the recent historical event of robotic landing on a comet. The news is about Philae, a robotic lander from European Space Agency (ESA), which successfully made the first-ever rendezvous and touchdown of its kind on a nucleus comet on November 12, 2014. In order to understand how Twitter is practically used in spreading messages on historical events, we conducted an analysis of one-week tweet feeds that contain the #CometLanding hashtag. We studied the trends of tweets, the diffusion of the information and the characteristics of the social network created. The results indicated that the use of Twitter as a platform enables online communities to engage and spread the historical event through social media network (e.g. tweets, retweets, mentions and replies). In addition, it was found that comprehensible and understandable hashtags could influence users to follow the same tweet stream compared to other laborious hashtags which were difficult to understand by users in online communities.

Keywords: diffusion of information, hashtag, social media, Twitter

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Authors: Yu Zhang, Ye Tian, Fang Ye Yixuan Kang

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The capacity of conventional cellular networks has reached its upper bound and it can be well handled by introducing femtocells with low-cost and easy-to-deploy. Spectrum interference issue becomes more critical in peace with the value-added multimedia services growing up increasingly in two-tier cellular networks. Spectrum allocation is one of effective methods in interference mitigation technology. This paper proposes a game-theory-based on OFDMA downlink spectrum allocation aiming at reducing co-channel interference in two-tier femtocell networks. The framework is formulated as a non-cooperative game, wherein the femto base stations are players and frequency channels available are strategies. The scheme takes full account of competitive behavior and fairness among stations. In addition, the utility function reflects the interference from the standpoint of channels essentially. This work focuses on co-channel interference and puts forward a negative logarithm interference function on distance weight ratio aiming at suppressing co-channel interference in the same layer network. This scenario is more suitable for actual network deployment and the system possesses high robustness. According to the proposed mechanism, interference exists only when players employ the same channel for data communication. This paper focuses on implementing spectrum allocation in a distributed fashion. Numerical results show that signal to interference and noise ratio can be obviously improved through the spectrum allocation scheme and the users quality of service in downlink can be satisfied. Besides, the average spectrum efficiency in cellular network can be significantly promoted as simulations results shown.

Keywords: femtocell networks, game theory, interference mitigation, spectrum allocation

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Authors: Jozef Mindas, Jaroslav Skvarenina, Jana Skvareninova

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Study of the effects of climate change on Norway Spruce (Picea abies) forests has mainly focused on the diversity of tree species diversity of tree species as a result of the ability of species to tolerate temperature and moisture changes as well as some effects of disturbance regime changes. The tree species’ diversity changes in spruce forests due to climate change have been analyzed via gap model. Forest gap model is a dynamic model for calculation basic characteristics of individual forest trees. Input ecological data for model calculations have been taken from the permanent research plots located in primeval forests in mountainous regions in Slovakia. The results of regional scenarios of the climatic change for the territory of Slovakia have been used, from which the values are according to the CGCM3.1 (global) model, KNMI and MPI (regional) models. Model results for conditions of the climate change scenarios suggest a shift of the upper forest limit to the region of the present subalpine zone, in supramontane zone. N. spruce representation will decrease at the expense of beech and precious broadleaved species (Acer sp., Sorbus sp., Fraxinus sp.). The most significant tree species diversity changes have been identified for the upper tree line and current belt of dwarf pine (Pinus mugo) occurrence. The results have been also discussed in relation to most important disturbances (wind storms, snow and ice storms) and phenological changes which consequences are little known. Special discussion is focused on biomass production changes in relation to carbon storage diversity in different carbon pools.

Keywords: biodiversity, climate change, Norway spruce forests, gap model

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Authors: Motahareh Fadaei

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Wireless sensor networks that are used to monitor a special environment, are formed from a large number of sensor nodes. The role of these sensors is to sense special parameters from ambient and to make connection. In these networks, the most important challenge is the management of energy usage. Clustering is one of the methods that are broadly used to face this challenge. In this paper, a distributed clustering protocol based on learning automata is proposed for underwater wireless sensor networks. The proposed algorithm that is called LA-Clustering forms clusters in the same energy level, based on the energy level of nodes and the connection radius regardless of size and the structure of sensor network. The proposed approach is simulated and is compared with some other protocols with considering some metrics such as network lifetime, number of alive nodes, and number of transmitted data. The simulation results demonstrate the efficiency of the proposed approach.

