Search results for: hybrid fusion

Authors: D. Dannehl, Z. Taylor, J. Suhl, L. Miranda, R., Ulrichs, C., Salazar, E. Fitz-Rodriguez, I. Lopez-Cruz, A. Rojano-Aguilar, G. Navas-Gomez, U. Schmidt

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Growing environmental and sustainability concerns have driven continual modernization of horticultural practices, especially for urban farming. Controlled environment and soilless production methods are increasing in popularity because of their efficient resource use and intensive cropping capabilities. However, some popular substrates used for hydroponic cultivation, particularly rock wool, represent a large environmental burden in regard to their manufacture and disposal. Substrate-less hydroponic systems are effective in producing short cropping cycle plants such as lettuce or herbs, but less information is available for the production of plants with larger root-systems and longer cropping times. Here, we investigated the viability of a hybrid aeroponic/nutrient film technique (AP/NFT) system for the cultivation of greenhouse tomatoes (Solanum lycopersicum ‘Panovy’). The plants grown in the AP/NFT system had a more compact phenotype, accumulated more Na⁺ and less P and S than the rock wool grown counterparts. Due to forced irrigation interruptions, we propose that the differences observed were cofounded by the differing severity of water-stress for plants with and without substrate. They may also be caused by a higher root zone temperature predominant in plants exposed to AP/NFT. However, leaf area, stem diameter, and number of trusses did not differ significantly. The same was found for leaf pigments and plant photosynthetic efficiency. Overall, the AP/NFT system appears to be viable for the production of greenhouse tomato, enabling the environment to be relieved by way of lessening rock wool usage.

Keywords: closed aeroponic systems, fruit quality, nutrient dynamics, substrate waste reduction, urban farming systems, water savings

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Authors: Ermias A. Tegegn, Million Meshesha

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Human Immunodeficiency Virus and Acquired Immunodeficiency Syndrome (HIV/AIDS) is a major cause of death for most African countries. Ethiopia is one of the seriously affected countries in sub Saharan Africa. Previously in Ethiopia, having HIV/AIDS was almost equivalent to a death sentence. With the introduction of Antiretroviral Therapy (ART), HIV/AIDS has become chronic, but manageable disease. The study focused on a data mining technique to predict future living status of HIV/AIDS patients at the time of drug regimen change when the patients become toxic to the currently taking ART drug combination. The data is taken from University of Gondar Hospital ART program database. Hybrid methodology is followed to explore the application of data mining on ART program dataset. Data cleaning, handling missing values and data transformation were used for preprocessing the data. WEKA 3.7.9 data mining tools, classification algorithms, and expertise are utilized as means to address the research problem. By using four different classification algorithms, (i.e., J48 Classifier, PART rule induction, Naïve Bayes and Neural network) and by adjusting their parameters thirty-two models were built on the pre-processed University of Gondar ART program dataset. The performances of the models were evaluated using the standard metrics of accuracy, precision, recall, and F-measure. The most effective model to predict the status of HIV patients with drug regimen substitution is pruned J48 decision tree with a classification accuracy of 98.01%. This study extracts interesting attributes such as Ever taking Cotrim, Ever taking TbRx, CD4 count, Age, Weight, and Gender so as to predict the status of drug regimen substitution. The outcome of this study can be used as an assistant tool for the clinician to help them make more appropriate drug regimen substitution. Future research directions are forwarded to come up with an applicable system in the area of the study.

Keywords: HIV drug regimen, data mining, hybrid methodology, predictive model

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Authors: Terkimbi Vange, Obed Abimiku, Lateef Lekan Bello, Lucky Omoigui

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In 2014 and 2015, eight maize inbred lines resistant to Striga hermonthica (Del) Benth were crossed in 8 x 8 half diallel (Griffing method 11, model 1). The eight parent inbred lines were planted out in a Randomized Complete Block Design (RCBD) with three replications at two different Striga infested environments (Lafia and Makurdi) during the late cropping season. The objectives were to determine the combining ability of Striga resistant maize inbred lines and identify suitable inbreds for hybrids development. The lines were used to estimate general combining ability (GCA), and specific combining ability (SCA) effects for Striga related parameters such as Striga shoot counts, Striga damage rating (SDR), plant height and grain yield and other agronomic traits. The result of combined ANOVA revealed that mean squares were highly significant for all traits except Striga damage rating (SDR1) at 8WAS and Striga emergence count (STECOI) at 8WAS. Mean squares for SCA were significantly low for all traits. TZSTR190 was the highest yielding parent, and TZSTR166xTZST190 was the highest yielding hybrid (cross). Parent TZSTR166, TZEI188, TZSTR190 and TZSTR193 shows significant (p < 0.05) positive GCA effects for grain yield while the rest had negative GCA effects for grain yield. Parent TZSTR166, TZEI188, TZSTR190, and TZSTR193 could be used for initiating hybrid development. Also, TZSTR166xTZSTR190 cross was the best specific combiner followed by TZEI188xTZSTR193, TZEI80xTZSTR193, and TZSTR190xTZSTR193. TZSTR166xTZSTR190 and TZSTR190xTZSTR193 had the highest SCA effects. However, TZEI80 and TZSTR190 manifested a high positive SCA effect with TZSTR166 indicating that these two inbreds combined better with TZSTR166.

