Search results for: thermal processing

Authors: Dehni Abdellatif, Lounis Mourad

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The new approach of applied spatial geostatistics in materials sciences, agriculture accuracy, agricultural statistics, permitted an apprehension of managing and monitoring the water and groundwater qualities in a relationship with salt-affected soil. The anterior experiences concerning data acquisition, spatial-preparation studies on optical and multispectral data has facilitated the integration of correction models of electrical conductivity related with soils temperature (horizons of soils). For tomography apprehension, this physical parameter has been extracted from calibration of the thermal band (LANDSAT ETM+6) with a radiometric correction. Our study area is Oran region (Northern West of Algeria). Different spectral indices are determined such as salinity and sodicity index, the Combined Spectral Reflectance Index (CSRI), Normalized Difference Vegetation Index (NDVI), emissivity, Albedo, and Sodium Adsorption Ratio (SAR). The approach of geostatistical modeling of electrical conductivity (salinity), appears to be a useful decision support system for estimating corrected electrical resistivity related to the temperature of surface soils, according to the conversion models by substitution, the reference temperature at 25°C (where hydrochemical data are collected with this constraint). The Brightness temperatures extracted from satellite reflectance (LANDSAT ETM+) are used in consistency models to estimate electrical resistivity. The confusions that arise from the effects of salt stress and water stress removed followed by seasonal application of the geostatistical analysis in Geographic Information System (GIS) techniques investigation and monitoring the variation of the electrical conductivity in the alluvial aquifer of Es-Sénia for the salt-affected soil.

Keywords: geostatistical modelling, landsat, brightness temperature, conductivity

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Authors: Arian Hosseini, Mahmudul Hasan

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To address the increasing need for efficient and accurate content moderation, we propose an efficient and lightweight deep classification ensemble structure. Our approach is based on a combination of simple visual features, designed for high-accuracy classification of violent content with low false positives. Our ensemble architecture utilizes a set of lightweight models with narrowed-down color features, and we apply it to both images and videos. We evaluated our approach using a large dataset of explosion and blast contents and compared its performance to popular deep learning models such as ResNet-50. Our evaluation results demonstrate significant improvements in prediction accuracy, while benefiting from 7.64x faster inference and lower computation cost. While our approach is tailored to explosion detection, it can be applied to other similar content moderation and violence detection use cases as well. Based on our experiments, we propose a "think small, think many" philosophy in classification scenarios. We argue that transforming a single, large, monolithic deep model into a verification-based step model ensemble of multiple small, simple, and lightweight models with narrowed-down visual features can possibly lead to predictions with higher accuracy.

Keywords: deep classification, content moderation, ensemble learning, explosion detection, video processing

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Authors: W. G. Kim, C. H. Jo

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We analyzed the effects of variations in the diameter of silicone rubber and polyvinyl chloride (PVC) tubings on the likelihood of tubing rupture during modeling of accidental arterial line clamping in cardiopulmonary bypass with a roller pump. A closed CPB circuit constructed with a roller pump was tested with both PVC and silicone rubber tubings of 1/2, 3/8, and 1/4 inch internal diameter. Arterial line pressure was monitored, and an occlusive clamp was placed across the tubing distal to the pressure monitor site to model an accidental arterial line occlusion. A CCD camera with 512(H) x 492(V) pixels was installed above the roller pump to measure tubing diameters at pump outlet, where the maximum deformations (distension) of the tubings occurred. Quantitative measurement of the changes of tubing diameters with the change of arterial line pressure was performed using computerized image processing techniques. A visible change of tubing diameter was generally noticeable by around 250 psi of arterial line pressure, which was already very high. By 1500 psi, the PVC tubings showed an increase of diameter of between 5-10 %, while the silicone rubber tubings showed an increase between 20-25 %. Silicone rubber tubings of all sizes showed greater distensibility than PVC tubings of equivalent size. In conclusion, although roller-pump induced tubing rupture remains a theoretical problem during cardiopulmonary bypass in terms of the inherent mechanism of the pump, in reality such an occurrence is impossible in real clinical conditions.

Keywords: roller pump, tubing rupture, cardiopulmonary bypass, arterial line

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Authors: Satyam Raikwar, Thomas Herlitzius, Jens Fehrmann

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In recent years, the autonomous navigation of orchard and field robots is an emerging technology of the mobile robotics in agriculture. One of the core aspects of autonomous navigation builds upon path planning, which is still a crucial issue. Generally, for simple representation, the path planning for a mobile robot is performed in a two-dimensional space, which creates a path between the start and goal point. This paper presents the automatic path planning approach for robots used in orchards and vineyards using occupancy grid maps with field consideration. The orchards and vineyards are usually structured environment and their topology is assumed to be constant over time; therefore, in this approach, an RGB image of a field is used as a working environment. These images undergone different image processing operations and then discretized into two-dimensional grid matrices. The individual grid or cell of these grid matrices represents the occupancy of the space, whether it is free or occupied. The grid matrix represents the robot workspace for motion and path planning. After the grid matrix is described, a probabilistic roadmap (PRM) path algorithm is used to create the obstacle-free path over these occupancy grids. The path created by this method was successfully verified in the test area. Furthermore, this approach is used in the navigation of the orchard robot.

