Search results for: axial flux induction machine

Authors: Dania Baaboud, Renad Al-zahrani, Mahnoor Khan, Riya Afroz

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Flo is a smart period-tracking tool that uses artificial intelligence (AI) to offer individualized reproductive health predictions and insights. Flo makes very accurate predictions about menstrual cycles, ovulation, and fertility windows by evaluating user inputs, including cycle duration, symptoms, and patterns. Its machine learning algorithms are constantly evolving, providing personalized health recommendations, instructional materials, and early identification of possible health abnormalities such as reproductive problems and hormone imbalances. Flo, which was introduced in 2015 and upgraded with AI in 2017, is a revolutionary use of technology in healthcare that empowers people to make knowledgeable decisions regarding their well-being. Despite its advantages, our study included drawbacks, such as limited access to premium services and a small sample size. While highlighting unique characteristics, a comparative comparison with similar applications such as Clue and Glow confirmed Flo's outstanding AI integration for individualized healthcare. All things considered, Flo is a prime example of how AI can be used to tackle intricate biological processes, giving consumers the ability to efficiently control their reproductive health and opening the door for improvements in individualized medical technology.

Keywords: Flo, period-tracking app, period symptoms, women’s health, machinery

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Authors: Thammarak Srimarut, Witthaya Mekhum

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This study analysed and developed a model for monk’s food bowl production on occupational health safety and environment at work for the encouragement of Rattanakosin local wisdom at Banbart Community. The process of blowpipe welding was necessary to produce the bowl which was very dangerous or 93.59% risk. After the employment of new sitting posture, the work risk was lower 48.41% or moderate risk. When considering in details, it was found that: 1) the traditional sitting posture could create work risk at 88.89% while the new sitting posture could create the work risk at 58.86%. 2) About the environmental pollution, with the traditional sitting posture, workers exposed to the polluted fume from welding at 61.11% while with the new sitting posture workers exposed to the polluted fume from welding at 40.47%. 3) On accidental risk, with the traditional sitting posture, workers exposed to the accident from welding at 94.44% while with the new sitting posture workers exposed to the accident from welding at 62.54%.

Keywords: occupational health safety, environment at work, Monk’s food bowl, machine intelligence

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Authors: Achirawatt Tungsombatsanti, Toansakul Santiboon, Kamon Ponkham

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Strategies of the STEM education was aimed to prepare of an interdisciplinary and applied approach for the instructional of science, technology, engineering, and mathematics in an integrated students for enhancing engagement of their science skills to the Problem-Based Learning (PBL) method in Borabu School with a sample consists of 80 students in 2 classes at the 12th grade level of their learning achievements on electromagnetic issue. Research administrations were to separate on two different instructional model groups, the 40-experimental group was designed with the STEM instructional experimenting preparation and induction in a 40-student class and the controlling group using the PBL was designed to students identify what they already know, what they need to know, and how and where to access new information that may lead to the resolution of the problem in other class. The learning environment perceptions were obtained using the 35-item Physics Laboratory Environment Inventory (PLEI). Students’ creating attitude skills’ sustainable development toward physics were assessed with the Test Of Physics-Related Attitude (TOPRA) The term scaling was applied to the attempts to measure the attitude objectively with the TOPRA was used to assess students’ perceptions of their science attitude toward physics. Comparisons between pretest and posttest techniques were assessed students’ learning achievements on each their outcomes from each instructional model, differently. The results of these findings revealed that the efficiency of the PLB and the STEM based on criteria indicate that are higher than the standard level of the 80/80. Statistically, significant of students’ learning achievements to their later outcomes on the controlling and experimental physics class groups with the PLB and the STEM instructional designs were differentiated between groups at the .05 level, evidently. Comparisons between the averages mean scores of students’ responses to their instructional activities in the STEM education method are higher than the average mean scores of the PLB model. Associations between students’ perceptions of their physics classes to their attitudes toward physics, the predictive efficiency R2 values indicate that 77%, and 83% of the variances in students’ attitudes for the PLEI and the TOPRA in physics environment classes were attributable to their perceptions of their physics PLB and the STEM instructional design classes, consequently. An important of these findings was contributed to student understanding of scientific concepts, attitudes, and skills as evidence with STEM instructional ought to higher responding than PBL educational teaching. Statistically significant between students’ learning achievements were differentiated of pre and post assessments which overall on two instructional models.