Keywords: combining ability, Striga hermonthica, resistance, grain yield

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Authors: Jeronimo Cox, Tomonari Furukawa

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Magnetometers have become more popular in inertial measurement units (IMU) for their ability to correct estimations using the earth's magnetic field. Accelerometer and gyroscope-based packages fail with dead-reckoning errors accumulated over time. Localization in robotic applications with magnetometer-inclusive IMUs has become popular as a way to track the odometry of slower-speed robots. With high-speed motions, the accumulated error increases over smaller periods of time, making them difficult to track with IMU. Tracking a high-speed motion is especially difficult with limited observability. Visual obstruction of motion leaves motion-tracking cameras unusable. When motions are too dynamic for estimation techniques reliant on the observability of the gravity vector, the use of magnetometers is further justified. As available magnetometer calibration methods are limited with the assumption that background magnetic fields are uniform, estimation in nonuniform magnetic fields is problematic. Hard iron distortion is a distortion of the magnetic field by other objects that produce magnetic fields. This kind of distortion is often observed as the offset from the origin of the center of data points when a magnetometer is rotated. The magnitude of hard iron distortion is dependent on proximity to distortion sources. Soft iron distortion is more related to the scaling of the axes of magnetometer sensors. Hard iron distortion is more of a contributor to the error of attitude estimation with magnetometers. Indoor environments or spaces inside ferrite-based structures, such as building reinforcements or a vehicle, often cause distortions with proximity. As positions correlate to areas of distortion, methods of magnetometer localization include the production of spatial mapping of magnetic field and collection of distortion signatures to better aid location tracking. The goal of this paper is to compare magnetometer methods that don't need pre-productions of magnetic field maps. Mapping the magnetic field in some spaces can be costly and inefficient. Dynamic measurement fusion is used to track the motion of a multi-link system with us. Conventional calibration by data collection of rotation at a static point, real-time estimation of calibration parameters each time step, and using two magnetometers for determining local hard iron distortion are compared to confirm the robustness and accuracy of each technique. With opposite-facing magnetometers, hard iron distortion can be accounted for regardless of position, Rather than assuming that hard iron distortion is constant regardless of positional change. The motion measured is a repeatable planar motion of a two-link system connected by revolute joints. The links are translated on a moving base to impulse rotation of the links. Equipping the joints with absolute encoders and recording the motion with cameras to enable ground truth comparison to each of the magnetometer methods. While the two-magnetometer method accounts for local hard iron distortion, the method fails where the magnetic field direction in space is inconsistent.

Keywords: motion tracking, sensor fusion, magnetometer, state estimation

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Authors: Marius Gavrilescu, Sabina-Adriana Floria, Florin Leon, Silvia Curteanu, Costel Anton

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Natural sciences provide a wide range of experimental data whose related problems require study and modeling beyond the capabilities of conventional methodologies. Such problems have solution spaces whose complexity and high dimensionality require correspondingly complex regression methods for proper characterization. In this context, we propose an optimization method which consists in a hybrid dual optimizer setup: a global optimizer based on a modified variant of the popular Imperialist Competitive Algorithm (ICA), and a local optimizer based on a gradient descent approach. The ICA is modified such that intermediate solution populations are more quickly and efficiently pruned of low-fitness individuals by appropriately altering the assimilation, revolution and competition phases, which, combined with an initialization strategy based on low-discrepancy sampling, allows for a more effective exploration of the corresponding solution space. Subsequently, gradient-based optimization is used locally to seek the optimal solution in the neighborhoods of the solutions found through the modified ICA. We use this combined approach to find the optimal configuration and weights of a fully-connected neural network, resulting in regression models used to characterize the process of obtained bricks using silicon-based materials. Installations in the raw ceramics industry, i.e., bricks, are characterized by significant energy consumption and large quantities of emissions. Thus, the purpose of our approach is to determine by simulation the working conditions, including the manufacturing mix recipe with the addition of different materials, to minimize the emissions represented by CO and CH4. Our approach determines regression models which perform significantly better than those found using the traditional ICA for the aforementioned problem, resulting in better convergence and a substantially lower error.

Keywords: optimization, biologically inspired algorithm, regression models, bricks, emissions

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Authors: Taiheng Zhang, Hongbin Zhao

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Compressed air energy storage become increasingly vital for solving intermittency problem of some renewable energies. In this study, a new hybrid system on a combination of compressed air energy storage (CAES), solid oxide fuel cell (SOFC), gas turbine (GT), and organic Rankine cycle (ORC) is proposed. In the new system, excess electricity during off-peak time is utilized to compress air. Then, the compressed air is stored in compressed air storage tank. During peak time, the compressed air enters the cathode of SOFC directly instead of combustion chamber of traditional CAES. There is no air compressor consumption of SOFC-GT in peak demand, so SOFC- GT can generate power with high-efficiency. In addition, the waste heat of exhaust from GT is recovered by applying an ORC. Three different organic working fluid (R123, R601, R601a) of ORC are chosen to evaluate system performance. Based on Aspen plus and Engineering Equation Solver (EES) software, energy and exergoeconomic analysis are used to access the viability of the combined system. Besides, the effect of two parameters (fuel flow and ORC turbine inlet pressure) on energy efficiency is studied. The effect of low-price electricity at off-peak hours on thermodynamic criteria (total unit exergy cost of products and total cost rate) is also investigated. Furthermore, for three different organic working fluids, the results of round-trip efficiency, exergy efficiency, and exergoeconomic factors are calculated and compared. Based on thermodynamic performance and exergoeconomic performance of different organic working fluids, the best suitable working fluid will be chosen. In conclusion, this study can provide important guidance for system efficiency improvement and viability.