Keywords: orchard robots, automatic path planning, occupancy grid, probabilistic roadmap

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Authors: Anna Strakowska, Marian Zaborski

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This abstract focuses an overview of the methods used to create self-healing silicone composites. It has been shown how incorporating of polyhedral oligomeric silsesquioxanes (POSS) molecules with acid and basic groups to silicone rubber affects the barrier properties, mechanical properties in room and reduced temperature or the influence on relaxation rates of the methylvinylsilicone rubber vulcanizates. Moreover, the presence of silsesquioxanes, their content and the way of composites preparing affect the amount of ionic bonds, as indicated by dynamic - mechanical thermal analysis (DMTA) as well as measurements of equilibrium swelling in toluene. The aim of this work was to study the influence of concentration and different functional groups types selected silsesquioxanes compounds on self-healing effect of silicone rubber and obtain elastomers with good barrier and mechanical properties. Composites based on the methylvinylsilicone rubber with fumed silica as the fillers were manufactured and studied. To obtain self-healing effect various silsesquioxanes with amino and acid groups were used. Every tested sample demonstrated the ability to the self-treatment. The most significant effect was observed for system containing amic-acid isobytyl POSS/ aminopropylisobutyl POSS. Composite with this silsesquioxanes was exhibited the best improvement of gas permeability after heal. Moreover, the addition of POSS with acid and basic groups clearly affects the mechanical properties of the vulcanizates. The most significant effect was observed for the composite material consisting of amic-acid isobytyl POSS / aminoethylaminopropylisobutyl POSS, which tensile strength was even greater than the reference vulcanizate with fumed silica. The development of autonomous self-healing materials could have an enormous influence on all industry branches from motorization to power industry. Self-repairing materials would have a massive impact on lengthening product lifetimes, increasing safety, and lowering product costs by reducing maintenance requirements.

Keywords: barrier properties, mechanical properties, POSS, self-healing composites

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Authors: Manisha A. Hira, Arup Rakshit

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Textile substrates are endowed with flexibility and ease of making–up, but are non-conductors of electricity. Conductive materials like carbon can be incorporated into textile structures to make flexible conductive materials. Such conductive textiles find applications as electrostatic discharge materials, electromagnetic shielding materials and flexible materials to carry current or signals. This work focuses on use of carbon fiber as conductor of electricity. Carbon fibers in staple or tow form can be incorporated in textile yarn structure to conduct electricity. The paper highlights the process for development of these conductive yarns of polyester/carbon using Friction spinning (DREF) as well as ring spinning. The optimized process parameters for processing hybrid structure of polyester with carbon tow on DREF spinning and polyester with carbon staple fiber using ring spinning have been presented. The studies have been linked to highlight the electrical conductivity of the developed yarns. Further, the developed yarns have been incorporated as weft in fabric and their electrical conductivity has been evaluated. The paper demonstrates the structure and properties of fabrics developed from such polyester/carbon blend yarns and their suitability as electrically dissipative fabrics.

Keywords: carbon fiber, conductive textiles, electrostatic dissipative materials, hybrid yarns

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Authors: Sandrine Cardinal, Jean-Marc Pelletier, Guang Xie, Florian Mercier, Florent Delmas

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Bulk metallic glasses exhibit several superior properties, compared to their corresponding crystalline counterpart, such as high strength, high elastic limit or good corrosion resistance. Therefore they can be considered as good candidates for structural applications in many sectors. However, they are generally brittle and do not exhibit plastic deformation at room temperature. These materials are mainly obtained by rapid cooling from a liquid state to prevent crystallization, which limits their size. To overcome these two drawbacks: fragility and limited dimensions, composite metallic glass matrix reinforced by a second phase whose role is to slow crack growth are developed. Concerning the limited size of the pieces, the proposed solution is to get the material from amorphous powders by densifying under load. In this study, Cu50Zr45Al5 bulk metallic glassy matrix composites (MGMCs) containing different volume fraction (Vf) of Zr crystalline particles were manufactured by spark plasma sintering (SPS). Microstructure, thermal stability and mechanical properties of the MGMCs were investigated. Matrix of the composites remains a fully amorphous phase after consolidation at 420°C under 600 MPa. A good dispersion of the particles in the glassy matrix is obtained. Results show that the compressive strength decreases with Vf : 1670 MPa (Vf=0%) to 1300MPa (Vf=30%), the elastic modulus decreases but only slighty respectively 97.3GPa and 94.5 GPa and plasticity is improved from 0 to 4%. Fractographic investigation indicates a good bonding between amorphous and crystalline particles. In conclusion, present study has demonstrated that SPS method is useful for the synthesis of the bulk glassy composites. Large controlled microstructure specimens with interesting ductility can be obtained compared with others methods.

Keywords: composite, mechanical properties, metallic glasses, spark plasma sintering

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Authors: Hamid Sharif, Nazish Saleem Abbas, Muhammad Haris Jamil

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An adaptive filter is a filter that self-adjusts its transfer function according to an optimization algorithm driven by an error signal. Because of the complexity of the optimization algorithms, most adaptive filters are digital filters. Adaptive filtering constitutes one of the core technologies in digital signal processing and finds numerous application areas in science as well as in industry. Adaptive filtering techniques are used in a wide range of applications, including adaptive noise cancellation and echo cancellation. Acoustic echo cancellation is a common occurrence in today’s telecommunication systems. The signal interference caused by acoustic echo is distracting to both users and causes a reduction in the quality of the communication. In this paper, we review different techniques of adaptive filtering to reduce this unwanted echo. In this paper, we see the behavior of techniques and algorithms of adaptive filtering like Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Variable Step-Size Least Mean Square (VSLMS), Variable Step-Size Normalized Least Mean Square (VSNLMS), New Varying Step Size LMS Algorithm (NVSSLMS) and Recursive Least Square (RLS) algorithms to reduce this unwanted echo, to increase communication quality.