Keywords: learning instructional managements, problem-based learning, STEM education, method, enhancement, students learning achievements, science attitude, physics classes

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Authors: Maryam Heidari, James H. Jones Jr.

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Millions of online posts about different topics and products are shared on popular social media platforms. One use of this content is to provide crowd-sourced information about a specific topic, event or product. However, this use raises an important question: what percentage of information available through these services is trustworthy? In particular, might some of this information be generated by a machine, i.e., a bot, instead of a human? Bots can be, and often are, purposely designed to generate enough volume to skew an apparent trend or position on a topic, yet the consumer of such content cannot easily distinguish a bot post from a human post. In this paper, we introduce a model for social media bot detection which uses Bidirectional Encoder Representations from Transformers (Google Bert) for sentiment classification of tweets to identify topic-independent features. Our use of a Natural Language Processing approach to derive topic-independent features for our new bot detection model distinguishes this work from previous bot detection models. We achieve 94\% accuracy classifying the contents of data as generated by a bot or a human, where the most accurate prior work achieved accuracy of 92\%.

Keywords: bot detection, natural language processing, neural network, social media

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Authors: Gaweł Magdalena, Lipkowska Anna, Olbert Magdalena, Frąckiewicz Ewelina, Librowski Tadeusz, Nowak Gabriel, Pilc Andrzej

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Heme oxygenase-1 (HO-1), also known as heat shock protein 32 (HSP32), has been shown to be implicated in cytoprotection in various organs. Its activation plays a significant role in acute and chronic inflammation, protecting cells from oxidative injury and apoptosis. This inducible isoform of HO catalyzes the first and rate-limiting step in heme degradation to produce equimolar quantities of biologically active products: carbon monoxide (CO), free iron and biliverdin. CO has been reported to possess anti-apoptotic properties. Moreover, it inhibits the production of proinflammatory cytokines and stimulates the synthesis of the anti-inflammatory interleukin-10 (IL-10), as well as promotes vasodilatation at sites of inflammation. The second product of catalytic HO-1 activity, free cytotoxic iron, is promptly sequestered into the iron storage protein ferritin, which lowers the pro-oxidant state of the cell. The third product, biliverdin, is subsequently converted by biliverdin reductase into the bile pigment bilirubin, the most potent endogenous antioxidant among the constituents of human serum, which modulates immune effector functions and suppresses inflammatory response. Furthermore, being one of the so-called stress proteins, HO-1 adaptively responds to different stressors, such as reactive oxygen species (ROS), inflammatory cytokines and heavy metals and thus protects cells against such conditions as ischemia, hemorrhagic shock, heat shock or hypoxia. It is suggested that pharmacologic modulation of HO-1 may represent an effective strategy for prevention of stress and drug-induced gastrointestinal toxicity. HO-1 is constitutively expressed in normal gastric, intestinal and colonic mucosa and up-regulated during inflammation. It has been proven that HO-1 up-regulated by hemin, heme and cobalt-protoporphyrin ameliorates experimental colitis. In addition, the up-regulation of HO-1 partially explains the mechanism of action of 5-aminosalicylic acid (5-ASA), which is used clinically as an anti-colitis agent. In 2009 Ueda et al. has reported for the first time that mucosal protection by Polaprezinc, a chelate compound of zinc and L-carnosine used as an anti-ulcer drug in Japan, is also attributed to induction of HO-1 in the stomach. Since then, inducers of HO-1 are desired subject of research, as they may constitute therapeutically effective anti-ulcer drugs.

Keywords: heme oxygenase-1, gastric lesions, gastroprotection, Polaprezinc

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Authors: Tamar Lazariashvili