Keywords: CAES, SOFC, ORC, energy and exergoeconomic analysis, organic working fluids

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Authors: Barbara Urasinska-Wojcik, Neil Chilton, Peter Todd, Christopher Elsworthy, Gregory J. Gibbons

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The recent increase in the application of Additive Manufacturing (AM) of products has resulted in new demands on capability. The ability to integrate both form and function within printed objects is the next frontier in the 3D printing area. To move beyond prototyping into low volume production, we demonstrate a UK-designed and built AM hybrid system that combines polymer based structural deposition with digital deposition of electrically conductive elements. This hybrid manufacturing system is based on a multi-planar build approach to improve on many of the limitations associated with AM, such as poor surface finish, low geometric tolerance, and poor robustness. Specifically, the approach involves a multi-planar Material Extrusion (ME) process in which separated build stations with up to 5 axes of motion replace traditional horizontally-sliced layer modeling. The construction of multi-material architectures also involved using multiple print systems in order to combine both ME and digital deposition of conductive material. To demonstrate multi-material 3D printing, three thermoplastics, acrylonitrile butadiene styrene (ABS), polyamide 6,6/6 copolymers (CoPA) and polyamide 12 (PA) were used to print specimens, on top of which our high viscosity Ag-particulate ink was printed in a non-contact process, during which drop characteristics such as shape, velocity, and volume were assessed using a drop watching system. Spectroscopic analysis of these 3D printed materials in the IR region helped to determine the optimum in-situ curing system for implementation into the AM system to achieve improved adhesion and surface refinement. Thermal Analyses were performed to determine the printed materials glass transition temperature (Tg), stability and degradation behavior to find the optimum annealing conditions post printing. Electrical analysis of printed conductive tracks on polymer surfaces during mechanical testing (static tensile and 3-point bending and dynamic fatigue) was performed to assess the robustness of the electrical circuits. The tracks on CoPA, ABS, and PA exhibited low electrical resistance, and in case of PA resistance values of tracks remained unchanged across hundreds of repeated tensile cycles up to 0.5% strain amplitude. Our developed AM printer has the ability to fabricate fully functional objects in one build, including complex electronics. It enables product designers and manufacturers to produce functional saleable electronic products from a small format modular platform. It will make 3D printing better, faster and stronger.

Keywords: additive manufacturing, conductive tracks, hybrid 3D printer, integrated manufacture

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Authors: Sara Camino, Fernando Vega, Mercedes Cano, Benito Navarrete, José A. Camino

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Carbon capture and storage (CCS) technologies should play a relevant role towards low-carbon systems in the European Union by 2030. Partial oxy-combustion emerges as a promising CCS approach to mitigate anthropogenic CO₂ emissions. Its advantages respect to other CCS technologies rely on the production of a higher CO₂ concentrated flue gas than these provided by conventional air-firing processes. The presence of more CO₂ in the flue gas increases the driving force in the separation process and hence it might lead to further reductions of the energy requirements of the overall CO₂ capture process. A higher CO₂ concentrated flue gas should enhance the CO₂ capture by chemical absorption in solvent kinetic and CO₂ cyclic capacity. They have impact on the performance of the overall CO₂ absorption process by reducing the solvent flow-rate required for a specific CO₂ removal efficiency. Lower solvent flow-rates decreases the reboiler duty during the regeneration stage and also reduces the equipment size and pumping costs. Moreover, R&D activities in this field are focused on novel solvents and blends that provide lower CO₂ absorption enthalpies and therefore lower energy penalties associated to the solvent regeneration. In this respect, sterically hindered amines are considered potential solvents for CO₂ capture. They provide a low energy requirement during the regeneration process due to its molecular structure. However, its absorption kinetics are slow and they must be promoted by blending with faster solvents such as monoethanolamine (MEA) and piperazine (PZ). In this work, the kinetic behavior of two sterically hindered amines were studied under partial oxy-combustion conditions and compared with MEA. A lab-scale semi-batch reactor was used. The CO₂ composition of the synthetic flue gas varied from 15%v/v – conventional coal combustion – to 60%v/v – maximum CO₂ concentration allowable for an optimal partial oxy-combustion operation. Firstly, 2-amino-2-methyl-1-propanol (AMP) showed a hybrid behavior with fast kinetics and a low enthalpy of CO₂ absorption. The second solvent was Isophrondiamine (IF), which has a steric hindrance in one of the amino groups. Its free amino group increases its cyclic capacity. In general, the presence of higher CO₂ concentration in the flue gas accelerated the CO₂ absorption phenomena, producing higher CO₂ absorption rates. In addition, the evolution of the CO2 loading also exhibited higher values in the experiments using higher CO₂ concentrated flue gas. The steric hindrance causes a hybrid behavior in this solvent, between both fast and slow kinetic solvents. The kinetics rates observed in all the experiments carried out using AMP were higher than MEA, but lower than the IF. The kinetic enhancement experienced by AMP at a high CO2 concentration is slightly over 60%, instead of 70% – 80% for IF. AMP also improved its CO₂ absorption capacity by 24.7%, from 15%v/v to 60%v/v, almost double the improvements achieved by MEA. In IF experiments, the CO₂ loading increased around 10% from 15%v/v to 60%v/v CO₂ and it changed from 1.10 to 1.34 mole CO₂ per mole solvent, more than 20% of increase. This hybrid kinetic behavior makes AMP and IF promising solvents for partial oxy–combustion applications.

Keywords: absorption, carbon capture, partial oxy-combustion, solvent

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Authors: Sokkhey Phauk, Takeo Okazaki

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The challenging task in educational institutions is to maximize the high performance of students and minimize the failure rate of poor-performing students. An effective method to leverage this task is to know student learning patterns with highly influencing factors and get an early prediction of student learning outcomes at the timely stage for setting up policies for improvement. Educational data mining (EDM) is an emerging disciplinary field of data mining, statistics, and machine learning concerned with extracting useful knowledge and information for the sake of improvement and development in the education environment. The study is of this work is to propose techniques in EDM and integrate it into a web-based system for predicting poor-performing students. A comparative study of prediction models is conducted. Subsequently, high performing models are developed to get higher performance. The hybrid random forest (Hybrid RF) produces the most successful classification. For the context of intervention and improving the learning outcomes, a feature selection method MICHI, which is the combination of mutual information (MI) and chi-square (CHI) algorithms based on the ranked feature scores, is introduced to select a dominant feature set that improves the performance of prediction and uses the obtained dominant set as information for intervention. By using the proposed techniques of EDM, an academic performance prediction system (APPS) is subsequently developed for educational stockholders to get an early prediction of student learning outcomes for timely intervention. Experimental outcomes and evaluation surveys report the effectiveness and usefulness of the developed system. The system is used to help educational stakeholders and related individuals for intervening and improving student performance.

