Search results for: solar cooling machine

Authors: Jeaha Yang, Ahmed Khan, Donna L. Rodela, Mohammed A. Qaudeer

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Traditional supply chain silos still exist today due to the difficulty of establishing trust between various partners and technological barriers across industries. Companies lose opportunities and revenue and inadvertently make poor business decisions resulting in further challenges. Blockchain technology can bring a new level of transparency through sharing information with a distributed ledger in a decentralized manner that creates a basis of trust for business. Blockchain is a loosely coupled, hub-style communication network in which trading partners can work indirectly with each other for simpler integration, but they work together through the orchestration of their supply chain operations under a coherent process that is developed jointly. A Blockchain increases efficiencies, lowers costs, and improves interoperability to strengthen and automate the supply chain management process while all partners share the risk. Blockchain ledger is built to track inventory lifecycle for supply chain transparency and keeps a journal of inventory movement for real-time reconciliation. State design patterns are used to capture the life cycle (behavior) of inventory management as a state machine for a common, transparent and coherent process which creates an opportunity for trading partners to become more responsive in terms of changes or improvements in process, reconcile discrepancies, and comply with internal governance and external regulations. It enables end-to-end, inter-company visibility at the unit level for more accurate demand planning with better insight into order fulfillment and replenishment.

Keywords: supply chain management, inventory trace-ability, perpetual inventory system, inventory lifecycle, blockchain, inventory consignment, supply chain transparency, digital thread, demand planning, hyper ledger fabric

Procedia PDF Downloads 85

Authors: K. Sathishkumar, V. Thiagarasu

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Due to recent advances in DNA microarray technology, it is now feasible to obtain gene expression profiles of tissue samples at relatively low costs. Many scientists around the world use the advantage of this gene profiling to characterize complex biological circumstances and diseases. Microarray techniques that are used in genome-wide gene expression and genome mutation analysis help scientists and physicians in understanding of the pathophysiological mechanisms, in diagnoses and prognoses, and choosing treatment plans. DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. This work presents an analysis of several clustering algorithms proposed to deals with the gene expression data effectively. The existing clustering algorithms like Support Vector Machine (SVM), K-means algorithm and evolutionary algorithm etc. are analyzed thoroughly to identify the advantages and limitations. The performance evaluation of the existing algorithms is carried out to determine the best approach. In order to improve the classification performance of the best approach in terms of Accuracy, Convergence Behavior and processing time, a hybrid clustering based optimization approach has been proposed.

Keywords: microarray technology, gene expression data, clustering, gene Selection

Procedia PDF Downloads 316

Authors: Gaurav D. Sonawane, Vikas G. Sargade

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The present investigation is a study of the effect of advanced Physical Vapor Deposition (PVD) coatings on cutting temperature residual stresses and surface roughness during Duplex Stainless Steel (DSS) 2205 turning. Austenite stabilizers like nickel, manganese, and molybdenum reduced the cost of DSS. Surface Integrity (SI) plays an important role in determining corrosion resistance and fatigue life. Resistance to various types of corrosion makes DSS suitable for applications with critical environments like Heat exchangers, Desalination plants, Seawater pipes and Marine components. However, lower thermal conductivity, poor chip control and non-uniform tool wear make DSS very difficult to machine. Cemented carbide tools (M grade) were used to turn DSS in a dry environment. AlTiN and AlTiCrN coatings were deposited using advanced PVD High Pulse Impulse Magnetron Sputtering (HiPIMS) technique. Experiments were conducted with cutting speed of 100 m/min, 140 m/min and 180 m/min. A constant feed and depth of cut of 0.18 mm/rev and 0.8 mm were used, respectively. AlTiCrN coated tools followed by AlTiN coated tools outperformed uncoated tools due to properties like lower thermal conductivity, higher adhesion strength and hardness. Residual stresses were found to be compressive for all the tools used for dry turning, increasing the fatigue life of the machined component. Higher cutting temperatures were observed for coated tools due to its lower thermal conductivity, which results in very less tool wear than uncoated tools. Surface roughness with uncoated tools was found to be three times higher than coated tools due to lower coefficient of friction of coating used.

Keywords: cutting temperature, DSS2205, dry turning, HiPIMS, surface integrity

Procedia PDF Downloads 124

Authors: Maryam Shirmohammadi, Prasad K. D. V. Yarlagadda, YuanTong Gu

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The mechanical damage on the agricultural crop during and after harvesting can create high volume of damage on tissue. Uniaxial compression and tensile loading were performed on flesh and peel samples of pumpkin. To investigate the structural changes on the tissue, Scanning Electron Microscopy (SEM) was used to capture the cellular structure change before and after loading on tissue for tensile, compression and indentation tests. To obtain required mechanical properties of tissue for the finite element analysis (FEA) model, laser measurement sensors were used to record the lateral displacement of tissue under the compression loading. Uniaxial force versus deformation data were recorded using Universal Testing Machine for both tensile and compression tests. The experimental Results were employed to develop a material model with failure criteria. The results obtained by the simulation were compared with those obtained by experiments. Note that although modelling food materials’ behaviour is not a new concept however, majority of previous studies focused on elastic behaviour and damages under linear limit, this study, however, has developed FEA models for tensile and compressive loading of pumpkin flesh and peel samples using, as the first study, both elastic and elasto-plastic material types. In addition, pumpkin peel and flesh tissues were considered as two different materials with different properties under mechanical loadings. The tensile and compression loadings were used to develop the material model for a composite structure for FEA model of mechanical peeling of pumpkin as a tough skinned vegetable.

Keywords: compressive and tensile response, finite element analysis, poisson’s ratio, elastic modulus, elastic and plastic response, rupture and bio-yielding

Procedia PDF Downloads 325

Authors: Irene Yi

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Machine learning, natural language processing, and neural network models of language are becoming more and more prevalent in the fields of technology and linguistics today. Training data for machines are at best, large corpora of human literature and at worst, a reflection of the ugliness in society. Machines have been trained on millions of human books, only to find that in the course of human history, derogatory and sexist adjectives are used significantly more frequently when describing females in history and literature than when describing males. This is extremely problematic, both as training data, and as the outcome of natural language processing. As machines start to handle more responsibilities, it is crucial to ensure that they do not take with them historical sexist and misogynistic notions. This paper gathers data and algorithms from neural network models of language having to deal with syntax, semantics, sociolinguistics, and text classification. Results are significant in showing the existing intentional and unintentional misogynistic notions used to train machines, as well as in developing better technologies that take into account the semantics and syntax of text to be more mindful and reflect gender equality. Further, this paper deals with the idea of non-binary gender pronouns and how machines can process these pronouns correctly, given its semantic and syntactic context. This paper also delves into the implications of gendered grammar and its effect, cross-linguistically, on natural language processing. Languages such as French or Spanish not only have rigid gendered grammar rules, but also historically patriarchal societies. The progression of society comes hand in hand with not only its language, but how machines process those natural languages. These ideas are all extremely vital to the development of natural language models in technology, and they must be taken into account immediately.