Keywords: clustering, energy consumption‎, learning automata, underwater sensor networks

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Authors: Jungyeol Hong, Dongjoo Park

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The primary purpose of this study is to develop a methodological framework to predict daily subway ridership at a station-level and to examine the association between subway ridership and bus demand incorporating commercial business facility in the vicinity of each subway station. The socio-economic characteristics, land-use, and built environment as factors may have an impact on subway ridership. However, it should be considered not only the endogenous relationship between bus and subway demand but also the characteristics of commercial business within a subway station’s sphere of influence, and integrated transit network topology. Regarding a statistical approach to estimate subway ridership at a station level, therefore it should be considered endogeneity and heteroscedastic issues which might have in the subway ridership prediction model. This study focused on both discovering the impacts of bus demand, commercial business characteristics, and network topology on subway ridership and developing more precise subway ridership estimation accounting for its statistical bias. The spatial scope of the study covers entire Seoul city in South Korea and includes 243 stations with the temporal scope set at twenty-four hours with one-hour interval time panels each. The data for subway and bus ridership was collected Seoul Smart Card data from 2015 and 2016. Three-Stage Least Square(3SLS) approach was applied to develop daily subway ridership model as capturing the endogeneity and heteroscedasticity between bus and subway demand. Independent variables incorporating in the modeling process were commercial business characteristics, social-economic characteristics, safety index, transit facility attributes, and dummies for seasons and time zone. As a result, it was found that bus ridership and subway ridership were endogenous each other and they had a significantly positive sign of coefficients which means one transit mode could increase another transportation mode’s ridership. In other words, two transit modes of subway and bus have a mutual relationship instead of the competitive relationship. The commercial business characteristics are the most critical dimension among the independent variables. The variables of commercial business facility rate in the paper containing six types; medical, educational, recreational, financial, food service, and shopping. From the model result, a higher rate in medical, financial buildings, shopping, and food service facility lead to increment of subway ridership at a station, while recreational and educational facility shows lower subway ridership. The complex network theory was applied for estimating integrated network topology measures that cover the entire Seoul transit network system, and a framework for seeking an impact on subway ridership. The centrality measures were found to be significant and showed a positive sign indicating higher centrality led to more subway ridership at a station level. The results of model accuracy tests by out of samples provided that 3SLS model has less mean square error rather than OLS and showed the methodological approach for the 3SLS model was plausible to estimate more accurate subway ridership. Acknowledgement: This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science and ICT (2017R1C1B2010175).

Keywords: subway ridership, bus ridership, commercial business characteristic, endogeneity, network topology

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Authors: Jinhoa Lee

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The relationships between environmental quality, energy use and economic output have created growing attention over the past decades among researchers and policy makers. Focusing on the empirical aspects of the role of carbon dioxide (CO2) emissions and energy use in affecting the economic output, this paper is an effort to fulfill the gap in a comprehensive case study at a country level using modern econometric techniques. To achieve the goal, this country-specific study examines the short-run and long-run relationships among energy consumption (using disaggregated energy sources: crude oil, coal, natural gas, and electricity), CO2 emissions and gross domestic product (GDP) for Turkey using time series analysis from the year 1980-2010. To investigate the relationships between the variables, this paper employs the Augmented Dickey-Fuller (ADF) test for stationarity, Johansen’s maximum likelihood method for cointegration and a Vector Error Correction Model (VECM) for both short- and long-run causality among the research variables for the sample. The long-run equilibrium in the VECM suggests no effects of the CO2 emissions and energy use on the GDP in Turkey. There exists a short-run bidirectional relationship between the electricity and natural gas consumption, and also there is a negative unidirectional causality running from the GDP to electricity use. Overall, the results partly support arguments that there are relationships between energy use and economic output; however, the effects may differ due to the source of energy such as in the case of Turkey for the period of 1980-2010. However, there is no significant relationship between the CO2 emissions and the GDP and between the CO2 emissions and the energy use both in the short term and long term.