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Introduction: The growth and development of the economy in the country depend on many factors, the most important of which is the use of innovation. The article analyzes the innovations and the potential of agricultural development in Georgia, presents the problems in the field, justifies the need to introduce innovations, shows the policy of innovation development, evaluates the positive and negative factors of the use of innovations in agriculture. Methodology: The article uses general and specific research methods, namely, analysis, synthesis, induction, deduction, comparison and statistical ones: selection, grouping, observation, trend. All these methods used together in the article reveal the main problems and challenges and their development trends. Main Findings: The introduction of innovations for the country has an impact if there is established state support system for business development and the State creates an effective environment for innovation development. As a result, the appropriate establishment gives incentives to increase budget revenues, create new jobs, increase export turnover and improve the overall economic situation in the country. Georgia has sufficient resource potential to create and develop new businesses in agriculture by introducing innovations and contribute to the further socio-economic development of the country. Political and economic stability, the existing legislation in the country, infrastructure, the proper functioning of financial institutions and the qualification of the workforce are crucial for the development of innovations. These criteria determine the political and economic ratings of all countries of the world, which are of great importance to foreign investors in the implementation of innovations. Conclusion: Enactment of agro-insurance will increase the interest and confidence of financial institutions in the farming sector, financial resources will be accessible to the farmers that will facilitate the stable development of the sector in the country. The size of the agro-insurance market in the country should be increased and the new territories should be covered. The State must have an obligation to ensure the risk of farmers and subsidize insurance companies. Based on an analysis of the insurance market the conclusions on agro-insurance issues and the relevant recommendations are proposed. The introduction of innovations in agriculture will have a great impact on the Georgian economy: it will improve the technological base, establish enterprises equipped with modern equipment and methodologies, retrain existing enterprises, promote to improve skills of workers and improve management systems. Based on the analysis, conclusions are made about the prospects for the development of innovation in agriculture and relevant recommendations are proposed.

Keywords: agriculture, development potential, innovation, optimal environment

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Authors: Ambra Giovannelli, Erika Maria Archilei

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The amount of the electrical power required by refrigeration systems is relevant worldwide. It is evaluated in the order of 15% of the total electricity production taking refrigeration and air-conditioning into consideration. For this reason, in the last years several energy saving techniques have been proposed to reduce the power demand of such plants. The paper deals with the development of an innovative internal recovery system for cryogenic cooling plants. Such a system consists in a Compressor-Expander Group (CEG) designed on the basis of the automotive turbocharging technology. In particular, the paper is focused on the design of the expander, the critical component of the CEG system. Due to the low volumetric flow entering the expander and the high expansion ratio, a commercial turbocharger expander wheel was strongly modified. It was equipped with a transonic nozzle, designed to have a radially inflow full admission. To verify the performance of such a machine and suggest improvements, two different set of nozzles have been designed and modelled by means of the commercial Ansys-CFX software. steady-state 3D CFD simulations of the second-generation prototype are presented and compared with the initial ones.

Keywords: vapour cCompression systems, energy saving, refrigeration plant, organic fluids, radial turbine

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Authors: Zanele Ntshidi, Sebinasi Dzikiti, Dominic Mazvimavi

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South Africa is a dry country and yet it is ranked as the 8th largest exporter of fresh apples (Malus Domestica) globally. Prime apple producing regions are in the Eastern and Western Cape Provinces of the country where all the fruit is grown under irrigation. Climate change models predict increasingly drier future conditions in these regions and the frequency and severity of droughts is expected to increase. For the sustainability and growth of the fruit industry it is important to minimize non-beneficial water losses from the orchard floor. The aims of this study were firstly to compare the water use of cover crop species used in South African orchards for which there is currently no information. The second aim was to investigate how orchard water use (evapotranspiration) was partitioned into beneficial (tree transpiration) and non-beneficial (orchard floor evaporation) water uses for micro-sprinkler irrigated orchards with different canopy covers. This information is important in order to explore opportunities to minimize non-beneficial water losses. Six cover crop species (four exotic and two indigenous) were grown in 2 L pots in a greenhouse. Cover crop transpiration was measured using the gravimetric method on clear days. To establish how water use was partitioned in orchards, evapotranspiration (ET) was measured using an open path eddy covariance system, while tree transpiration was measured hourly throughout the season (October to June) on six trees per orchard using the heat ratio sap flow method. On selected clear days, soil evaporation was measured hourly from sunrise to sunset using six micro-lysimeters situated at different wet/dry and sun/shade positions on the orchard floor. Transpiration of cover crops was measured using miniature (2 mm Ø) stem heat balance sap flow gauges. The greenhouse study showed that exotic cover crops had significantly higher (p < 0.01) average transpiration rates (~3.7 L/m2/d) than the indigenous species (~ 2.2 L/m²/d). In young non-bearing orchards, orchard floor evaporative fluxes accounted for more than 60% of orchard ET while this ranged from 10 to 30% in mature orchards with a high canopy cover. While exotic cover crops are preferred by most farmers, this study shows that they use larger quantities of water than indigenous species. This in turn contributes to a larger orchard floor evaporation flux. In young orchards non-beneficial losses can be minimized by adopting drip or short range micro-sprinkler methods that reduce the wetted soil fraction thereby conserving water.