Keywords: academic performance prediction system, educational data mining, dominant factors, feature selection method, prediction model, student performance

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Authors: Ghoutia Naima Sabri, Tayeb Benouaz

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The interaction between wave and electron cyclotron movement when the electron passes through a layer of resonance at a fixed frequency results an Electron Cyclotron (EC) absorption in Tokamak plasma and dependent magnetic field. This technique is the principle of additional heating (ECRH) and the generation of non-inductive current drive (ECCD) in modern fusion devices. In this paper we are interested by the problem of EC absorption which used a microscopic description of kinetic theory treatment versus the propagation which used the cold plasma description. The power absorbed depends on the optical depth which in turn depends on coefficient of absorption and the order of the excited harmonic for O-mode or X-mode. There is another possibility of heating by dissipation of Alfven waves, based on resonance of cold plasma waves, the shear Alfven wave (SW) and the compressional Alfven wave (FW). Once the (FW) power is coupled to (SW), it stays on the magnetic surface and dissipates there, which cause the heating of bulk plasmas.

Keywords: electron cyclotron, heating, plasma, tokamak

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Authors: You-Lin Wu, Yi-Hsing Sung, Shih-Hung Lin, Jing-Jenn Lin

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Performance improvement in optoelectronic devices such as solar cells and photodetectors has been reported when a polymer/ZnO nanorods stack is used. Resistance switching of polymer/ZnO nanocrystals (or nanorods) hybrid has also gained a lot of research interests recently. It has been reported that high- and low-resistance states of a metal/insulator/metal (MIM) structure diode with a polystyrene (PS) and ZnO hybrid as the insulator layer can be switched by applied bias after a high-voltage forming process, while the same device structure merely with a PS layer does not show any forming behavior. In this work, we investigated the current-voltage (I-V) characteristics of an MIM device with a PS/ZnO nanorods stack deposited on fluorine-doped tin oxide (FTO) glass substrate. The ZnO nanorods were grown by a hydrothermal method using a mixture of zinc nitrate, hexamethylenetetramine, and DI water. Following that, a PS layer was deposited by spin coating. Finally, the device with a structure of Ti/ PS/ZnO nanorods/FTO was completed by e-gun evaporated Ti layer on top of the PS layer. Semiconductor parameters analyzer Agilent 4156C was then used to measure the I-V characteristics of the device by applying linear ramp sweep voltage with sweep sequence of 0V → 4V → 0V → 3V → 0V → 2V → 0V → 1V → 0V in both positive and negative directions. It is interesting to find that the I-V characteristics are bias dependent and hysteretic, indicating that the device Ti/PS/ZnO nanorods/FTO structure has ferroelectricity. Our results also show that the maximum hysteresis loop height of the I-V characteristics as well as the voltage at which the maximum hysteresis loop height of each scan occurs increase with increasing maximum sweep voltage. It should be noticed that, although ferroelectricity has been found in ZnO at its melting temperature (1975℃) and in Li- or Co-doped ZnO, neither PS nor ZnO has ferroelectricity at room temperature. Using the same structure but with a PS or ZnO layer only as the insulator does not give and hysteretic I-V characteristics. It is believed that a charge polarization layer is induced near the PS/ZnO nanorods stack interface and thus causes the ferroelectricity in the device with Ti/PS/ZnO nanorods/FTO structure. Our results show that the PS/ZnO stack can find a potential application in a resistive switching memory device with MIM structure.

Keywords: ferroelectricity, hysteresis, polystyrene, resistance switching, ZnO nanorods

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Authors: Armin Najipour, A. M. Fattahi

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The aim of this research was to investigate the effect of the addition of multi walled carbon nanotubes on the mechanical properties of polyethylene/carbon nanotube nanocomposites. To do so, polyethylene and carbon nanotube were mixed in different weight percentages containing 0, 0.5, 1, and 1.5% carbon nanotube in two screw extruder apparatus by fusion. Then the nanocomposite samples were molded in injection apparatus according to ASTM: D6110 standard. The effects of carbon nanotube addition in 4 different levels and injection pressure in 2 levels on the hardness and impact strength of the nanocomposite samples were investigated. The results showed that the addition of carbon nanotube had a significant effect on improving hardness and impact strength of the nanocomposite samples such that by adding 1% w/w carbon nanotube, the impact strength and hardness of the samples improved to 74% and 46.7% respectively. Also, according to the results, the effect of injection pressure on the results was much less than that of carbon nanotube weight percentage.

Keywords: carbon nanotube, injection molding, mechanical properties, nanocomposite, polyethylene

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Authors: Jaana Sepp, Ulvi Kõrgemaa, Kristi Puusepp, Õie Tähtla