Keywords: gendered grammar, misogynistic language, natural language processing, neural networks

Procedia PDF Downloads 106

Authors: Ana Julia C. Kfouri

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A prerequisite of any building design is to provide security to the users, taking the climate and its physical and physical-geometrical variables into account. It is also important to highlight the relevance of the right material elements, which arise between the person and the agent, and must provide improved thermal comfort conditions and low environmental impact. Furthermore, technology is constantly advancing, as well as computational simulations for projects, and they should be used to develop sustainable building and to provide higher quality of life for its users. In relation to comfort, the more satisfied the building users are, the better their intellectual performance will be. Based on that, the study of thermal comfort in educational buildings is of relative relevance, since the thermal characteristics in these environments are of vital importance to all users. Moreover, educational buildings are large constructions and when they are poorly planned and executed they have negative impacts to the surrounding environment, as well as to the user satisfaction, throughout its whole life cycle. In this line of thought, to evaluate university classroom conditions, it was accomplished a detailed case study on the thermal comfort situation at Federal University of Parana (UFPR). The main goal of the study is to perform a thermal analysis in three classrooms at UFPR, in order to address the subjective and physical variables that influence thermal comfort inside the classroom. For the assessment of the subjective components, a questionnaire was applied in order to evaluate the reference for the local thermal conditions. Regarding the physical variables, it was carried out on-site measurements, which consist of performing measurements of air temperature and air humidity, both inside and outside the building, as well as meteorological variables, such as wind speed and direction, solar radiation and rainfall, collected from a weather station. Then, a computer simulation based on results from the EnergyPlus software to reproduce air temperature and air humidity values of the three classrooms studied was conducted. The EnergyPlus outputs were analyzed and compared with the on-site measurement results to be possible to come out with a conclusion related to the local thermal conditions. The methodological approach included in the study allowed a distinct perspective in an educational building to better understand the classroom thermal performance, as well as the reason of such behavior. Finally, the study induces a reflection about the importance of thermal comfort for educational buildings and propose thermal alternatives for future projects, as well as a discussion about the significant impact of using computer simulation on engineering solutions, in order to improve the thermal performance of UFPR’s buildings.

Keywords: computer simulation, educational buildings, EnergyPlus, humidity, temperature, thermal comfort

Procedia PDF Downloads 377

Authors: Shuyu Zhang

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The waterfront historical district, as a type of historical districts on the verge of waters such as the sea, lake, and river, have a relatively special urban form. In the past preservation and renewal of traditional historic districts, there have been many discussions on the land range, and the waterfront and marginal spaces are easily overlooked. However, the waterfront space of the historic districts, as a cultural landscape heritage combining historical buildings and landscape elements, has strong ecological and sustainable values. At the same time, Suzhou and Venice, as sister water cities in history, have more waterfront spaces that can be compared in urban form and other levels. Therefore, this paper focuses on the waterfront historic districts in Venice and Suzhou, establishes quantitative evaluation indicators for environmental perception, makes analogies, and promotes the renewal and activation of the entire historical district by improving the spatial quality and vitality of the waterfront area. First, this paper uses multi-source data for analysis, such as Baidu Maps and Google Maps API to crawl the street view of the waterfront historic districts, uses machine learning algorithms to analyze the proportion of cultural landscape elements such as green viewing rate in the street view pictures, and uses space syntax software to make quantitative selectivity analysis, so as to establish environmental perception evaluation indicators for the waterfront historic districts. Finally, by comparing and summarizing the waterfront historic districts in Venice and Suzhou, it reveals their similarities and differences, characteristics and conclusions, and hopes to provide a reference for the heritage preservation and renewal of other waterfront historic districts.

Keywords: waterfront historical district, cultural landscape, perception, multi-source Data

Procedia PDF Downloads 185

Authors: Lucilla Crosta, Anthony Edwards

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Companies and the market place nowadays struggle to find employees with adequate skills in relation to anticipated growth of their businesses. At least half of workers will need to undertake some form of up-skilling process in the next five years in order to remain aligned with the requests of the market . In order to meet these challenges, there is a clear need to explore the potential uses of AI (artificial Intelligence) based tools in assessing transversal skills (critical thinking, communication and soft skills of different types in general) of workers and adult students while empowering them to develop those same skills in a reliable trustworthy way. Companies seek workers with key transversal skills that can make a difference between workers now and in the future. However, critical thinking seems to be the one of the most imprtant skill, bringing unexplored ideas and company growth in business contexts. What employers have been reporting since years now, is that this skill is lacking in the majority of workers and adult students, and this is particularly visible trough their writing. This paper investigates how critical thinking and communication skills are currently developed in Higher Education environments through use of AI tools at postgraduate levels. It analyses the use of a branch of AI namely Machine Learning and Big Data and of Neural Network Analysis. It also examines the potential effect the acquisition of these skills through AI tools and what kind of effects this has on employability This paper will draw information from researchers and studies both at national (Italy & UK) and international level in Higher Education. The issues associated with the development and use of one specific AI tool Edulai, will be examined in details. Finally comparisons will be also made between these tools and the more recent phenomenon of Chat GPT and forthcomings and drawbacks will be analysed.

Keywords: critical thinking, artificial intelligence, higher education, soft skills, chat GPT

Procedia PDF Downloads 95

Authors: Bushra Zafar, Usman Qamar

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Large data sample size and dimensions render the effectiveness of conventional data mining methodologies. A data mining technique are important tools for collection of knowledgeable information from variety of databases and provides supervised learning in the form of classification to design models to describe vital data classes while structure of the classifier is based on class attribute. Classification efficiency and accuracy are often influenced to great extent by noisy and undesirable features in real application data sets. The inherent natures of data set greatly masks its quality analysis and leave us with quite few practical approaches to use. To our knowledge first time, we present a new approach for investigation of structure and quality of datasets by providing a targeted analysis of localization of noisy and irrelevant features of data sets. Machine learning is based primarily on feature selection as pre-processing step which offers us to select few features from number of features as a subset by reducing the space according to certain evaluation criterion. The primary objective of this study is to trim down the scope of the given data sample by searching a small set of important features which may results into good classification performance. For this purpose, a heuristic for wrapper-based feature selection using genetic algorithm and for discriminative feature selection an external classifier are used. Selection of feature based on its number of occurrence in the chosen chromosomes. Sample dataset has been used to demonstrate proposed idea effectively. A proposed method has improved average accuracy of different datasets is about 95%. Experimental results illustrate that proposed algorithm increases the accuracy of prediction of different diseases.