Keywords: adaptive acoustic, echo cancellation, LMS algorithm, adaptive filter, normalized least mean square (NLMS), variable step-size least mean square (VSLMS)

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Authors: A. Nour Eddine, D. Chalet, L. Aixala, P. Chessé, X. Faure, N. Hatat

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Thermal power technology such as the TEG (Thermo-Electric Generator) arouses significant attention worldwide for waste heat recovery. Despite the potential benefits of marine application due to the permanent heat sink from sea water, no significant studies on this application were to be found. In this study, a test rig has been designed and built to test the performance of the TEG on engine operating points. The TEG device is built from commercially available materials for the sake of possible economical application. Two types of commercial TEM (thermo electric module) have been studied separately on the test rig. The engine data were extracted from a commercial Diesel engine since it shares the same principle in terms of engine efficiency and exhaust with the marine Diesel engine. An open circuit water cooling system is used to replicate the sea water cold source. The characterization tests showed that the silicium-germanium alloys TEM proved a remarkable reliability on all engine operating points, with no significant deterioration of performance even under sever variation in the hot source conditions. The performance of the bismuth-telluride alloys was 100% better than the first type of TEM but it showed a deterioration in power generation when the air temperature exceeds 300 °C. The temperature distribution on the heat exchange surfaces revealed no useful combination of these two types of TEM with this tube length, since the surface temperature difference between both ends is no more than 10 °C. This study exposed the perspective of use of TEG technology for marine engine exhaust heat recovery. Although the results suggested non-sufficient power generation from the low cost commercial TEM used, it provides valuable information about TEG device optimization, including the design of heat exchanger and the types of thermo-electric materials.

Keywords: internal combustion engine application, Seebeck, thermo-electricity, waste heat recovery

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Authors: A. Ramkumar, Anvesh Sagar, Preetham P. Karkera

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Globalization in the modern era is dependent on the International logistics, the economic and reliable means is provided by the ocean going merchant vessel. The propulsion system which drives this massive vessels has gone through leaps and bounds of evolution. Most reliable system of propulsion adopted by the majority of vessels is by marine diesel engine. Since the first oil crisis of 1973, there is demand in increment of efficiency of main engine. Due to increase in the oil prices ship-operators explores for reduction in the operational cost of ship. And newly adopted IMO’s EEDI & SEEMP rules calls for the effective measures taken in this regard. The main engine of a ship suffers a lot of thermal losses, they mainly occur due to exhaust gas waste heat, radiation and cooling. So to increase the overall efficiency of system, we have to look into the solution to harnessing this waste energy of main engine to increase the fuel economy. During the course of research, engine manufacturers have developed many waste heat recovery systems. In our paper we see about additional options to harness this waste heat. The exhaust gas of engine coming out from the turbocharger still holds enough heat to go to the exhaust gas economiser to produce steam. This heat of exhaust gas can be used to heat a liquid of less boiling point after coming out from the turbocharger. The vapour of this secondary liquid can be superheated by a bypass exhaust or exhaust of turbocharger. This vapour can be utilized to rotate the turbine which is coupled to a generator. And the electric power for ship service can be produced with proper configuration of system. This can be included in PMS of ship. In this paper we seek to concentrate on power generation with use of exhaust gas. Thereby taking out the load on the main generator and increasing the efficiency of the system. This will help us to comply with the new rules of IMO. Our method helps to develop clean energy.

Keywords: EEDI–energy efficiency design index, IMO–international maritime organization PMS-power management system, SEEMP–ship energy efficiency management plan

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Authors: Chigozirim Onwusiribe, Jude Mbanasor

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Egusi Melon (Citrullus Lanatus Thunb. Mansf ) is a very important oil seed that serves a major ingredient in the diet of most of the households in Nigeria. Egusi Melon is very nutritious and very important in meeting the food security needs of Nigerians. Egusi Melon is cultivated in most farm enterprise in South East Nigeria but the profitability of its value chain needs to be investigated. This study analyzed the profitability of the Egusi Melon value chain. Specifically this study developed a value chain map for Egusi Melon, analysed the profitability of each stage of the Egusi Melon Value chain and analysed the determinants of the profitability of the Egusi Melon at each stage of the value chain. Multi stage sampling technique was used to select 125 farm enterprises with similar capacity and characteristics. Questionnaire and interview were used to elicit the required data while descriptive statistics, Food and Agriculture Organization Value Chain Analysis Tool, profitability ratios and multiple regression analysis were used for the data analysis. One of the findings showed that the stages of the Egusi Melon value chain are very profitable. Based on the findings, we recommend the provision of grants by government and donor agencies to the farm enterprises through their cooperative societies, this will provide the necessary funds for the local fabrication of value addition and processing equipment to suit their unique value addition needs not met by the imported equipment.

Keywords: value, chain, melon, farm, enterprises

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Authors: Zohra Mekranfar, Ahmed Saidi, Abdellah Mebrek

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This work aims to develop multi-criteria decision making (MCDM) and spatial data warehouse (SDW) methods, which will be integrated into a GIS according to a ‘GIS dominant’ approach. The GIS operating tools will be operational to operate the SDW. The MCDM methods can provide many solutions to a set of problems with various and multiple criteria. When the problem is so complex, integrating spatial dimension, it makes sense to combine the MCDM process with other approaches like data mining, ascending analyses, we present in this paper an experiment showing a geo-decisional methodology of SWD construction, On-line analytical processing (OLAP) technology which combines both basic multidimensional analysis and the concepts of data mining provides powerful tools to highlight inductions and information not obvious by traditional tools. However, these OLAP tools become more complex in the presence of the spatial dimension. The integration of OLAP with a GIS is the future geographic and spatial information solution. GIS offers advanced functions for the acquisition, storage, analysis, and display of geographic information. However, their effectiveness for complex spatial analysis is questionable due to their determinism and their decisional rigor. A prerequisite for the implementation of any analysis or exploration of spatial data requires the construction and structuring of a spatial data warehouse (SDW). This SDW must be easily usable by the GIS and by the tools offered by an OLAP system.