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COVID-19 was officially recognized as a pandemic in late 2019 by the WHO, and it has led to changes in the education sector. Educational institutions were closed, and most schools adopted distance learning. Estonia is known as a digitally well-developed country. Based on that, in the pandemic time, nursing education continued, and new technological solutions were implemented. To provide nursing education, special focus was paid on quality and flexibility. The aim of this paper is to present administrative, digital, and technological solutions which support Estonian nursing educators to continue the study process in the pandemic time and to develop a sustainable solution for nursing education for the future. This paper includes the authors’ analysis of the documents and decisions implemented in the institutions through the pandemic time. It is a case study of Estonian nursing educators. Results of the analysis show that the implementation of distance learning principles challenges the development of innovative strategies and technics for the assessment of student performance and educational outcomes and implement new strategies to encourage student engagement in the virtual classroom. Additionally, hospital internships were canceled, and the simulation approach was deeply implemented as a new opportunity to develop and assess students’ practical skills. There are many other technical and administrative changes that have also been carried out, such as students’ support and assessment systems, the designing and conducting of hybrid and blended studies, etc. All services were redesigned and made more available, individual, and flexible. Hence, the feedback system was changed, the information was collected in parallel with educational activities. Experiences of nursing education during the pandemic time are widely presented in scientific literature. However, to conclude our study, authors have found evidence that solutions implemented in Estonian nursing education allowed the students to graduate within the nominal study period without any decline in education quality. Operative information system and flexibility provided the minimum distance between the students, support, and academic staff, and likewise, the changes were implemented quickly and efficiently. Institution memberships were updated with the appropriate information, and it positively affected their satisfaction, motivation, and commitment. We recommend that the feedback process and the system should be permanently changed in the future to place all members in the same information area, redefine the hospital internship process, implement hybrid learning, as well as to improve the communication system between stakeholders inside and outside the organization. The main limitation of this study relates to the size of Estonia. Nursing education is provided by two institutions only, and similarly, the number of students is low. The result could be generated to the institutions with a similar size and administrative system. In the future, the relationship between nurses’ performance and organizational outcomes should be deeply investigated and influences of the pandemic time education analyzed at workplaces.

Keywords: hybrid learning, nursing education, nursing, COVID-19

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Authors: Konstantinos Tolidis

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The mountains are amongst the most fragile environments in the world. The world’s mountain areas cover 24% of the Earth’s land surface and are home to 12% of the global population. A further 14% of the global population is estimated to live in the vicinity of their surrounding areas. As urbanization continues to increase in the world, the mountains are also key centers for recreation and tourism; their attraction is often heightened by their remarkably high levels of biodiversity. Due to the fact that the features in mountain areas vary spatially (development degree, human geography, socio-economic reality, relations of dependency and interaction with other areas-regions), the spatial planning on these areas consists of a crucial process for preserving the natural, cultural and human environment and consists of one of the major processes of an integrated spatial policy. This research has been focused on the spatial decision problem of land use allocation optimization which is an ordinary planning problem on the mountain areas. It is a matter of fact that such decisions must be made not only on what to do, how much to do, but also on where to do, adding a whole extra class of decision variables to the problem when combined with the consideration of spatial optimization. The utility of optimization as a normative tool for spatial problem is widely recognized. However, it is very difficult for planners to quantify the weights of the objectives especially when these are related to mountain areas. Furthermore, the land use allocation optimization problems at mountain areas must be addressed not only by taking into account the general development objectives but also the spatial objectives (e.g. compactness, compatibility and accessibility, etc). Therefore, the main research’s objective was to approach the land use allocation problem by utilizing a hybrid meta-heuristic optimization technique tailored to the mountain areas’ spatial characteristics. The results indicates that the proposed methodological approach is very promising and useful for both generating land use alternatives for further consideration in land use allocation decision-making and supporting spatial management plans at mountain areas.

Keywords: multiobjective land use allocation, mountain areas, spatial planning, spatial decision making, meta-heuristic methods

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Authors: Thinh Ngo, Paul Rad, Brian Kelley

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Noise estimation is essential in today wireless systems for power control, adaptive modulation, interference suppression and quality of service. Deep learning (DL) has already been applied in the physical layer for modulation and signal classifications. Unacceptably low accuracy of less than 50% is found to undermine traditional application of DL classification for SNR prediction. In this paper, we use divide-and-conquer algorithm and classifier fusion method to simplify SNR classification and therefore enhances DL learning and prediction. Specifically, multiple CNNs are used for classification rather than a single CNN. Each CNN performs a binary classification of a single SNR with two labels: less than, greater than or equal. Together, multiple CNNs are combined to effectively classify over a range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained CNNs to predict SNR over a wide range of joint channel parameters including multiple Doppler shifts (0, 60, 120 Hz), power-delay profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The approach achieves individual SNR prediction accuracy of 92%, composite accuracy of 70% and prediction convergence one order of magnitude faster than that of traditional estimation.

Keywords: classification, CNN, deep learning, prediction, SNR

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Authors: Ravi Patel, Krishna K. Krishnan

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In global supply chains, appropriate and sustainable suppliers play a vital role in supply chain development and feasibility. In a larger organization with huge number of suppliers, it is necessary to divide suppliers based on their past history of quality and delivery of each product category. Since performance of any organization widely depends on their suppliers, well evaluated selection criteria and decision-making models lead to improved supplier assessment and development. In this paper, SCOR® performance evaluation approach and ISO standards are used to determine selection criteria for better utilization of supplier assessment by using hybrid model of Analytic Hierchchy Problem (AHP) and Fuzzy Techniques for Order Preference by Similarity to Ideal Solution (FTOPSIS). AHP is used to determine the global weightage of criteria which helps TOPSIS to get supplier score by using triangular fuzzy set theory. Both qualitative and quantitative criteria are taken into consideration for the proposed model. In addition, a multi-product and multi-time period model is selected for order allocation. The optimization model integrates multi-objective integer linear programming (MOILP) for order allocation and a hybrid approach for supplier selection. The proposed MOILP model optimizes order allocation based on manufacturing process and product tolerances as per manufacturer’s requirement for quality product. The integrated model and solution approach are tested to find optimized solutions for different scenario. The detailed analysis shows the superiority of proposed model over other solutions which considered individual decision making models.