Keywords: evapotranspiration, sap flow, soil evaporation, transpiration

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Authors: M. Khairudin

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This paper investigates the system identification and design quantitative feedback theory (QFT) for the robust control of a lathe spindle. The dynamic of the lathe spindle is uncertain and time variation due to the deepness variation on cutting process. System identification was used to obtain the dynamics model of the lathe spindle. In this work, real time system identification is used to construct a linear model of the system from the nonlinear system. These linear models and its uncertainty bound can then be used for controller synthesis. The real time nonlinear system identification process to obtain a set of linear models of the lathe spindle that represents the operating ranges of the dynamic system. With a selected input signal, the data of output and response is acquired and nonlinear system identification is performed using Matlab to obtain a linear model of the system. Practical design steps are presented in which the QFT-based conditions are formulated to obtain a compensator and pre-filter to control the lathe spindle. The performances of the proposed controller are evaluated in terms of velocity responses of the the lathe machine spindle in corporating deepness on cutting process.

Keywords: lathe spindle, QFT, robust control, system identification

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Authors: Hao Mi, Ming Yang, Tian-yue Yang

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Ultrasonic infrared nondestructive testing is a kind of testing method with high speed, accuracy and localization. However, there are still some problems, such as the detection requires manual real-time field judgment, the methods of result storage and viewing are still primitive. An intelligent non-destructive detection system based on embedded linux is put forward in this paper. The hardware part of the detection system is based on the ARM (Advanced Reduced Instruction Set Computer Machine) core and an embedded linux system is built to realize image processing and defect detection of thermal images. The CLAHE algorithm and the Butterworth filter are used to process the thermal image, and then the boa server and CGI (Common Gateway Interface) technology are used to transmit the test results to the display terminal through the network for real-time monitoring and remote monitoring. The system also liberates labor and eliminates the obstacle of manual judgment. According to the experiment result, the system provides a convenient and quick solution for industrial non-destructive testing.

Keywords: remote monitoring, non-destructive testing, embedded Linux system, image processing

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Authors: Alexander Goncharenko, Andrey Denisov, Sergey Alyamkin, Evgeny Terentev

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The neural network quantization is highly desired procedure to perform before running neural networks on mobile devices. Quantization without fine-tuning leads to accuracy drop of the model, whereas commonly used training with quantization is done on the full set of the labeled data and therefore is both time- and resource-consuming. Real life applications require simplification and acceleration of quantization procedure that will maintain accuracy of full-precision neural network, especially for modern mobile neural network architectures like Mobilenet-v1, MobileNet-v2 and MNAS. Here we present a method to significantly optimize training with quantization procedure by introducing the trained scale factors for discretization thresholds that are separate for each filter. Using the proposed technique, we quantize the modern mobile architectures of neural networks with the set of train data of only ∼ 10% of the total ImageNet 2012 sample. Such reduction of train dataset size and small number of trainable parameters allow to fine-tune the network for several hours while maintaining the high accuracy of quantized model (accuracy drop was less than 0.5%). Ready-for-use models and code are available in the GitHub repository.

Keywords: distillation, machine learning, neural networks, quantization

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Authors: Juliza Hidayati, Yesie M. Sinuhaji, Sawarni Hasibuan

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A company producing drinking water through many incompatibility issues that affect supply chain performance. The study was conducted to determine the factors that affect the performance of the supply chain and improve it. To obtain the dominant factors affecting the performance of the supply chain used Analytic Network Process, while to improve performance is done by using Kaizen. Factors affecting the performance of the supply chain to be a reference to identify the cause of the non-conformance. Results weighting using ANP indicates that the dominant factor affecting the level of performance is the precision of the number of shipments (15%), the ability of the fulfillment of the booking amount (12%), and the number of rejected products when signing (12%). Incompatibility of the factors that affect the performance of the supply chain are identified, so that found the root cause of the problem is most dominant. Based on the weight of Risk Priority Number (RPN) gained the most dominant root cause of the problem, namely the poorly maintained engine, the engine worked for three shifts, machine parts that are not contained in the plant. Improvements then performed using the Kaizen method of systematic and sustainable.