Keywords: data warehouse, GIS, MCDM, SOLAP

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Authors: Hussein Alahmer, Amr Ahmed

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Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate.  This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.

Keywords: CAD system, difference of feature, fuzzy c means, lesion detection, liver segmentation

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Authors: Hasan Türe, Kader Terzioglu, Evren Tunca

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Contamination of aqueous environment by heavy metal ions is a serious and complex problem, owing to their hazards to human being and ecological systems. The treatment methods utilized for removing metal ions from aqueous solution include membrane separation, ion exchange and chemical precipitation. However, these methods are limited by high operational cost. Recently, biobased beads are considered as promising biosorbent to remove heavy metal ions from water. The aim of present study was to characterize the alginate/perlite composite beads and to investigate the adsorption performance of obtained beads for removing Pb (II) from aqueous solution. Alginate beads were synthesized by ionic gelation methods and different amount of perlite (aljinate:perlite=1, 2, 3, 4, 5 wt./wt.) was incorporated into alginate beads. Samples were characterized by means of X-ray diffraction (XRD), thermogravimetric analysis (TGA), scanning electron microscopy (SEM). The effects of perlite level, the initial concentration of Pb (II), initial pH value of Pb(II) solution and effect of contact time on the adsorption capacity of beads were investigated by using batch method. XRD analysis indicated that perlite includes silicon or silicon and aluminum bearing crystalline phase. The diffraction pattern of perlite containing beads is similar to that of that perlite powder with reduced intensity. SEM analysis revealed that perlite was embedded into alginate polymer and SEM-EDX (Energy-Dispersive X-ray) showed that composite beads (aljinate:perlite=1) composed of C (41.93 wt.%,), O (43.64 wt.%), Na (10.20 wt.%), Al (0.74 wt.%), Si (2.72 wt.%) ve K (0.77 wt.%). According to TGA analysis, incorporation of perlite into beads significantly improved the thermal stability of the samples. Batch experiment indicated that optimum pH value for Pb (II) adsorption was found at pH=7 with 1 hour contact time. It was also found that the adsorption capacity of beads decreased with increases in perlite concentration. The results implied that alginate/perlite composite beads could be used as promising adsorbents for the removal of Pb (II) from wastewater. Acknowledgement: This study was supported by TUBITAK (Project No: 214Z146).

Keywords: alginate, adsorption, beads, perlite

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Authors: Wenhao Wang, Xinzhi Xu, Limin Feng, Wei Cong

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This paper comprehensively investigates current development status of global power grids and fossil energy pipelines (oil and natural gas), proposes a standard visual platform of global power and fossil energy based on Geographic Information System (GIS) technology. In this visual platform, a series of systematic visual models is proposed with global spatial data, systematic energy and power parameters. Under this visual platform, the current Global Power Grids Map and Global Fossil Energy Pipelines Map are plotted within more than 140 countries and regions across the world. Using the multi-scale fusion data processing and modeling methods, the world’s global fossil energy pipelines and power grids information system basic database is established, which provides important data supporting global fossil energy and electricity research. Finally, through the systematic and comparative study of global fossil energy pipelines and global power grids, the general status of global fossil energy and electricity development are reviewed, and energy transition in key areas are evaluated and analyzed. Through the comparison analysis of fossil energy and clean energy, the direction of relevant research is pointed out for clean development and energy transition.

Keywords: energy transition, geographic information system, fossil energy, power systems

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Authors: Mehdi Montakhabrazlighi, Ercan Balikci

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The neural network (NN) method is applied to alloy development of single crystal Ni-base Superalloys with low density and improved mechanical strength. A set of 1200 dataset which includes chemical composition of the alloys, applied stress and temperature as inputs and density and time to rupture as outputs is used for training and testing the network. Thermodynamic phase diagram modeling of the screened alloys is performed with Thermocalc software to model the equilibrium phases and also microsegregation in solidification processing. The model is first trained by 80% of the data and the 20% rest is used to test it. Comparing the predicted values and the experimental ones showed that a well-trained network is capable of accurately predicting the density and time to rupture strength of the Ni-base superalloys. Modeling results is used to determine the effect of alloying elements, stress, temperature and gamma-prime phase volume fraction on rupture strength of the Ni-base superalloys. This approach is in line with the materials genome initiative and integrated computed materials engineering approaches promoted recently with the aim of reducing the cost and time for development of new alloys for critical aerospace components. This work has been funded by TUBITAK under grant number 112M783.