Keywords: data mining, generic algorithm, KNN algorithms, wrapper based feature selection

Procedia PDF Downloads 309

Authors: Rajasekar Elangopandian, Anand Shanmugam

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Now-a-days for surveillances unmanned aerial vehicle plays a vital role. Not only for surveillances, aerial photography disaster management and the notice of earth behavior UAV1s envisages meticulously. To reduce the maintenance and fuel nuclear powered Vehicles are greater support. The design consideration is much important for the UAV manufacturing industry and Research and development agency. Eventually design is looking like a pentagon shaped fuselage and black rubber coated paint in order to escape from the enemy radar and other targets. The pentagon shape fuselage has large space to keep the mini nuclear reactor inside and the material is carbon – carbon fiber specially designed by the software called cosmol and hyper mesh 14.2. So the weight consideration will produce the positive result for productivity. The walls of the fuselage are coated with lead and protective shield. A double layer of W/Bi sheet is proposed for radiation protection at the energy range of 70 Kev to 90 Kev. The designed W/bi sheet, only 0.14 mm thick and is 36% light. The properties of the fillers were determined from zeta potential and particle size measurements. The Exposes of the radiation can be attenuated by 3 ways such as minimizing exposure time, Maximizing distance from the radiation source and shielding the whole vehicle. The inside reactor will be switched ON when the UAV starts its cruise. The moderators and the control rods can be inserted by automation technique by newly developed software. The heat generated by the reactor will be used to run the turbine which is fixed inside the UAV called mini turbine with natural rubber composite Shaft radiation shield. Cooling system will be in two mode such as liquid and air cooled. Liquid coolant for the heat regeneration is ordinary water, liquid sodium, helium and the walls are made up of regenerative and radiation protective material. The other components like camera and arms bay will be located at the bottom of the UAV high are specially made products in order to escape from the radiation. They are coated with lead Pb and natural rubber composite material. This technique provides the long rang and endurance for eternal flight mission until we need any changeability of parts or product. This UAV has the special advantage of ` land on String` means it`ll land at electric line to charge the automated electronics. Then the fuel is enriched uranium (< 5% U - 235) contains hundreds of fuel pins. This technique provides eternal duty for surveillances and aerial photography. The landing of the vehicle is ease of operation likewise the takeoff is also easier than any other mechanism which present in nowadays. This UAV gives great immense and immaculate technology for surveillance and target detecting and smashing the target.

Keywords: mini turbine, liquid coolant for the heat regeneration, in order to escape from the radiation, eternal flight mission, it`ll land at electric line

Procedia PDF Downloads 405

Authors: Henrry Vicente Rojas Asuero, Holger Manuel Benavides Muñoz

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Approximately 80% of the energy consumed worldwide is generated from fossil sources, which are responsible for the emission of a large volume of greenhouse gases. For this reason, the global trend, at present, is the widespread use of energy produced from renewable sources. This seeks safety and diversification of energy supply, based on social cohesion, economic feasibility and environmental protection. In this scenario, small hydropower systems (P ≤ 10MW) stand out due to their high efficiency, economic competitiveness and low environmental impact. Small hydropower systems, along with wind and solar energy, are expected to represent a significant percentage of the world's energy matrix in the near term. Among the various technologies present in the state of the art, relating to small hydropower systems, is the Gravitational Water Vortex Power Plant, a recent technology that excels because of its versatility of operation, since it can operate with jumps in the range of 0.70 m-2.00 m and flow rates from 1 m3/s to 20 m3/s. Its operating system is based on the utilization of the energy of rotation contained within a large water vortex artificially induced. This paper presents the study and experimental design of an optimal hydraulic structure with the capacity to induce the artificial formation of a gravitational water vortex trough a system of easy application and high efficiency, able to operate in conditions of very low head and minimum flow. The proposed structure consists of a channel, with variable base, vortex inductor, tangential flow generator, coupled to a circular tank with a conical transition bottom hole. In the laboratory test, the angular velocity of the water vortex was related to the geometric characteristics of the inductor channel, as well as the influence of the conical transition bottom hole on the physical characteristics of the water vortex. The results show angular velocity values of greater magnitude as a function of depth, in addition the presence of the conical transition in the bottom hole of the circular tank improves the water vortex formation conditions while increasing the angular velocity values. Thus, the proposed system is a sustainable solution for the energy supply of rural areas near to watercourses.

Keywords: experimental model, gravitational water vortex power plant, renewable energy, small hydropower

Procedia PDF Downloads 280

Authors: M. S. Wickramarachchi, L. S. Nawarathna, C. M. B. Dematawewa

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Hatchery performance is critical for the profitability of poultry breeder operations. Some extrinsic parameters of eggs and breeders cause to increase or decrease the hatchability. This study aims to identify the affecting extrinsic parameters on the commercial hatchability of local chicken's eggs and determine the most efficient classification model with a hatchability rate greater than 90%. In this study, seven extrinsic parameters were considered: egg weight, moisture loss, breeders age, number of fertilised eggs, shell width, shell length, and shell thickness. Multiple linear regression was performed to determine the most influencing variable on hatchability. First, the correlation between each parameter and hatchability were checked. Then a multiple regression model was developed, and the accuracy of the fitted model was evaluated. Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), k-Nearest Neighbors (kNN), Support Vector Machines (SVM) with a linear kernel, and Random Forest (RF) algorithms were applied to classify the hatchability. This grouping process was conducted using binary classification techniques. Hatchability was negatively correlated with egg weight, breeders' age, shell width, shell length, and positive correlations were identified with moisture loss, number of fertilised eggs, and shell thickness. Multiple linear regression models were more accurate than single linear models regarding the highest coefficient of determination (R²) with 94% and minimum AIC and BIC values. According to the classification results, RF, CART, and kNN had performed the highest accuracy values 0.99, 0.975, and 0.972, respectively, for the commercial hatchery process. Therefore, the RF is the most appropriate machine learning algorithm for classifying the breeder outcomes, which are economically profitable or not, in a commercial hatchery.

Keywords: classification models, egg weight, fertilised eggs, multiple linear regression

Procedia PDF Downloads 82

Authors: Annalakshmi G., Sakthivel Murugan S.