Keywords: CO2 emissions, energy consumption, GDP, Turkey, time series analysis

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Authors: Grace C. Kuroki, Yixin Hu, Bailey Surtees, Rebecca Krimins, Nicholas J. Durr, Dara L. Kraitchman

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The purpose of this study was to perform a Phase I veterinary clinical trial with a low-cost, carbon-dioxide-based, passive thaw cryoablation device as proof-of-principle for application in pets and translation to third-world treatment of breast cancer. This study was approved by the institutional animal care and use committee. Client-owned dogs with subcutaneous masses, primarily lipomas or mammary cancers, were recruited for the study. Inclusion was based on clinical history, lesion location, preanesthetic blood work, and fine needle aspirate or biopsy confirmation of mass. Informed consent was obtained from the owners for dogs that met inclusion criteria. Ultrasound assessment of mass extent was performed immediately prior to mass cryoablation. Dogs were placed under general anesthesia and sterilely prepared. A stab incision was created to insert a custom 4.19 OD x 55.9 mm length cryoablation probe (Kubanda Cryotherapy) into the mass. Originally designed for treating breast cancer in low resource settings, this device has demonstrated potential in effectively necrosing subcutaneous masses. A dose escalation study of increasing freeze-thaw cycles (5/4/5, 7/5/7, and 10/7/10 min) was performed to assess the size of the iceball/necrotic extent of cryoablation. Each dog was allowed to recover for ~1-2 weeks before surgical removal of the mass. A single mass was treated in seven dogs (2 mammary masses, a sarcoma, 4 lipomas, and 1 adnexal mass) with most masses exceeding 2 cm in any dimension. Mass involution was most evident in the malignant mammary and adnexal mass. Lipomas showed minimal shrinkage prior to surgical removal, but an area of necrosis was evident along the cryoablation probe path. Gross assessment indicated a clear margin of cryoablation along the cryoprobe independent of tumor type. Detailed histopathology is pending, but complete involution of large lipomas appeared to be unlikely with a 10/7/10 protocol. The low-cost, carbon dioxide-based cryotherapy device permits a minimally invasive technique that may be useful for veterinary applications but is also informative of the unlikely resolution of benign adipose breast masses that may be encountered in third world countries.

Keywords: cryoablation, cryotherapy, interventional oncology, veterinary technology

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Authors: Sagir M. Yusuf, Chris Baber

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We describe issues bedeviling the coordination of heterogeneous (different sensors carrying agents) multi-agent missions such as belief conflict, situation reasoning, etc. We applied Bayesian and agents' presumptions inferential reasoning to solve the outlined issues with the heterogeneous multi-agent belief variation and situational-base reasoning. Bayesian Belief Network (BBN) was used in modeling the agents' belief conflict due to sensor variations. Simulation experiments were designed, and cases from agents’ missions were used in training the BBN using gradient descent and expectation-maximization algorithms. The output network is a well-trained BBN for making inferences for both agents and human experts. We claim that the Bayesian learning algorithm prediction capacity improves by the number of training data and argue that it enhances multi-agents robustness and solve agents’ sensor conflicts.

Keywords: distributed constraint optimization problem, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence

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Authors: L. S. Chathurika

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Early prediction of student performance is an important factor to be gained academic excellence. Whatever the study stream in secondary education, students lay the foundation for higher studies during the first year of their degree or diploma program in Sri Lanka. The information technology (IT) field has certain improvements in the education domain by selecting specialization areas to show the talents and skills of students. These specializations can be software engineering, network administration, database administration, multimedia design, etc. After completing the first-year, students attempt to select the best path by considering numerous factors. The purpose of this experiment is to predict the best study path using machine learning algorithms. Five classification algorithms: decision tree, support vector machine, artificial neural network, Naïve Bayes, and logistic regression are selected and tested. The support vector machine obtained the highest accuracy, 82.4%. Then affecting features are recognized to select the best study path.