Keywords: neural network, rupture strength, superalloy, thermocalc

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Authors: Yina A. Cifuentes Triana, Andrés M. Pinzón Velásco, Marío E. Velásquez Lozano

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In this work the sequencing and genome characterization of a natural isolate of Saccharomyces cerevisiae yeast (strain 202-3), identified with potential for the production of second generation ethanol from sugarcane bagasse hydrolysates is presented. This strain was selected because its capability to consume xylose during the fermentation of sugarcane bagasse hydrolysates, taking into account that many strains of S. cerevisiae are incapable of processing this sugar. This advantage and other prominent positive aspects during fermentation profiles evaluated in bagasse hydrolysates made the strain 202-3 a candidate strain to improve the production of second-generation ethanol, which was proposed as a first step to study the strain at the genomic level. The molecular characterization was carried out by genome sequencing with the Illumina HiSeq 2000 platform paired end; the assembly was performed with different programs, finally choosing the assembler ABYSS with kmer 89. Gene prediction was developed with the approach of hidden Markov models with Augustus. The genes identified were scored based on similarity with public databases of nucleotide and protein. Records were organized from ontological functions at different hierarchical levels, which identified central metabolic functions and roles of the S. cerevisiae strain 202-3, highlighting the presence of four possible new proteins, two of them probably associated with the positive consumption of xylose.

Keywords: cellulosic ethanol, Saccharomyces cerevisiae, genome sequencing, xylose consumption

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Authors: Ahmed Al-Adaileh

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Renewable energy sources, domestic storage systems, controllable loads and machine learning technologies will be key components of future smart homes management systems. An energy management scheme that uses a Deep Learning (DL) approach to support the smart home management systems, which consist of a standalone photovoltaic system, storage unit, heating ventilation air-conditioning system and a set of conventional and smart appliances, is presented. The objective of the proposed scheme is to apply DL-based machine learning to predict various running parameters within a smart home's environment to achieve maximum comfort levels for occupants, reduced electricity bills, and less dependency on the public grid. The problem is using Reinforcement learning, where decisions are taken based on applying the Continuous-time Markov Decision Process. The main contribution of this research is the proposed framework that applies DL to enhance the system's supervised dataset to offer unlimited chances to effectively support smart home systems. A case study involving a set of conventional and smart appliances with dedicated processing units in an inhabited building can demonstrate the validity of the proposed framework. A visualization graph can show "before" and "after" results.

Keywords: smart homes systems, machine learning, deep learning, Markov Decision Process

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Authors: Jose Melena, Rosa Santander, Tanya Gonzalez, Richard Duque, Juan Illanes

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Ecuador is one of the countries with the greatest aquatic biodiversity worldwide. In particular, there are at least a dozen native marine species with great aquaculture potential locally. This research concerns one of those species. It has proposed to implement experimental protocols in order to induce spawning and to generate the early larval development of the giant reef clam P. multicostata under controlled conditions. Bioassays were carried out with one adult batch (n= 8) with an average valvar length of 118,4 ± 5,8 mm, which were collected near of the Puerto Santa Rosa (2° 12' 30'' S, 80° 58' 28'' W), Santa Elena Province. During a short acclimation stage, the eight adults of giant reef clam P. multicostata were exposed to thermal stress. Briefly, the experimental protocol for spawning induction was based on the application of 20°C for 1 h and 30°C for 1 h on P. multicostata broodstock at least three consecutive times by one day. After spawning, collected sexual material was released for external fertilization process. After the delivery of gametes, it was achieved 3,25 × 10⁶ viable zygotes. As results, fertilized eggs had 56 µm diameter; while first and second cell divisions were observed to 2,5 h post-fertilization, with individual average length of 68 ± 5 µm and polar body. Latter cell divisions, including gastrula stage, appeared at 9 h post-fertilization, with individual average length of 73 ± 4 µm and trochophore stage at 15 h post-fertilization with individual average length of 75 ± 4 µm. In addition, veliger stage was registered at 20 h post-fertilization with individual average length of 82 ± 6 µm. Umboned larvae appeared at day 8 post-fertilization, with individual average length of 148 ± 6 µm. These pioneering results worldwide can strengthen the local conservation process of the overexploited P. multicostata and to encourage its production for commercial purposes.

Keywords: Ecuador, larval development, Periglypta multicostata, spawning induction

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Authors: Zhengkun Yi

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Surface texture discrimination using artificial tactile sensors has attracted increasing attentions in the past decade as it can endow technical and robot systems with a key missing ability. However, as a major component of texture, roughness has rarely been explored. This paper presents an approach for tactile surface roughness discrimination, which includes two parts: (1) design and fabrication of a bioinspired artificial fingertip, and (2) tactile signal processing for tactile surface roughness discrimination. The bioinspired fingertip is comprised of two polydimethylsiloxane (PDMS) layers, a polymethyl methacrylate (PMMA) bar, and two perpendicular polyvinylidene difluoride (PVDF) film sensors. This artificial fingertip mimics human fingertips in three aspects: (1) Elastic properties of epidermis and dermis in human skin are replicated by the two PDMS layers with different stiffness, (2) The PMMA bar serves the role analogous to that of a bone, and (3) PVDF film sensors emulate Meissner’s corpuscles in terms of both location and response to the vibratory stimuli. Various extracted features and classification algorithms including support vector machines (SVM) and k-nearest neighbors (kNN) are examined for tactile surface roughness discrimination. Eight standard rough surfaces with roughness values (Ra) of 50 μm, 25 μm, 12.5 μm, 6.3 μm 3.2 μm, 1.6 μm, 0.8 μm, and 0.4 μm are explored. The highest classification accuracy of (82.6 ± 10.8) % can be achieved using solely one PVDF film sensor with kNN (k = 9) classifier and the standard deviation feature.