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This paper presents a local directional encoded derivative binary pattern (LDEDBP) feature extraction method that can be applied for the classification of submarine coral reef images. The classification of coral reef images using texture features is difficult due to the dissimilarities in class samples. In coral reef image classification, texture features are extracted using the proposed method called local directional encoded derivative binary pattern (LDEDBP). The proposed approach extracts the complete structural arrangement of the local region using local binary batten (LBP) and also extracts the edge information using local directional pattern (LDP) from the edge response available in a particular region, thereby achieving extra discriminative feature value. Typically the LDP extracts the edge details in all eight directions. The process of integrating edge responses along with the local binary pattern achieves a more robust texture descriptor than the other descriptors used in texture feature extraction methods. Finally, the proposed technique is applied to an extreme learning machine (ELM) method with a meta-heuristic algorithm known as weighted distance grey wolf optimizer (GWO) to optimize the input weight and biases of single-hidden-layer feed-forward neural networks (SLFN). In the empirical results, ELM-WDGWO demonstrated their better performance in terms of accuracy on all coral datasets, namely RSMAS, EILAT, EILAT2, and MLC, compared with other state-of-the-art algorithms. The proposed method achieves the highest overall classification accuracy of 94% compared to the other state of art methods.

Keywords: feature extraction, local directional pattern, ELM classifier, GWO optimization

Procedia PDF Downloads 152

Authors: Kusno Kamil, Andi Asni, Sungkono

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According to a report of the World Food and Agriculture Agency (FAO): the post-harvest fish losses in Indonesia reaches 30 percent from 170 trillion rupiahs of marine fisheries reserves, then the potential loss reaches 51 trillion rupiahs (end of 2016 data). This condition is caused by traditionally vulnerable fish catches damaged due to disruption of the cold chain of preservation. The physical and chemical changes in fish flesh increase rapidly, especially if exposed to the scorching heat in the middle of the sea, exacerbated by the low awareness of catch hygiene; many unclean catches which contain blood are often treated without special attention and mixed with freshly caught fish, thereby increasing the potential for faster fish spoilage. This background encourages research on traditional fisherman catch preservation methods that aim to find the best and most affordable methods and/or combinations of fish preservation methods so that they can help fishermen increase their fishing duration without worrying that their catch will be damaged, thereby reducing their economic value when returning to the beach to sell their catches. This goal is expected to be achieved through experimental methods of treatment of fresh fish catches in containers with the addition of anti-bacterial copper, liquid smoke solution, and the use of vacuum containers. The other three treatments combined the three previous treatment variables with an electrically powered cooler (temperature 0~4 ᵒC). As a control specimen, the untreated fresh fish (placed in the open air and in the refrigerator) were also prepared for comparison for 1, 3, and 6 days. To test the level of freshness of fish for each treatment, physical observations were used, which were complemented by tests for bacterial content in a trusted laboratory. The content of copper (Cu) in fish meat (which is suspected of having a negative impact on consumers) was also part of the examination on the 6th day of experimentation. The results of physical observations on the test specimens (organoleptic method) showed that preservation assisted by the use of coolers was still better for all treatment variables. The specimens, without cooling, sequentially showed that the best preservation effectiveness was the addition of copper plates, the use of vacuum containers, and then liquid smoke immersion. Especially for liquid smoke, soaking for 6 days of preservation makes the fish meat soft and easy to crumble, even though it doesn't have a bad odor. The visual observation was then complemented by the results of testing the amount of growth (or retardation) of putrefactive bacteria in each treatment of test specimens within similar observation periods. Laboratory measurements report that the minimum amount of putrefactive bacteria achieved by preservation treatment combining cooler with liquid smoke (sample A+), then cooler only (D+), copper layer inside cooler (B+), vacuum container inside cooler (C+), respectively. Other treatments in open air produced a hundred times more putrefactive bacteria. In addition, treatment of the copper layer contaminated the preserved fresh fish more than a thousand times bigger compared to the initial amount, from 0.69 to 1241.68 µg/g.

Keywords: fish, preservation, traditional, fishermen, boat

Procedia PDF Downloads 62

Authors: А. I. Dovgyalo, S. O. Nekrasova, D. V. Sarmin, A. A. Shimanov, D. A. Uglanov

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Usually liquefied natural gas (LNG) gasification is performed due to atmospheric heat. In order to produce a liquefied gas a sufficient amount of energy is to be consumed (about 1 kW∙h for 1 kg of LNG). This study offers a number of solutions, allowing using a cold energy of LNG. In this paper it is evaluated the application turbines installed behind the evaporator in LNG complex due to its work additional energy can be obtained and then converted into electricity. At the LNG consumption of G=1000kg/h the expansion work capacity of about 10 kW can be reached. Herewith-open Rankine cycle is realized, where a low capacity cryo-pump (about 500W) performs its normal function, providing the cycle pressure. Additionally discussed an application of Stirling engine within the LNG complex also gives a possibility to realize cold energy. Considering the fact, that efficiency coefficient of Stirling engine reaches 50 %, LNG consumption of G=1000 kg/h may result in getting a capacity of about 142 kW of such a thermal machine. The capacity of the pump, required to compensate pressure losses when LNG passes through the hydraulic channel, will make 500 W. Apart from the above-mentioned converters, it can be proposed to use thermoelectric generating packages (TGP), which are widely used now. At present, the modern thermoelectric generator line provides availability of electric capacity with coefficient of efficiency up to 15%. In the proposed complex, it is suggested to install the thermoelectric generator on the evaporator surface is such a way, that the cold end is contacted with the evaporator’s surface, and the hot one – with the atmosphere. At the LNG consumption of G=1000 kgг/h and specified coefficient of efficiency the capacity of the heat flow Qh will make about 32 kW. The derivable net electric power will be P=4,2 kW, and the number of packages will amount to about 104 pieces. The carried out calculations demonstrate the research perceptiveness in this field of propulsion plant development, as well as allow realizing the energy saving potential with the use of liquefied natural gas and other cryogenics technologies.

Keywords: cold energy, gasification, liquefied natural gas, electricity

Procedia PDF Downloads 266

Authors: Sudhanshu Kumar

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Electrical discharge machining, also known as EDM, process is one of the most applicable machining process for removal of material in hard to machine materials like superalloy metals. EDM process utilizes electrical energy into sparks to erode the metals in presence of dielectric medium. In the present investigation, superalloy, Inconel 718 has been selected as workpiece and electrolytic copper as tool electrode. Attempt has been made to understand the effect of size of tool with varying cavity depth during drilling of hole through EDM process. In order to systematic investigate, tool size in terms of tool diameter and cavity depth along with other important electrical parameters namely, peak current, pulse-on time and servo voltage have been varied at three different values and the experiments has been designed using fractional factorial (Taguchi) method. Each experiment has been repeated twice under the same condition in order to understand the variability within the experiments. The effect of variations in parameters has been evaluated in terms of material removal rate, tool wear rate and surface roughness. Results revel that change in tool diameter during machining affects the response characteristics significantly. Larger tool diameter yielded 13% more material removal rate than smaller tool diameter. Analysis of the effect of variation in cavity depth is notable. There is no significant effect of cavity depth on material removal rate, tool wear rate and surface quality. This indicates that number of experiments can be performed to analyze other parameters effect even at smaller depth of cavity which can reduce the cost and time of experiments. Further, statistical analysis has been carried out to identify the interaction effect between parameters.