Keywords: AHP, fuzzy set theory, multi-criteria decision making, multi-objective integer linear programming, TOPSIS

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Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra

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In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of Artificial Intelligence (AI), specifically Deep Learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our pioneering approach introduces a hybrid model, amalgamating the strengths of two renowned Convolutional Neural Networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging

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Authors: Yeng Min Yi, Rosli Md Illias, Salehhuddin Hamdan

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Industrial biocatalysis is mainly based on the use of cell free or intracellular enzyme systems. However, the expensive cost and relatively lower operational stability of free enzymes limit practical use in industries. Cell surface display system can be used as a cost-efficient alternative to overcome the laborious purification and substrate transport limitation. In this research, TibA autotransporter from E. coli was used to display Aspergillus fumigatus xylanase (xyn). The amplified xyn was fused in between N-terminal signal peptide and C-terminal β-barrel of TibA. The cloned was transformed and expressed in E. coli BL21 (DE3). Outer membrane localization of TibA-xyn fusion protein was confirmed by SDS PAGE and western blot with expected size of 62.5 kDa. Functional display of xyn was examined by activity assay. Cell surface displayed xyn exhibited the highest activity at 37 °c, 0.3 mM IPTG. As a summary, TibA displaying system has the potential for further industrial applications. Moreover, this is the first report of the display of xylanase using TibA on the surface of E. coli.

Keywords: biocatalysis, cell surface display, Escherichia coli, TibA autotransporter

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Authors: Armin Najipour, A. M. Fattahi

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The aim of this research was to investigate the effect of the addition of multi walled carbon nanotubes on the mechanical properties of polyethylene/carbon nanotube nanocomposites. To do so, polyethylene and carbon nanotube were mixed in different weight percentages containing 0, 0.5, 1, and 1.5% carbon nanotube in two screw extruder apparatus by fusion. Then the nanocomposite samples were molded in injection apparatus according to ASTM:D638 standard. The effects of carbon nanotube addition in 4 different levels on the tensile strength, elastic modulus and elongation of the nanocomposite samples were investigated. The results showed that the addition of carbon nanotube had a significant effect on improving tensile strength of the nanocomposite samples such that by adding 1% w/w carbon nanotube, the tensile strength 23.4%,elastic modulus 60.4%and elongation 29.7% of the samples improved. Also, according to the results, Manera approximation model at percentages about 0.5% weight and modified Halpin-Tsai at percentages about 1% weight lead to favorite and reliable results.

Keywords: carbon nanotube, injection molding, Mechanical properties, Nanocomposite, polyethylene

Procedia PDF Downloads 256

Authors: Timine Suoware, Sylvester Edelugo, Charles Amgbari

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Natural fibre composites (NFC) are becoming very attractive especially for automotive interior and non-structural building applications because they are biodegradable, low cost, lightweight and environmentally friendly. NFC are known to release high combustible products during exposure to heat atmosphere and this behaviour has raised concerns to end users. To improve on their fire response, flame retardants (FR) such as aluminium tri-hydroxide (ATH) and ammonium polyphosphate (APP) are incorporated during processing to delay the start and spread of fire. In this paper, APP was modified with Gum Arabic powder (GAP) and synergized with carbon black (CB) to form new FR species. Four FR species at 0, 12, 15 and 18% loading ratio were added to oil palm fibre polyester composite (OPFC) panels as follows; OPFC12%APP-GAP, OPFC15%APP-GAP/CB, OPFC18%ATH/APP-GAP and OPFC18%ATH/APPGAP/CB. The panels were produced using hand lay-up compression moulding and cured at room temperature. Specimens were cut from the panels and these were tested for ignition time (Tig), peak heat released rate (HRRp), average heat release rate (HRRavg), peak mass loss rate (MLRp), residual mass (Rm) and average smoke production rate (SPRavg) using cone calorimeter apparatus as well as the available flame energy (ɸ) in driving the flame using radiant panel flame spread apparatus. From the ignitability data obtained at 50 kW/m2 heat flux (HF), it shows that the hybrid FR modified with APP that is OPFC18%ATH/APP-GAP exhibited superior flame retardancy and the improvement was based on comparison with those without FR which stood at Tig = 20 s, HRRp = 86.6 kW/m2, HRRavg = 55.8 kW/m2, MLRp =0.131 g/s, Rm = 54.6% and SPRavg = 0.05 m2/s representing respectively 17.6%, 67.4%, 62.8%, 50.9%, 565% and 62.5% improvements less than those without FR (OPFC0%). In terms of flame spread, the least flame energy (ɸ) of 0.49 kW2/s3 for OPFC18%ATH/APP-GAP caused early flame regression. This was less than 39.6 kW2/s3 compared to those without FR (OPFC0%). It can be concluded that hybrid FR modified with APP could be useful in the automotive and building industries to delay the start and spread of fire.

Keywords: flame retardant, flame regression, oil palm fibre, composite panel

Procedia PDF Downloads 123

Authors: Mansouri Nabila, Ben Jemaa Yousra, Motamed Cina, Watelain Eric

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Research and development efforts in intelligent Advanced Driver Assistance Systems (ADAS) seek to save lives and reduce the number of on-road fatalities. For traffic and emergency monitoring, the essential but challenging task is vehicle detection and tracking in reasonably short time. This purpose needs first of all a powerful dynamic car detector model. In fact, this paper presents an optimized HOG process based on shape and motion parameters fusion. Our proposed approach mains to compute HOG by bloc feature from foreground blobs using configurable research window and pathway in order to overcome the shortcoming in term of computing time of HOG descriptor and improve their dynamic application performance. Indeed we prove in this paper that HOG by bloc descriptor combined with motion parameters is a very suitable car detector which reaches in record time a satisfactory recognition rate in dynamic outside area and bypasses several popular works without using sophisticated and expensive architectures such as GPU and FPGA.