Keywords: bioinspired fingertip, classifier, feature extraction, roughness discrimination

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Authors: Magy Atef Awad Attia

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The objective of this study was to study the effect of social stories on social interaction of students with autism. The sample was at level 5 student with autism, Another University Demonstration School student, who was diagnosed by the Physician as High Functioning Autism since he was able to read, write, calculate and was studying in inclusive classroom. However, he still had disability in social interaction to participate in social activity group and communication. He could not learn how to develop friendship or create relationship. He had inappropriate behavior in social context. He did not understand complex social situations. In addition, he did seemed to not know time and place. He was not able to understand feeling of oneself as well as the others. Consequently, he could not express his emotion appropriately. He did not understand or express his non-verbal language for communicating with friends. He lacked of common interest or emotion with nearby persons. He greeted inappropriately or was not interested in greeting. In addition, he did not have eye contact. He used inadequate language etc. He was elected by Purposive Sampling. His parents were willing to allow them to participate in this study. The research instruments were the lesson plan of social stories, and the picture book of social stories. The instruments used for data collection, were the social interaction evaluation of autistic students. This research was Experimental Research as One Group Pre-test, Post-test Design. For the Pre-test, the experiment was conducted by social stories. Then, the Post-test was implemented. The statistic used for data analysis. The research results were shown by scale. The results revealed that the autistic students taught by social stories indicated better social reaction after being taught by social stories.

Keywords: autism, autistic behavior, stability, harsh environments, techniques, thermal, properties, materials, applications, brittleness, fragility, disadvantages, bank, branches, profitability, setting prediction, effective target, measurement, evaluation, performance, commercial, business, sustainability, financial, system.

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Authors: Shubham Sharma, Sandra Barkauskaite, Brendan Duffy, Swarna Jaiswal, Amit K. Jaiswal

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Food packaging protects food from temperature, light, and humidity; preserves food and guarantees the safety and the integrity of the food. Advancement in packaging research leads to development of active packaging system with numerous properties such as oxygen scavengers, carbon-dioxide generating systems, antimicrobial active packaging, moisture control packaging, ethylene scavengers etc. In the active packaging, several additives such as essential oils, polyphenols etc. are incorporated into packaging film or within the packaging material to achieve the desired properties. This study investigates the effect on the structural, thermal and functional properties of different poly(lactide) – poly (butylene adipate-co-terephthalate) (PLA-PBAT) blend films incorporated with clove essential oil. The PLA-PBAT films were prepared by a solution casting method and then characterized based on their optical, mechanical properties, surface hydrophobicity, chemical composition, antimicrobial activity against S. aureus and E. coli, and inhibition of biofilm formation of E. coli. Results showed that, the developed packaging film containing clove oil has significant UV-blocking property (80%). However, incorporation of clove oil resulted in reduced transparency and tensile strength of the film as the concentration of clove oil increased. The surface hydrophobicity of packaging film was improved with the increasing concentration of essential oil. Similarly, thickness of the clove oil containing films increased from 36.71 µm to 106.67 µm as the concentration increases. The antimicrobial activity and biofilm inhibition study showed that the clove-incorporated PLA-PBAT composite film was effective against tested bacteria E. coli and S. aureus. This study showed that the PLA-PBAT – Clove oil composite film has significant antimicrobial and UV-blocking properties and can be used as an active food packaging film.

Keywords: active packaging, clove oil, poly(butylene adipate-co-terephthalate), poly(lactide)

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Authors: Yehya Elsayed, Sarah Dalibalta, Fareedah Alqtaishat, Ioline Gomes, Nagelle Fernandes

Abstract:

Bakhour or Arabian incense is traditionally used to perfume houses, shops and clothing as part of cultural or religious practices in several Middle Eastern countries. Conventionally, Bakhour consists of a mixture of natural ingredients such as chips of agarwood (oud), musk and sandalwoods that are soaked in scented oil. Bakhour is usually burned by charcoal or by using gas or electric burners to produce the scented smoke. It is necessary to evaluate the impact of such practice on human health and environment especially that the burning of Bakhour is usually done on a regular basis and in closed areas without proper ventilation. Although significant amount of research has been reported in scientific literature on the chemical analysis of various types of incense smoke, unfortunately only very few of them focused specifically on the health impacts of Bakhour. Raw Bakhour samples, their smoke emissions and the ash residue were analyzed to assess the existence of toxic ingredients and their possible influence on health and the environment. Three brands of Bakhour samples were analyzed for the presence of harmful heavy metals and organic compounds. Thermal Desorption Gas Chromatography-Mass Spectrometry (TD-GC-MS) was used to identify organic compounds while Inductively Coupled Plasma (ICP) and Scanning Electron Microscope-Energy Dispersive X-Ray Spectrometer (SEM-EDS) were used to analyze the presence of toxic and heavy metals. Organic compounds from the smoke were collected on specific tenax and activated carbon adsorption tubes. More than 850 chemical compounds were identified. The presence of 19 carcinogens, 23 toxins and 173 irritants were confirmed. Additionally, heavy metals were detected in amounts similar to those present in cigarettes. However, it was noticed that many of the detected compounds in the smoke lacked clinical studies on their health effects which shows the need for further clinical studies to be devoted to this area of study.