Keywords: EDM, Inconel 718, material removal rate, roughness, tool wear, tool size

Procedia PDF Downloads 201

Authors: Ying-Chang Yu, Yuan-Lung Lo

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Building envelope design is one of the most popular design fields of architectural profession in nowadays. The main design trend of such system is to highlight the designer's aesthetic intention from the outlook of building project. Due to the trend of current façade design, the building envelope contains more and more layers of components, such as double skin façade, photovoltaic panels, solar control system, or even ornamental components. These exterior components are designed for various functional purposes. Most researchers focus on how these exterior elements should be structurally sound secured. However, not many researchers consider these elements would help to improve the performance of façade system. When the exterior elements are deployed in large scale, it creates an additional layer outside of original façade system and acts like a porous interface which would interfere with the aerodynamic of façade surface in micro-scale. A standard façade performance consists with 'water penetration, air infiltration rate, operation force, and component deflection ratio', and these key performances are majorly driven by the 'Design Wind Load' coded in local regulation. A design wind load is usually determined by the maximum wind pressure which occurs on the surface due to the geometry or location of building in extreme conditions. This research was designed to identify the air damping phenomenon of micro turbulence caused by porous exterior layer leading to surface wind load reduction for improvement of façade system performance. A series of wind tunnel test on dynamic pressure sensor array covered by various scale of porous exterior skin was conducted to verify the effect of wind pressure reduction. The testing specimens were designed to simulate the typical building with two-meter extension offsetting from building surface. Multiple porous exterior skins were prepared to replicate various opening ratio of surface which may cause different level of damping effect. This research adopted 'Pitot static tube', 'Thermal anemometers', and 'Hot film probe' to collect the data of surface dynamic pressure behind porous skin. Turbulence and distributed resistance are the two main factors of aerodynamic which would reduce the actual wind pressure. From initiative observation, the reading of surface wind pressure was effectively reduced behind porous media. In such case, an actual building envelope system may be benefited by porous skin from the reduction of surface wind pressure, which may improve the performance of envelope system consequently.

Keywords: multi-layer facade, porous media, facade performance, turbulence and distributed resistance, wind tunnel test

Procedia PDF Downloads 213

Authors: Galal Omer, Onisimo Mutanga, Elfatih M. Abdel-Rahman, Elhadi Adam

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Threatened tree species (TTS) play a significant role in ecosystem functioning and services, land use dynamics, and other socio-economic aspects. Such aspects include ecological, economic, livelihood, security-based, and well-being benefits. The development of techniques for mapping and monitoring TTS is thus critical for understanding the functioning of ecosystems. The advent of advanced imaging systems and supervised learning algorithms has provided an opportunity to classify TTS over fragmenting landscape. Recently, vegetation maps have been produced using advanced imaging systems such as WorldView-2 (WV-2) and robust classification algorithms such as support vectors machines (SVM) and artificial neural network (ANN). However, delineation of TTS in a fragmenting landscape using high resolution imagery has widely remained elusive due to the complexity of the species structure and their distribution. Therefore, the objective of the current study was to examine the utility of the advanced WV-2 data for mapping TTS in the fragmenting Dukuduku indigenous forest of South Africa using SVM and ANN classification algorithms. The results showed the robustness of the two machine learning algorithms with an overall accuracy (OA) of 77.00% (total disagreement = 23.00%) for SVM and 75.00% (total disagreement = 25.00%) for ANN using all eight bands of WV-2 (8B). This study concludes that SVM and ANN classification algorithms with WV-2 8B have the potential to classify TTS in the Dukuduku indigenous forest. This study offers relatively accurate information that is important for forest managers to make informed decisions regarding management and conservation protocols of TTS.

Keywords: artificial neural network, threatened tree species, indigenous forest, support vector machines

Procedia PDF Downloads 506

Authors: Nkechinyere Joy Olawuyi, Babajide Samuel Afolabi, Bola Ibitoye

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This study collected a preprocessed dataset of chest radiographs and formulated a deep neural network model for detecting abnormalities. It also evaluated the performance of the formulated model and implemented a prototype of the formulated model. This was with the view to developing a deep neural network model to automatically classify abnormalities in chest radiographs. In order to achieve the overall purpose of this research, a large set of chest x-ray images were sourced for and collected from the CheXpert dataset, which is an online repository of annotated chest radiographs compiled by the Machine Learning Research Group, Stanford University. The chest radiographs were preprocessed into a format that can be fed into a deep neural network. The preprocessing techniques used were standardization and normalization. The classification problem was formulated as a multi-label binary classification model, which used convolutional neural network architecture to make a decision on whether an abnormality was present or not in the chest radiographs. The classification model was evaluated using specificity, sensitivity, and Area Under Curve (AUC) score as the parameter. A prototype of the classification model was implemented using Keras Open source deep learning framework in Python Programming Language. The AUC ROC curve of the model was able to classify Atelestasis, Support devices, Pleural effusion, Pneumonia, A normal CXR (no finding), Pneumothorax, and Consolidation. However, Lung opacity and Cardiomegaly had a probability of less than 0.5 and thus were classified as absent. Precision, recall, and F1 score values were 0.78; this implies that the number of False Positive and False Negative is the same, revealing some measure of label imbalance in the dataset. The study concluded that the developed model is sufficient to classify abnormalities present in chest radiographs into present or absent.