Keywords: car-detector, HOG, motion, computing time

Procedia PDF Downloads 315

Authors: H. Zouar, I. Kassous, F. Benzert

Abstract:

The relentless pace of technology development in the last two decades has pervaded much of the recent educational discourse on a nation-wide scale. The rippling echoes of the buzz that account for the myriad of advantages the new technologies bring to the pedagogical activity has inevitably transcended from the western world to the Algerian educational contexts. Attempts have been made by Algerian practitioners to heed this digital advancement and push their instructional practices forward. However, due to the still largely existing first-order barriers as exemplified in the forms of deficient institutional infrastructure and unavailability of sufficient digital materials, the results of those attempts have polarised the views of Algerian academics regarding technology integration within higher education context. Hence, this study aims at measuring the possibility of integrating technology in our classrooms in a way that conforms to the philosophy of hybrid education. It also attempts to re-consider teachers’ understanding of technology integration in our context. Furthermore, the purpose of this research is also to reveal the level of teachers’ awareness regarding the distinction between technology integration and instrumental use. In view of the nature of these aims, a mixed-methods mode of investigation has been adopted to collect both qualitative and quantitative data from different perspectives. The data collection tools comprise of an observation as well as students’ and teachers’ questionnaires. The findings show that despite the fact that the examined context is not without its technological limitations, technology integration can be successfully incorporated contingent on teachers' level of knowledge and agency. Technology integration in Algerian universities does not proceed as the bedrock theory of it entails due to issues within teachers' general understanding of utilizing technology in class. It seems that technology is a means to an end, depending on the teachers who make use of it in order to deliver lessons (PowerPoint presentation) and issue commands (Facebook posting). Teachers' ability to clearly discern between integrating technology in their practices versus employing it as an instrument of instruction needs further consideration in order to establish a solid understanding of technology integration within higher education context.

Keywords: technology integration, hybrid education, teachers' understanding, teachers' awareness, instrumental use

Procedia PDF Downloads 114

Authors: S. M. Chabane sari S. Zargou, A.R. Senoudi, F. Benmouna

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Immobilization of nano particles (NPs) is the subject of numerous studies pertaining to the design of polymer nano composites, supported catalysts, bioactive colloidal crystals, inverse opals for novel optical materials, latex templated-hollow inorganic capsules, immunodiagnostic assays; “Pickering” emulsion polymerization for making latex particles and film-forming composites or Janus particles; chemo- and biosensors, tunable plasmonic nano structures, hybrid porous monoliths for separation science and technology, biocidal polymer/metal nano particle composite coatings, and so on. Particularly, in the recent years, the literature has witnessed an impressive progress of investigations on polymer coatings, grafts and particles as supports for anchoring nano particles. This is actually due to several factors: polymer chains are flexible and may contain a variety of functional groups that are able to efficiently immobilize nano particles and their precursors by dispersive or van der Waals, electrostatic, hydrogen or covalent bonds. We review methods to prepare polymer-immobilized nano particles through a plethora of strategies in view of developing systems for separation, sensing, extraction and catalysis. The emphasis is on methods to provide (i) polymer brushes and grafts; (ii) monoliths and porous polymer systems; (iii) natural polymers and (iv) conjugated polymers as platforms for anchoring nano particles. The latter range from soft bio macromolecular species (proteins, DNA) to metallic, C60, semiconductor and oxide nano particles; they can be attached through electrostatic interactions or covalent bonding. It is very clear that physicochemical properties of polymers (e.g. sensing and separation) are enhanced by anchored nano particles, while polymers provide excellent platforms for dispersing nano particles for e.g. high catalytic performances. We thus anticipate that the synergetic role of polymeric supports and anchored particles will increasingly be exploited in view of designing unique hybrid systems with unprecedented properties.

Keywords: gold, layer, polymer, macromolecular

Procedia PDF Downloads 385

Authors: Prabhu Thangaraju, Manoj Deepak, A. Sivakumar

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Removal of inter vertebral disc along with spinal fusion has many disadvantages such as causing stress fractures. If it is possible regenerate the spine it would be possible avoid the complications of the surgery and achieve better results. Our study involves the use of mesenchymal stem cells in regenerating the discs. Our study involved 10 patients who presented with degenerative disc disease between 2008-2011 in our hospital. After adequate pre-operative check prepared mesenchymal stem cells were injected into the disc spaces. These patients were subjected to conservative therapy for a minimum of six weeks before they were accepted into the study. They were followed up regularly for a minimum of 2years with serial radiographs and MRI. 8 out of the 10 patients had completed reduction in the pain. The T2 weighted MRI images in 9 out of the 10 patients showed a bright signal compared the previous Images which indicated that there was improvement in the hydration levels. From the case study of 10 patients who were subjected to mesenchymal cell therapy in our hospital, we can conclude that the use of mesenchymal cells in treatment of intervertebral disc degeneration in a safe and effective option.

Keywords: mesenchymal stem cells, intervertebral disc, the spine, disc degeneration

Procedia PDF Downloads 360

Authors: Diego Liberati

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Standard molecular modeling is traditionally done through Schroedinger equations via the help of powerful tools helping to manage them atom by atom, often needing High Performance Computing. Here, a full portfolio of new tools, conjugating statistical inference in the so called eXplainable Artificial Intelligence framework (in the form of Machine Learning of understandable rules) to the more traditional modeling and simulation control theory of mixed dynamic logic hybrid processes, is offered as quite a general purpose even if making an example to a popular chemical physics set of problems.

Keywords: understandable rules ML, k-means, PCA, PieceWise Affine Auto Regression with eXogenous input

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Authors: Sang Hun Park, Moung Young Lee, Su Min Yu, Hyun Sang Jo, Ji Hyeon Kim, Chul Gyu Song

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A near-infrared light source used as a light source in the fluorescence imaging system is suitable for use in real-time during the operation since it has no interference in surgical vision. However, fluorescence images do not have depth information. In this paper, we configured the device with the research on molecular imaging systems for monitoring thrombus imaging using fluorescence and photoacoustic. Fluorescence imaging was performed using a phantom experiment in order to search the exact location, and the Photoacoustic image was in order to detect the depth. Fluorescence image obtained when evaluated through current phantom experiments when the concentration of the contrast agent is 25μg / ml, it was confirmed that it looked sharper. The phantom experiment is has shown the possibility with the fluorescence image and photoacoustic image using an indocyanine green contrast agent. For early diagnosis of cardiovascular diseases, more active research with the fusion of different molecular imaging devices is required.