Keywords: analytic network process, booking amount, risk priority number, supply chain performance

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Authors: Bachir Chemani, Rachid Halfaoui

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The aim of study was to evaluate pressure distribution characteristics of the elastic textile bandages using two instrumental techniques: a prototype Instrument and a load Transference. The prototype instrument which simulates shape of real leg has pressure sensors which measure bandage pressure. Using this instrument, the results show that elastic textile bandages presents different pressure distribution characteristics and none produces a uniform distribution around lower limb. The load transference test procedure is used to determine whether a relationship exists between elastic textile bandage structure and pressure distribution characteristics. The test procedure assesses degree of load, directly transferred through a textile when loads series are applied to bandaging surface. A range of weave fabrics was produced using needle weaving machine and a sewing technique. A textile bandage was developed with optimal characteristics far superior pressure distribution than other bandages. From results, we find that theoretical pressure is not consistent exactly with practical pressure. It is important in this study to make a practical application for specialized nurses in order to verify the results and draw useful conclusions for predicting the use of this type of elastic band.

Keywords: textile, cotton, pressure, venous ulcers, elastic

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Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: Yi-Ping Lo, Shi-Yao Wei, Chih-Chun Ma

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Since Google launched Google Glass in 2012, numbers of commercial wearable devices were released, such as smart belt, smart band, smart shoes, smart clothes ... etc. However, most of these devices perform as sensors to show the readings of measurements and few of them provide the interactive feedback to the user. Furthermore, these devices are single task devices which are not able to communicate with each other. In this paper a new health promotion system, Ubiquitous Life People Informatics Engine (U-Life PIE), will be presented. This engine consists of People Informatics Engine (PIE) and the interactive user interface. PIE collects all the data from the compatible devices, analyzes this data comprehensively and communicates between devices via various application programming interfaces. All the data and informations are stored on the PIE unit, therefore, the user is able to view the instant and historical data on their mobile devices any time. It also provides the real-time hands-free feedback and instructions through the user interface visually, acoustically and tactilely. These feedback and instructions suggest the user to adjust their posture or habits in order to avoid the physical injuries and prevent illness.

Keywords: machine learning, wearable devices, user interface, user experience, internet of things

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Authors: Ashish Ankus Yerunkar, Ravi Terkar

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Study aimed at evaluating the best process environment which could simultaneously satisfy requirements of both quality as well as productivity with special emphasis on reduction of cutting tool flank wear, because reduction in flank wear ensures increase in tool life. The predicted optimal setting ensured minimization of surface roughness. Purpose of this paper is focused on the analysis of optimum cutting conditions to get lowest surface roughness in turning SCM 440 alloy steel by Taguchi method. Design for the experiment was done using Taguchi method and 18 experiments were designed by this process and experiments conducted. The results are analyzed using ANOVA method. Taguchi method has depicted that the depth of cut has significant role to play in producing lower surface roughness followed by feed. The Cutting speed has lesser role on surface roughness from the tests. The vibrations of the machine tool, tool chattering are the other factors which may contribute poor surface roughness to the results and such factors ignored for analyses. The inferences by this method will be useful to other researches for similar type of study and may be vital for further research on tool vibrations, cutting forces etc.

Keywords: surface roughness (ra), machining, dry turning, taguchi method, turning process, anova method, mahr perthometer

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Authors: Lenon M. Pereira, Helen J. Khoury, Marcos E. A. Andrade, Dean L. Cutajar, Vinicius S. M. Barros, Anatoly B. Rozenfeld