Keywords: transfer learning, convolutional neural network, radiograph, classification, multi-label

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Authors: Supanitayanon Lalita, Dechkunakorn Surachai, Anuwongnukroh Niwat, Srikhirin Toemsak, Roongrujimek Pitchaya, Tua-Ngam Peerapong

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Objective: To compare maximum load, percentage of elongation, physical characteristics of 4 types of dental floss: (1) Thai Silk Floss (silk, waxed), (2) Oral B® Essential Floss (nylon, waxed), (3) Experimental Floss Xu (nylon, unwaxed), (4) Experimental Floss Xw (nylon, waxed). Materials & method: Four types of floss were tested (n=30) with a Universal Testing Machine (Instron®). Each sample (30 cm long, 5 cm segment) was fixed, and pulled apart with load cell of 100 N and a test speed of 100 mm/min. Physical characteristics were investigated by digital microscope under 2.5×10 magnification, and scanning electron microscope under 1×100 and 5×100 magnification. The size of the filaments was measured in micron (μm) and the fineness were measured in Denier. Statistical analysis: For mechanical properties, the maximum load and the percentage of elongation were presented as mean ± SD. The distribution of the data was calculated by the Kolmogorov-Smirnov test. One-way ANOVA and multiple comparison (Tukey HSD) were used to analyze the differences among the groups with the level of a statistical difference at p < 0.05. Results: The maximum load of Floss Xu, Floss Xw, Oral B and Thai Silk were 47.39, 46.46, 25.38, and 23.70 N, respectively. The percentage of elongation of Oral B, Floss Xw, Floss Xu and Thai Silk were 72.43, 44.62, 31.25, and 16.44%, respectively. All 4 types of dental floss showed statistically differences in both the maximum load and percentage of elongation at p < 0.05, except for maximum load between Floss Xw and Floss Xu that showed no statistically significant difference. Physical characteristics of Thai silk revealed the most disintegrated, the smallest, and the least fine filaments. Conclusion: Floss Xu had the highest maximum load. Oral B had the highest percentage of elongation. Wax coating on Floss X increased the elongation but had no significant effect on the maximum load. The physical characteristics of Thai Silk resulted in the lowest mechanical properties values.

Keywords: dental floss, maximum load, mechanical property, percentage of elongation, physical property

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Authors: Angelis P. Barlampas, Ghita Bianca-Andreea

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A case of a prostatic midline cyst is presented, which was found during a routine general ultrasound examination in an otherwise healthy young man. The incidence of prostatic cysts discovered in suprapubic ultrasound examination has constantly been rising over the previous decades. Despite the fact that the majority of them are benign, a significant amount is related to symptoms, such as pain, dysuria, infertility, and even cancer. The wide use of ultrasound examination and the increasing availability of high-resolution ultrasound systems have rendered new diagnostic challenges. Once upon a time a suprapubic ultrasound was only useful for measuring only the size and the dimensions of the prostatic gland. It did not have the ability to analyze and resolve structures such as cystic or solid nodules. The current machine equipment has managed to depict the imaging characteristics of lesions with high acuity that compares of an intrarectal ultrasound. But the last one is a specialized examination, which demands expertise and good knowledge. Maybe the time has come for the general radiologist and, especially the one who uses suprapubic ultrasound, to pay more attention to the examination of the prostate gland and to take advantage of the superb abilities and the high resolution of the new ultrasound systems. That is exactly, what this case is emphasizing. The incidental discovery of prostatic cysts, and the relatively little available literature about managing them turns them into an interesting theme for exploring and studying. The prostatic cysts are further divided into midline and paramidline cysts, with the first being usually utricle cysts. A more precise categorization is as follows: A midline cystic lesion usually regards a Mullerian duct cyst, a prostatic utricle cyst, an ejaculatory duct cyst, a prostatic cystadenoma, a ductus deferens cyst, and a TURP. On the other hand, a lateral cystic lesion usually refers to a cystic degeneration of benign prostatic hyperplasia, a prostatic retention cyst, a seminal vesicle cyst, diverticular prostatitis, a prostatic abscess, cavitatory prostatitis from chronic prostatitis, a parasitic prostatic cyst, a cystic prostatic carcinoma, e.t.c.

Keywords: prostatic cyst, radiology, benign prostatic lesions, prostatic cancer, suprapubic prostatic ultrasound

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Authors: Kazuki Kamada, Haruka Yamashita

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In Japan, taxi is one of the popular transportations and taxi industry is one of the big businesses. However, in recent years, there has been a difficult problem of reducing the number of taxi drivers. In the taxi business, mainly three passenger catching methods are applied. One style is "cruising" that drivers catches passengers while driving on a road. Second is "waiting" that waits passengers near by the places with many requirements for taxies such as entrances of hospitals, train stations. The third one is "dispatching" that is allocated based on the contact from the taxi company. Above all, the cruising taxi drivers need the experience and intuition for finding passengers, and it is difficult to decide "the destination for cruising". The strong recommendation system for the cruising taxies supports the new drivers to find passengers, and it can be the solution for the decreasing the number of drivers in the taxi industry. In this research, we propose a method of recommending a destination for cruising taxi drivers. On the other hand, as a machine learning technique, the embedding models that embed the high dimensional data to a low dimensional space is widely used for the data analysis, in order to represent the relationship of the meaning between the data clearly. Taxi drivers have their favorite courses based on their experiences, and the courses are different for each driver. We assume that the course of cruising taxies has meaning such as the course for finding business man passengers (go around the business area of the city of go to main stations) and course for finding traveler passengers (go around the sightseeing places or big hotels), and extract the meaning of their destinations. We analyze the cruising history data of taxis based on the embedding model and propose the recommendation system for passengers. Finally, we demonstrate the recommendation of destinations for cruising taxi drivers based on the real-world data analysis using proposing method.

Keywords: taxi industry, decision making, recommendation system, embedding model

Procedia PDF Downloads 129

Authors: Andrew Kanzunguze, Lusayo Mwabumba, Jason K. Gilbertson, Dominic B. Gondwe, George Z. Nxumayo

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Knowledge about the spatio-temporal distribution of invasive plants in protected areas provides a base from which hypotheses explaining proliferation of plant invasions can be made alongside development of relevant invasive plant monitoring programs. The aim of this study was to investigate the spatio-temporal distribution of bracken fern on the grassland plateau of Nyika National Park over the past 30 years (1986-2016) as well as to determine the current extent of the invasion. Remote sensing, machine learning, and statistical modelling techniques (object-based image analysis, image classification and linear regression analysis) in geographical information systems were used to determine both the spatial and temporal distribution of bracken fern in the study area. Results have revealed that bracken fern has been increasing coverage on the Nyika plateau at an estimated annual rate of 87.3 hectares since 1986. This translates to an estimated net increase of 2,573.1 hectares, which was recorded from 1,788.1 hectares (1986) to 4,361.9 hectares (2016). As of 2017 bracken fern covered 20,940.7 hectares, approximately 14.3% of the entire grassland plateau. Additionally, it was observed that the fern was distributed most densely around Chelinda camp (on the central plateau) as well as in forest verges and roadsides across the plateau. Based on these results it is recommended that Ecological Niche Modelling approaches be employed to (i) isolate the most important factors influencing bracken fern proliferation as well as (ii) identify and prioritize areas requiring immediate control interventions so as to minimize bracken fern proliferation in Nyika National Park.