Keywords: fluorescence, photoacoustic, indocyanine green, carotid artery

Procedia PDF Downloads 586

Authors: Farzad Vakili-Farahani, Joern Lungershausen, Kilian Wasmer

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Wobbled laser beam welding, fast oscillations of a tiny laser beam within a designed path (weld geometry) during the laser pulse illumination, opens new possibilities to improve the marco-to micro-manufacturing process. The present work introduces the wobbled laser beam welding as a robust welding strategy for improving macro-to micro-fabrication process, e.g., the laser processing for gap-bridging and packaging industry. The typical requisites and relevant equipment for the development of a wobbled laser processing unit are addressed, including a suitable laser source, light delivery system, optics, proper beam deflection system and the design geometry. In addition, experiments have been carried out on titanium plate to compare the results of wobbled laser welding with conventional pulsed laser welding. As compared to the pulsed laser welding, the wobbled laser welding offers a much greater fusion area (i.e. additional molten material) while minimizing the HAZ and provides a better confinement of the material microstructural changes.

Keywords: wobbled laser beam welding, wobbling function, beam oscillation, micro welding

Procedia PDF Downloads 307

Authors: Same Noel Ngando, Yakub Abdulfatai Olatunji

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Renewable energy micro-grids, such as those powered by solar or wind energy, are often intermittent in nature. This means that the amount of energy generated by these systems can vary depending on weather conditions or other factors, which can make it difficult to ensure a steady supply of power. To address this issue, energy storage systems have been developed to increase the reliability of renewable energy micro-grids. Battery systems have been the dominant energy storage technology for renewable energy micro-grids. Batteries can store large amounts of energy in a relatively small and compact package, making them easy to install and maintain in a micro-grid setting. Additionally, batteries can be quickly charged and discharged, allowing them to respond quickly to changes in energy demand. However, the process involved in recycling batteries is quite costly and difficult. An alternative energy storage system that is gaining popularity is hydrogen storage. Hydrogen is a versatile energy carrier that can be produced from renewable energy sources such as solar or wind. It can be stored in large quantities at low cost, making it suitable for long-distance mass storage. Unlike batteries, hydrogen does not degrade over time, so it can be stored for extended periods without the need for frequent maintenance or replacement, allowing it to be used as a backup power source when the micro-grid is not generating enough energy to meet demand. When hydrogen is needed, it can be converted back into electricity through a fuel cell. Energy consumption data is got from a particular residential area in Daegu, South Korea, and the data is processed and analyzed. From the analysis, the total energy demand is calculated, and different hybrid energy system configurations are designed using HOMER Pro (Hybrid Optimization for Multiple Energy Resources) and MATLAB software. A techno-economic and environmental comparison and life cycle assessment (LCA) of the different configurations using battery and hydrogen as storage systems are carried out. The various scenarios included PV-hydrogen-grid system, PV-hydrogen-grid-wind, PV-hydrogen-grid-biomass, PV-hydrogen-wind, PV-hydrogen-biomass, biomass-hydrogen, wind-hydrogen, PV-battery-grid-wind, PV- battery -grid-biomass, PV- battery -wind, PV- battery -biomass, and biomass- battery. From the analysis, the least cost system for the location was the PV-hydrogen-grid system, with a net present cost of about USD 9,529,161. Even though all scenarios were environmentally friendly, taking into account the recycling cost and pollution involved in battery systems, all systems with hydrogen as a storage system produced better results. In conclusion, hydrogen is becoming a very prominent energy storage solution for renewable energy micro-grids. It is easier to store compared with electric power, so it is suitable for long-distance mass storage. Hydrogen storage systems have several advantages over battery systems, including flexibility, long-term stability, and low environmental impact. The cost of hydrogen storage is still relatively high, but it is expected to decrease as more hydrogen production, and storage infrastructure is built. With the growing focus on renewable energy and the need to reduce greenhouse gas emissions, hydrogen is expected to play an increasingly important role in the energy storage landscape.

Keywords: renewable energy systems, microgrid, hydrogen production, energy storage systems

Procedia PDF Downloads 82

Authors: Ma Lei-Lei, Zhou You

Abstract:

Aiming at the problems of low detection accuracy of deep learning algorithm in traffic sign detection, this paper proposes improved EfficientDet based traffic sign detection algorithm. Multi-head self-attention is introduced in the minimum resolution layer of the backbone of EfficientDet to achieve effective aggregation of local and global depth information, and this study proposes an improved feature fusion pyramid with increased vertical cross-layer connections, which improves the performance of the model while introducing a small amount of complexity, the Balanced L1 Loss is introduced to replace the original regression loss function Smooth L1 Loss, which solves the problem of balance in the loss function. Experimental results show, the algorithm proposed in this study is suitable for the task of traffic sign detection. Compared with other models, the improved EfficientDet has the best detection accuracy. Although the test speed is not completely dominant, it still meets the real-time requirement.

Keywords: convolutional neural network, transformer, feature pyramid networks, loss function

Procedia PDF Downloads 88

Authors: Mohsen Rezaee

Abstract:

Although robust security softwares, including anti-viruses, anti-spywares, anti-spam and firewalls are amalgamated with new technologies such as safe zone, hybrid cloud, sand box and etc., and although it can be said that they have managed to prepare highest level of security against viruses, spywares and other malwares in 2012, in fact, hacker attacks to websites are increasingly becoming more and more complicated. Because of security matters developments it can be said it was expected to happen so. Here in this work we try to point out some functional and vital notes to enhance security on the web, enabling the user to browse safely in unlimited web world and to use virtual space securely.

Keywords: firewalls, security, web services, computer science

Procedia PDF Downloads 389