Keywords: algorithm, classification, evaluation, features, testing, training

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Authors: Reza Behtash, Enayat Enayati

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The pool condenser is in principal a horizontal reactor, containing a bundle of U-tubes for heat exchange, coupling to low pressure steam drum. Condensation of gas takes place in a condensed pool around the tubes of the condenser. The heat of condensation is removed by the generation of low pressure steam on the inner tube side of the bundle. A circulation pump transfers ample boiler feed water to these tubes. The pressure of the steam generated influenced the heat flux. Changing the steam pressure means changing the steam condensate temperature and therefore the temperature difference between the tube side and the shell side. 2NH3 + CO2 ↔ NH2COONH4 + Heat. This reaction is exothermic and according to Le Chatelier's Principle if the heat is not removed enough, it will come back to left side and generate of the gas and so the Urea synthesis pressure will rise. The most principal reasons for high Urea synthesis pressure are non proportional of Ammonia/Dioxide Carbon ratio and too high a pressure in low pressure steam drum. Proportional of Ammonia/Dioxide Carbon ratio is 3.0 and normal pressure for low pressure steam drum is 4.5 bar. As regards these conditions were proportional but we could not control the synthesis pressure the plant endangered, therefore we had to control the steam drum pressure at about 3.5 bar. While we opened the pool condenser, we found the partition plate used to divide inlet and outlet boiler feed water to tubes, was broken partially and so amount of boiler feed water bypass the tubes and the heat was not removed totally and it resulted in the generation of gases and high pressure in synthesis.

Keywords: boiler, pressure, pool condenser, partition plate

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Authors: Kanya L. Khatri, Ashfaque A. Memon, Rod J. Smith, Shamas Bilal

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The viability and sustainability of crop production is currently threatened by increasing water scarcity. Water scarcity problems can be addressed through improved water productivity and the options usually presumed in this context are efficient water use and conversion of surface irrigation to pressurized systems. By replacing furrow irrigation with drip or centre pivot systems, the water efficiency can be improved by up to 30 to 45%. However, the installation and application of pumps and pipes, and the associated fuels needed for these alternatives increase energy consumption and cause significant greenhouse gas emissions. Hence, a balance between the improvement in water use and the potential increase in energy consumption is required keeping in view adverse impact of increased carbon emissions on the environment. When surface water is used, pressurized systems increase energy consumption substantially, by between 65% to 75%, and produce greenhouse gas emissions around 1.75 times higher than that of gravity based irrigation. With gravity based surface irrigation methods the energy consumption is assumed to be negligible. This study has shown that a novel real-time infiltration model REIP has enabled implementation of real-time optimization and control of surface irrigation and surface irrigation with real-time optimization has potential to bring significant improvements in irrigation performance along with substantial water savings of 2.92 ML/ha which is almost equivalent to that given by pressurized systems. Thus real-time optimization and control offers a modern, environment friendly and water efficient system with close to zero increase in energy consumption and minimal greenhouse gas emissions.

Keywords: pressurised irrigation, carbon emissions, real-time, environmentally-friendly, REIP

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Authors: Mohammad Yahaghifar

Abstract:

Nowadays Wireless system designers have been facing the continuously increasing demand for high data rates and mobility required by new wireless applications. Evolving future communication network generation cellular wireless networks are envisioned to overcome the fundamental challenges of existing cellular networks, for example, higher data rates, excellent end-to-end performance, and user coverage in hot-spots and crowded areas with lower latency,energy consumption and cost per information transfer. In this paper we propose a potential cellular architecture that separates indoor and outdoor scenarios and discuss various promising technologies for future wireless communication systemssystems, such as massive MIMO, energy-efficient communications,cognitive radio networks, and visible light communications and we disscuse about 5G that is next generation of wireless networks.

Keywords: future challenges in networks, cellur architecture, visible light communication, 5G wireless technologies, spatial modulation, massiva mimo, cognitive radio network, green communications

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Authors: Ho Yeon Park, Kyoung-Jae Kim

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The purpose of this study is to investigate whether friendship in social media can be an important factor in recommender system through social scientific analysis of friendship in popular social media such as Facebook and Twitter. For this purpose, this study analyzes data on friendship in real social media using component analysis and clique analysis among sub-group analysis in social network analysis. In this study, we propose an algorithm to reflect the results of sub-group analysis on the recommender system. The key to this algorithm is to ensure that recommendations from users in friendships are more likely to be reflected in recommendations from users. As a result of this study, outcomes of various subgroup analyzes were derived, and it was confirmed that the results were different from the results of the existing recommender system. Therefore, it is considered that the results of the subgroup analysis affect the recommendation performance of the system. Future research will attempt to generalize the results of the research through further analysis of various social data.

Keywords: sub-group analysis, social media, social network analysis, recommender systems

Procedia PDF Downloads 367