Keywords: bracken fern, image classification, Landsat-8, Nyika National Park, spatio-temporal distribution

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Authors: Swati Kapoor, Rajeev Upreti, Monica Mahajan, Abhaya Indrayan, Dinesh Srivastava

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Enteric Fever is caused by gram negative bacilli Salmonella typhi and paratyphi. It is associated with high morbidity and mortality worldwide. Timely initiation of treatment is a crucial step for prevention of any complications. Cultures of body fluids are diagnostic, but not always conclusive or practically feasible in most centers. Moreover, the results of cultures delay the treatment initiation. Serological tests lack diagnostic value. The blood counts can offer a promising option in diagnosis. A retrospective study to find out the relevance of leucopenia and eosinopenia was conducted on 203 culture proven enteric fever patients and 159 culture proven non-enteric fever patients in a tertiary care hospital in New Delhi. The patient details were retrieved from the electronic medical records section of the hospital. Absolute eosinopenia was considered as absolute eosinophil count (AEC) of less than 40/mm³ (normal level: 40-400/mm³) using LH-750 Beckman Coulter Automated machine. Leucopoenia was defined as total leucocyte count (TLC) of less than 4 X 10⁹/l. Blood cultures were done using BacT/ALERT FA plus automated blood culture system before first antibiotic dose was given. Case and control groups were compared using Pearson Chi square test. It was observed that absolute eosinophil count (AEC) of 0-19/mm³ was a significant finding (p < 0.001) in enteric fever patients, whereas leucopenia was not a significant finding (p=0.096). Using Receiving Operating Characteristic (ROC) curves, it was observed that patients with both AEC < 14/mm³ and TCL < 8 x 10⁹/l had 95.6% chance of being diagnosed as enteric fever and only 4.4% chance of being diagnosed as non-enteric fever. This result was highly significant with p < 0.001. This is a very useful association of AEC and TLC found in enteric fever patients of this study which can be used for the early initiation of treatment in clinically suspected enteric fever patients.

Keywords: absolute eosinopenia, absolute eosinophil count, enteric fever, leucopenia, total leucocyte count

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Authors: John Lamb, Robert Epstein, Vasundhara L. Bhupathi, Sanjeev Kumar Marimekala

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In this paper, we focus on the importance of scalable architecture for data centers and buildings in general to help an enterprise achieve environmental sustainability. The scalable architecture helps in many ways, such as adaptability to the business and user requirements, promotes high availability and disaster recovery solutions that are cost effective and low maintenance. The scalable architecture also plays a vital role in three core areas of sustainability: economy, environment, and social, which are also known as the 3 pillars of a sustainability model. If the architecture is scalable, it has many advantages. A few examples are that scalable architecture helps businesses and industries to adapt to changing technology, drive innovation, promote platform independence, and build resilience against natural disasters. Most importantly, having a scalable architecture helps industries bring in cost-effective measures for energy consumption, reduce wastage, increase productivity, and enable a robust environment. It also helps in the reduction of carbon emissions with advanced monitoring and metering capabilities. Scalable architectures help in reducing waste by optimizing the designs to utilize materials efficiently, minimize resources, decrease carbon footprints by using low-impact materials that are environmentally friendly. In this paper we also emphasize the importance of cultural shift towards the reuse and recycling of natural resources for a balanced ecosystem and maintain a circular economy. Also, since all of us are involved in the use of computers, much of the scalable architecture we have studied is related to data centers.

Keywords: scalable architectures, sustainability, application design, disruptive technology, machine learning and natural language processing, AI, social media platform, cloud computing, advanced networking and storage devices, advanced monitoring and metering infrastructure, climate change

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Authors: U. Pruthviraj, Praveen Kumar R. A. K. Athul, K. V. Gangadharan, S. Rao Shrikantha

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An open source based autonomous unmanned marine surface vehicle (UMSV) is developed for some of the marine applications such as pollution control, environmental monitoring and thermal imaging. A double rotomoulded hull boat is deployed which is rugged, tough, quick to deploy and moves faster. It is suitable for environmental monitoring, and it is designed for easy maintenance. A 2HP electric outboard marine motor is used which is powered by a lithium-ion battery and can also be charged from a solar charger. All connections are completely waterproof to IP67 ratings. In full throttle speed, the marine motor is capable of up to 7 kmph. The motor is integrated with an open source based controller using cortex M4F for adjusting the direction of the motor. This UMSV can be operated by three modes: semi-autonomous, manual and fully automated. One of the channels of a 2.4GHz radio link 8 channel transmitter is used for toggling between different modes of the USMV. In this electric outboard marine motor an on board GPS system has been fitted to find the range and GPS positioning. The entire system can be assembled in the field in less than 10 minutes. A Flir Lepton thermal camera core, is integrated with a 64-bit quad-core Linux based open source processor, facilitating real-time capturing of thermal images and the results are stored in a micro SD card which is a data storage device for the system. The thermal camera is interfaced to an open source processor through SPI protocol. These thermal images are used for finding oil spills and to look for people who are drowning at low visibility during the night time. A Real Time clock (RTC) module is attached with the battery to provide the date and time of thermal images captured. For the live video feed, a 900MHz long range video transmitter and receiver is setup by which from a higher power output a longer range of 40miles has been achieved. A Multi-parameter probe is used to measure the following parameters: conductivity, salinity, resistivity, density, dissolved oxygen content, ORP (Oxidation-Reduction Potential), pH level, temperature, water level and pressure (absolute).The maximum pressure it can withstand 160 psi, up to 100m. This work represents a field demonstration of an open source based autonomous navigation system for a marine surface vehicle.

Keywords: open source, autonomous navigation, environmental monitoring, UMSV, outboard motor, multi-parameter probe

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Authors: David A. Harness

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Natural evolution of ATP cognitive systems is to meet AI peer review standards. ATP process of axiom selection from Mizar to prove a conjecture would be further refined, as in all human and machine learning, by solving the real world problem of the proposed AI peer review challenge: Determine which conjecture forms the higher confidence level constructive proof between Standard Model of Physics SU(n) lattice gauge group operation vs. present non-standard 4D GEM EOS SU(n) lattice gauge group spatially extended operation in which the photon and electron are the first two trace angular momentum invariants of a gravitoelectromagnetic (GEM) energy momentum density tensor wavetrain integration spin-stress pressure-volume equation of state (EOS), initiated via 32 lines of Mathematica code. Resulting gravitoelectromagnetic spectrum ranges from compressive through rarefactive of the central cosmological constant vacuum energy density in units of pascals. Said self-adjoint group operation exclusively operates on the stress energy momentum tensor of the Einstein field equations, introducing quantization directly on the 4D spacetime level, essentially reformulating the Yang-Mills virtual superpositioned particle compounded lattice gauge groups quantization of the vacuum—into a single hyper-complex multi-valued GEM U(1) × SU(1,3) lattice gauge group Planck spacetime mesh quantization of the vacuum. Thus the Mizar corpus already contains all of the axioms required for relevant DeepMath premise selection and unambiguous formal natural language parsing in context deep learning.