Keywords: Bakhour, incense smoke, pollution, indoor environment, health risk, chemical analysis

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Authors: Yehya Elsayed, Sarah Dalibalta, Fareedah Alqtaishat, Ioline Gomes, Nagelle Fernandes

Abstract:

Bakhour, or Arabian incense, is traditionally used to perfume houses, shops and clothing as part of cultural or religious practices in several Middle Eastern countries. Conventionally, Bakhour consists of a mixture of natural ingredients such as chips of agarwood (oud), musk and sandalwoods that are soaked in scented oil. Bakhour is usually burned by charcoal or by using gas or electric burners to produce the scented smoke. It is necessary to evaluate the impact of such practice on human health and environment especially that the burning of Bakhour is usually done on a regular basis and in closed areas without proper ventilation. Although significant amount of research has been reported in scientific literature on the chemical analysis of various types of incense smoke, unfortunately, only very few of them focused specifically on the health impacts of Bakhour. Raw Bakhour samples, their smoke emissions and the ash residue were analyzed to assess the existence of toxic ingredients and their possible influence on health and the environment. Three brands of Bakhour samples were analyzed for the presence of harmful heavy metals and organic compounds. Thermal Desorption Gas Chromatography-Mass Spectrometry (TD-GC-MS) was used to identify organic compounds while Inductively Coupled Plasma (ICP) and Scanning Electron Microscope-Energy Dispersive X-Ray Spectrometer (SEM-EDS) were used to analyze the presence of toxic and heavy metals.. Organic compounds from the smoke were collected on specific tenax and activated carbon adsorption tubes. More than 850 chemical compounds were identified. The presence of 19 carcinogens, 23 toxins, and 173 irritants were confirmed. Additionally, heavy metals were detected in amounts similar to those present in cigarettes. However, it was noticed that many of the detected compounds in the smoke lacked clinical studies on their health effects which shows the need for further clinical studies to be devoted to this area of study.

Keywords: bakhour, incense smoke, pollution, indoor environment, health risk, chemical analysis

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Authors: Bryan Christian Baybay, M. Francesca Ronario

Abstract:

This study is intended to answer the question “What are the communication styles of selected Filipino gays in breaking their silence on their sexual orientation to their parents?” In this regard, six cases of Filipino gay disclosures were examined through in-depth interviews. The participants were selected through purposive sampling and snowball technique. The theories, Rhetorical Sensitivity of Roderick Hart and Communicator Style of Robert Norton were used to analyze the gathered data and to give support to the communication attitudes, message processing, message rendering and communication styles exhibited in each disclosure. As secondary data and validation, parents and experts in the field of communication, sociology, and psychology were also interviewed and consulted. The study found that Filipino gays vary in the communication styles they use during the disclosure with their parents. All communication styles: impression-leaving, contentious, open, dramatic, dominant, precise, relaxed, friendly, animated, and communicator image were observed by the gays depending on their motivation, relationship and thoughts contemplated. These results lend ideas for future researchers to look into the communication patterns and/or styles of lesbians, bisexuals, transgenders and queers or expand researches on the same subject and the utilization of Social Judgment and Relational Dialectics theories in determining and analyzing LGBTQ communication.

Keywords: communication attitudes, communication styles, Filipino gays, self-disclosure, sexual orientation

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Authors: Carla Palencia-Aguilar

Abstract:

Calculations of Net Carbon Balance can be obtained by means of Net Biome Productivity (NBP), Net Ecosystem Productivity (NEP), and Net Primary Production (NPP). The latter is an important component of the biosphere carbon cycle and is easily obtained data from MODIS MOD17A3HGF; however, the results are only available yearly. To overcome data availability, bands 33 to 36 from MODIS MYD021KM (obtained on a daily basis) were analyzed and compared with NPP data from the years 2000 to 2021 in 7 sites where surface mining takes place in the Colombian territory. Coal, Gold, Iron, and Limestone were the minerals of interest. Scales and Units as well as thermal anomalies, were considered for net carbon balance per location. The NPP time series from the satellite images were filtered by using two Matlab filters: First order and Discrete Transfer. After filtering the NPP time series, comparing the graph results from the satellite’s image value, and running a linear regression, the results showed R2 from 0,72 to 0,85. To establish comparable units among NPP and bands 33 to 36, the Greenhouse Gas Equivalencies Calculator by EPA was used. The comparison was established in two ways: one by the sum of all the data per point per year and the other by the average of 46 weeks and finding the percentage that the value represented with respect to NPP. The former underestimated the total CO2 emissions. The results also showed that coal and gold mining in the last 22 years had less CO2 emissions than limestone, with an average per year of 143 kton CO2 eq for gold, 152 kton CO2 eq for coal, and 287 kton CO2 eq for iron. Limestone emissions varied from 206 to 441 kton CO2 eq. The maximum emission values from unfiltered data correspond to 165 kton CO2 eq. for gold, 188 kton CO2 eq. for coal, and 310 kton CO2 eq. for iron and limestone, varying from 231 to 490 kton CO2 eq. If the most pollutant limestone site improves its production technology, limestone could count with a maximum of 318 kton CO2 eq emissions per year, a value very similar respect to iron. The importance of gathering data is to establish benchmarks in order to attain 2050’s zero emissions goal.

Keywords: carbon dioxide, NPP, MODIS, MINING

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Authors: Kishor T. Zingre, Seshadhri Srinivasan

Abstract:

Energy consumption by the building sector is increasing at an alarming rate throughout the world and leading to more building-related CO₂ emissions into the environment. In buildings, the main contributors to energy consumption are heating, ventilation, and air-conditioning (HVAC) systems, lighting, and electrical appliances. It is hypothesised that the energy efficiency in buildings can be achieved by implementing sustainable technologies such as i) enhancing the thermal resistance of fabric materials for reducing heat gain (in hotter climates) and heat loss (in colder climates), ii) enhancing daylight and lighting system, iii) HVAC system and iv) occupant localization. Energy performance of various sustainable technologies is highly dependent on climatic conditions. This paper investigated the use of machine learning techniques for accurate prediction of air-conditioning energy in seasonal climates. The data required to train the machine learning techniques is obtained using the computational simulations performed on a 3-story commercial building using EnergyPlus program plugged-in with OpenStudio and Google SketchUp. The EnergyPlus model was calibrated against experimental measurements of surface temperatures and heat flux prior to employing for the simulations. It has been observed from the simulations that the performance of sustainable fabric materials (for walls, roof, and windows) such as phase change materials, insulation, cool roof, etc. vary with the climate conditions. Various renewable technologies were also used for the building flat roofs in various climates to investigate the potential for electricity generation. It has been observed that the proposed technique overcomes the shortcomings of existing approaches, such as local linearization or over-simplifying assumptions. In addition, the proposed method can be used for real-time estimation of building air-conditioning energy.