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With the increase of beam widths and the advent of multiple-slice and helical scanners, concerns related to the current dose measurement protocols and instrumentation in computed tomography (CT) have arisen. The current methodology of dose evaluation, which is based on the measurement of the integral of a single slice dose profile using a 100 mm long cylinder ionization chamber (Ca,100 and CPPMA, 100), has been shown to be inadequate for wide beams as it does not collect enough of the scatter-tails to make an accurate measurement. In addition, a long ionization chamber does not offer a good representation of the dose profile when tube current modulation is used. An alternative approach has been suggested by translating smaller detectors through the beam plane and assessing the accumulated dose trough the integral of the dose profile, which can be done for any arbitrary length in phantoms or in the air. For this purpose, a MOSFET dosimeter of small dosimetric volume was used. One of its recently designed versions is known as the MOSkin, which is developed by the Centre for Medical Radiation Physics at the University of Wollongong, and measures the radiation dose at a water equivalent depth of 0.07 mm, allowing the evaluation of skin dose when placed at the surface, or internal point doses when placed within a phantom. Thus, the aim of this research was to characterize the response of the MOSkin dosimeter for X-ray CT beams and to evaluate its application for the accumulated dose assessment. Initially, tests using an industrial x-ray unit were carried out at the Laboratory of Ionization Radiation Metrology (LMRI) of Federal University of Pernambuco, in order to investigate the sensitivity, energy dependence, angular dependence, and reproducibility of the dose response for the device for the standard radiation qualities RQT 8, RQT 9 and RQT 10. Finally, the MOSkin was used for the accumulated dose evaluation of scans using a Philips Brilliance 6 CT unit, with comparisons made between the CPPMA,100 value assessed with a pencil ionization chamber (PTW Freiburg TW 30009). Both dosimeters were placed in the center of a PMMA head phantom (diameter of 16 cm) and exposed in the axial mode with collimation of 9 mm, 250 mAs and 120 kV. The results have shown that the MOSkin response was linear with doses in the CT range and reproducible (98.52%). The sensitivity for a single MOSkin in mV/cGy was as follows: 9.208, 7.691 and 6.723 for the RQT 8, RQT 9 and RQT 10 beams qualities respectively. The energy dependence varied up to a factor of ±1.19 among those energies and angular dependence was not greater than 7.78% within the angle range from 0 to 90 degrees. The accumulated dose and the CPMMA, 100 value were 3,97 and 3,79 cGy respectively, which were statistically equivalent within the 95% confidence level. The MOSkin was shown to be a good alternative for CT dose profile measurements and more than adequate to provide accumulated dose assessments for CT procedures.

Keywords: computed tomography dosimetry, MOSFET, MOSkin, semiconductor dosimetry

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Authors: Fekkak Bouazza, Menaa Mohamed, Loukriz Abdelhamid, Krim Mohamed L.

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This paper presents modeling and control strategy for the grid connected wind turbine system based on Permanent Magnet Synchronous Generator (PMSG). The considered system is based on back-to-back converter topology. The Grid Side Converter (GSC) achieves the DC bus voltage control and unity power factor. The Machine Side Converter (MSC) assures the PMSG speed control. The PMSG is used as a variable speed generator and connected directly to the turbine without gearbox. The pitch angle control is not either considered in this study. Further, Optimal Tip Speed Ratio (OTSR) based MPPT control strategy is used to ensure the most energy efficiency whatever the wind speed variations. A filter (L) is put between the GSC and the grid to reduce current ripple and to improve the injected power quality. The proposed grid connected wind system is built under MATLAB/Simulink environment. The simulation results show the feasibility of the proposed topology and performance of its control strategies.

Keywords: wind, grid, PMSG, MPPT, OTSR

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Authors: Ainur Sharip, Jose E. Perez, Nouf Alsharif, Aldo I. M. Bandeas, Enzo D. Fabrizio, Timothy Ravasi, Jasmeen S. Merzaban, Jürgen Kosel

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Bone marrow-derived human mesenchymal stem cells (MSCs) are attractive candidates for tissue engineering and regenerative medicine, due to their ability to differentiate into osteoblasts, chondrocytes or adipocytes. Differentiation is influenced by biochemical and biophysical stimuli provided by the microenvironment of the cell. Thus, altering the mechanical characteristics of a cell culture scaffold can directly influence a cell’s microenvironment and lead to stem cell differentiation. Mesenchymal stem cells were cultured on densely packed, vertically aligned magnetic iron nanowires (NWs) and the effect of NWs on the cell cytoskeleton rearrangement and differentiation were studied. An electrochemical deposition method was employed to fabricate NWs into nanoporous alumina templates, followed by a partial release to reveal the NW array. This created a cell growth substrate with free-standing NWs. The Fe NWs possessed a length of 2-3 µm, with each NW having a diameter of 33 nm on average. Mechanical stimuli generated by the physical movement of these iron NWs, in response to a magnetic field, can stimulate osteogenic differentiation. Induction of osteogenesis was estimated using an osteogenic marker, osteopontin, and a reduction of stem cell markers, CD73 and CD105. MSCs were grown on the NWs, and fluorescent microscopy was employed to monitor the expression of markers. A magnetic field with an intensity of 250 mT and a frequency of 0.1 Hz was applied for 12 hours/day over a period of one week and two weeks. The magnetically activated substrate enhanced the osteogenic differentiation of the MSCs compared to the culture conditions without magnetic field. Quantification of the osteopontin signal revealed approximately a seven-fold increase in the expression of this protein after two weeks of culture. Immunostaining staining against CD73 and CD105 revealed the expression of antibodies at the earlier time point (two days) and a considerable reduction after one-week exposure to a magnetic field. Overall, these results demonstrate the application of a magnetic NW substrate in stimulating the osteogenic differentiation of MSCs. This method significantly decreases the time needed to induce osteogenic differentiation compared to commercial biochemical methods, such as osteogenic differentiation kits, that usually require more than two weeks. Contact-free stimulation of MSC differentiation using a magnetic field has potential uses in tissue engineering, regenerative medicine, and bone formation therapies.