Keywords: automated theorem proving, constructive quantum field theory, information theory, neural networks

Procedia PDF Downloads 167

Authors: Adrian Niewiadomski, Barbara Surma, Katarzyna Kolodziejak, Dorota A. Pawlak

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Oxide-oxide eutectic is attracting increasing interest of scientific community because of their unique properties and numerous potential applications. Some of the most interesting examples of applications are metamaterials, glucose sensors, photoactive materials, thermoelectric materials, and photocatalysts. Their unique properties result from the fact that composite materials consist of two or more phases. As a result, these materials have additive and product properties. Additive properties originate from particular phases while product properties originate from the interaction between phases. MnTiO3-TiO2 eutectic is one of such materials. TiO2 is a well-known semiconductor, and it is used as a photocatalyst. Moreover, it may be used to produce solar cells, in a gas sensing devices and in electrochemistry. MnTiO3 is a semiconductor and antiferromagnetic. Therefore it has potential application in integrated circuits devices, and as a gas and humidity sensor, in non-linear optics and as a visible-light activated photocatalyst. The above facts indicate that eutectic MnTiO3-TiO2 constitutes an extremely promising material that should be studied. Despite that Raman spectroscopy is a powerful method to characterize materials, to our knowledge Raman studies of eutectics are very limited, and there are no studies of the MnTiO3-TiO2 eutectic. While to our knowledge the papers regarding this material are scarce. The MnTiO3-TiO2 eutectic, as well as TiO2 and MnTiO3 single crystals, were grown by the micro-pulling-down method at the Institute of Electronic Materials Technology in Warsaw, Poland. A nitrogen atmosphere was maintained during whole crystal growth process. The as-grown samples of MnTiO3-TiO2 eutectic, as well as TiO2 and MnTiO3 single crystals, are black and opaque. Samples were cut perpendicular to the growth direction. Cross sections were examined with scanning electron microscopy (SEM) and with Raman spectroscopy. The present studies showed that maintaining nitrogen atmosphere during crystal growth process may result in obtaining black TiO2 crystals. SEM and Raman experiments showed that studied eutectic consists of three distinct regions. Furthermore, two of these regions correspond with MnTiO3, while the third region corresponds with the TiO2-xNx phase. Raman studies pointed out that TiO2-xNx phase crystallizes in rutile structure. The studies show that Raman experiments may be successfully used to characterize eutectic materials. The MnTiO3-TiO2 eutectic was grown by the micro-pulling-down method. SEM and micro-Raman experiments were used to establish phase composition of studied eutectic. The studies revealed that the TiO2 phase had been doped with nitrogen. Therefore the TiO2 phase is, in fact, a solid solution with TiO2-xNx composition. The remaining two phases exhibit Raman lines of both rutile TiO2 and MnTiO3. This points out to some kind of coexistence of these phases in studied eutectic.

Keywords: compound materials, eutectic growth and characterization, Raman spectroscopy, rutile TiO₂

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Authors: W. Thepsuwan, N. Monmaturapoj

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Calcium phosphate cements (CPCs) is one of the most attractive bioceramics due to its moldable and shape ability to fill complicated bony cavities or small dental defect positions. In this study, CPCs were produced by using mixtures of tetracalcium phosphate (TTCP, Ca4O(PO4)2) and dicalcium phosphate anhydrous (DCPA, CaHPO4) in equimolar ratio (1/1) with aqueous solutions of acetic acid (C2H4O2) and disodium hydrogen phosphate dehydrate (Na2HPO4.2H2O) in combination with sodium alginate in order to improve theirs moldable characteristic. The concentrations of the aqueous solutions and sodium alginate were varied to investigate the effects of different aqueous solution and alginate on properties of the cements. The cement paste was prepared by mixing cement powder (P) with aqueous solution (L) in a P/L ratio of 1.0 g/ 0.35 ml. X-ray diffraction (XRD) was used to analyses phase formation of the cements. Setting times and compressive strength of the set CPCs were measured using the Gilmore apparatus and Universal testing machine, respectively. The results showed that CPCs could be produced by using both basic (Na2HPO4.2H2O) and acidic (C2H4O2) solutions. XRD results show the precipitation of hydroxyapatite in all cement samples. No change in phase formation among cements using difference concentrations of Na2HPO4.2H2O solutions. With increasing concentration of acidic solutions, samples obtained less hydroxyapatite with a high dicalcium phosphate dehydrate leaded to a shorter setting time. Samples with sodium alginate exhibited higher crystallization of hydroxyapatite than that of without alginate as a result of shorten setting time in basic solution but a longer setting time in acidic solution. The stronger cement was attained from samples using acidic solution with sodium alginate; however it was lower than using the basic solution.

Keywords: calcium phosphate cements, TTCP, DCPA, hydroxyapatite, properties

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Authors: Raymond Dominic Uzoh

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Starch fillers were extracted from three plant sources namely amora tuber (a wild variety of Irish potato), sweet potato and yam starch and their particle size, pH, amylose, and amylopectin percentage decomposition determined accordingly by high performance liquid chromatography (HPLC). The starch was introduced into natural rubber in liquid phase (through gelatinization) by the latex compounding method and compounded according to standard method. The prepared starch/natural rubber composites was characterized by Instron Universal testing machine (UTM) for tensile mechanical properties. The composites was further characterized by x-ray diffraction and crosslink density analysis. The particle size determination showed that amora starch granules have the highest particle size (156 × 47 μm) followed by yam starch (155× 40 μm) and then the sweet potato starch (153 × 46 μm). The pH test also revealed that amora starch has a near neutral pH of 6.9, yam 6.8, and sweet potato 5.2 respectively. Amylose and amylopectin determination showed that yam starch has a higher percentage of amylose (29.68), followed by potato (22.34) and then amora starch with the lowest value (14.86) respectively. The tensile mechanical properties testing revealed that yam starch produced the best tensile mechanical properties followed by amora starch and then sweet potato starch. The structure, crystallinity/amorphous nature of the product composite was confirmed by x-ray diffraction, while the nature of crosslinking was confirmed by swelling test in toluene solvent using the Flory-Rehner approach. This research study has rendered a workable strategy for enhancing interfacial interaction between a hydrophilic filler (starch) and hydrophobic polymeric matrix (natural rubber) yielding moderately good tensile mechanical properties for further exploitation development and application in the rubber processing industry.

Keywords: natural rubber, fillers, starch, amylose, amylopectin, crosslink density

Procedia PDF Downloads 158