Keywords: building energy efficiency, energyplus, machine learning techniques, seasonal climates

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Authors: Aka S. Koffi, N’Goran Yao, Philippe Bastide, Denis Bruneau, Diby Kadjo

Abstract:

Cocoa beans (Theobroma cocoa L.) are the main components for chocolate manufacturing. The beans must be correctly fermented at first. Traditional process to perform the first fermentation (lactic fermentation) often consists in confining cacao beans using banana leaves or a fermentation basket, both of them leading to a poor product thermal insulation and to an inability to mix the product. Box fermenter reduces this loss by using a wood with large thickness (e>3cm), but mixing to homogenize the product is still hard to perform. Automatic fermenters are not rentable for most of producers. Heat (T>45°C) and acidity produced during the fermentation by microbiology activity of yeasts and bacteria are enabling the emergence of potential flavor and taste of future chocolate. In this study, a cylindro-rotative fermenter (FCR-V1) has been built and coconut fibers were used in its structure to confine heat. An axis of rotation (360°) has been integrated to facilitate the turning and homogenization of beans in the fermenter. This axis permits to put fermenter in a vertical position during the anaerobic alcoholic phase of fermentation, and horizontally during acetic phase to take advantage of the mid height filling. For circulation of air flow during turning in acetic phase, two woven rattan with grid have been made, one for the top and second for the bottom of the fermenter. In order to reduce air flow during acetic phase, two airtight covers are put on each grid cover. The efficiency of the turning by this kind of rotation, coupled with homogenization of the temperature, caused by the horizontal position in the acetic phase of the fermenter, contribute to having a good proportion of well-fermented beans (83.23%). In addition, beans’pH values ranged between 4.5 and 5.5. These values are ideal for enzymatic activity in the production of the aromatic compounds inside beans. The regularity of mass loss during all fermentation makes it possible to predict the drying surface corresponding to the amount being fermented.

Keywords: cocoa fermentation, fermenter, microbial activity, temperature, turning

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Authors: M. Crozet, D. Roudil, T. Branger, S. Boden, P. Peerani, B. Russell, M. Herranz, L. Aldave de la Heras

Abstract:

The EURATOM work program project INSIDER (Improved Nuclear Site Characterization for Waste minimization in Decommissioning under Constrained Environment) was launched in June 2017. This 4-year project has 18 partners and aims at improving the management of contaminated materials arising from decommissioning and dismantling (D&D) operations by proposing an integrated methodology of characterization. This methodology is based on advanced statistical processing and modelling, coupled with adapted and innovative analytical and measurement methods, with respect to sustainability and economic objectives. In order to achieve these objectives, the approaches will be then applied to common case studies in the form of Inter-laboratory comparisons on matrix representative reference samples and benchmarking. Work Package 6 (WP6) ‘Performance analysis and overall uncertainty’ is in charge of the analysis of the benchmarking on real samples, the organisation of inter-laboratory comparison on synthetic certified reference materials and the establishment of overall uncertainty budget. Assessment of the outcome will be used for providing recommendations and guidance resulting in pre-standardization tests.

Keywords: decommissioning, sampling strategy, research and development, characterization, European project

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Authors: Foteini Zagklavara, Peter Jimack, Nikil Kapur, Ozz Querin, Harvey Thompson

Abstract:

The invention and development of the Polymerase Chain Reaction (PCR) technology have revolutionised molecular biology and molecular diagnostics. There is an urgent need to optimise their performance of those devices while reducing the total construction and operation costs. The present study proposes a CFD-enabled optimisation methodology for continuous flow (CF) PCR devices with serpentine-channel structure, which enables the trade-offs between competing objectives of DNA amplification efficiency and pressure drop to be explored. This is achieved by using a surrogate-enabled optimisation approach accounting for the geometrical features of a CF μPCR device by performing a series of simulations at a relatively small number of Design of Experiments (DoE) points, with the use of COMSOL Multiphysics 5.4. The values of the objectives are extracted from the CFD solutions, and response surfaces created using the polyharmonic splines and neural networks. After creating the respective response surfaces, genetic algorithm, and a multi-level coordinate search optimisation function are used to locate the optimum design parameters. Both optimisation methods produced similar results for both the neural network and the polyharmonic spline response surfaces. The results indicate that there is the possibility of improving the DNA efficiency by ∼2% in one PCR cycle when doubling the width of the microchannel to 400 μm while maintaining the height at the value of the original design (50μm). Moreover, the increase in the width of the serpentine microchannel is combined with a decrease in its total length in order to obtain the same residence times in all the simulations, resulting in a smaller total substrate volume (32.94% decrease). A multi-objective optimisation is also performed with the use of a Pareto Front plot. Such knowledge will enable designers to maximise the amount of DNA amplified or to minimise the time taken throughout thermal cycling in such devices.

Keywords: PCR, optimisation, microfluidics, COMSOL

Procedia PDF Downloads 139