Keywords: cell substrate, magnetic nanowire, mesenchymal stem cell, stem cell differentiation

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Authors: Rim Chang Hyon, Song Hak Jin, Song Kwang Hyok, Jong Ki Hun

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The calibration of the articulated arm coordinate measuring machine (AACMM) is key to improving calibration accuracy and saving calibration time. To reduce the time consumed for calibration, we should choose the proper calibration gauges and develop a reasonable calibration method. In addition, we should get the exact optimal solution by accurately removing the rough errors within the experimental data. In this paper, we present a calibration method of the portable coordinate measuring arm (PCMA) using the 1.2m long bar guage with cone-holes. First, we determine the locations of the bar gauge and establish an optimal objective function for identifying the structural parameter errors. Next, we make a mathematical model of the calibration algorithm and present a new mathematical method to remove the rough errors within calibration data. Finally, we find the optimal solution to identify the kinematic parameter errors by using Levenberg-Marquardt algorithm. The experimental results show that our calibration method is very effective in saving the calibration time and improving the calibration accuracy.

Keywords: AACMM, kinematic model, parameter identify, measurement accuracy, calibration

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Authors: Haiyan Wu, Ying Liu, Shaoyun Shi

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Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors' writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. Besides, predictions by neural networks are difficult to explain, which is vital in authorship attribution tasks. In this paper, we not only utilize the statistical style and content features but also take advantage of both syntactic and semantic features. Different from an end-to-end neural model, feature selection and prediction are two steps in our method. An attentive n-gram network is utilized to select useful features, and logistic regression is applied to give prediction and understandable representation of writing style. Experiments show that our extracted features can improve the state-of-the-art methods on three benchmark datasets.

Keywords: authorship attribution, attention mechanism, syntactic feature, feature extraction

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Authors: Sayem Zafar, Mahmood Rahi

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Rigorous international maritime regulations are in place to limit boat and ship hydrocarbon emissions. The global sustainability goals are reducing the fuel consumption and minimizing the emissions from the ships and boats. These maritime sustainability goals have attracted a lot of research interest. Energy harvesting and storage system is designed in this study based on hybrid renewable and conventional energy systems. This energy harvesting and storage system is designed for marine applications, such as, boats and small ships. These systems can be utilized for mobile use or off-grid remote electrification. This study analyzed the use of micro power generation for boats and small ships. The energy harvesting and storage system has two distinct systems i.e. dockside shore-based system and on-board system. The shore-based system consists of a small wind turbine, photovoltaic (PV) panels, small gas turbine, hydrogen generator and high-pressure hydrogen storage tank. This dockside system is to provide easy access to the boats and small ships for supply of hydrogen. The on-board system consists of hydrogen storage tanks and fuel cells. The wind turbine and PV panels generate electricity to operate electrolyzer. A small gas turbine is used as a supplementary power system to contribute in case the hybrid renewable energy system does not provide the required energy. The electrolyzer performs the electrolysis on distilled water to produce hydrogen. The hydrogen is stored in high-pressure tanks. The hydrogen from the high-pressure tank is filled in the low-pressure tanks on-board seagoing vessels to operate the fuel cell. The boats and small ships use the hydrogen fuel cell to provide power to electric propulsion motors and for on-board auxiliary use. For shore-based system, a small wind turbine with the total length of 4.5 m and the disk diameter of 1.8 m is used. The small wind turbine dimensions make it big enough to be used to charge batteries yet small enough to be installed on the rooftops of dockside facility. The small dimensions also make the wind turbine easily transportable. In this paper, PV, sizing and solar flux are studied parametrically. System performance is evaluated under different operating and environmental conditions. The parametric study is conducted to evaluate the energy output and storage capacity of energy storage system. Results are generated for a wide range of conditions to analyze the usability of hybrid energy harvesting and storage system. This energy harvesting method significantly improves the usability and output of the renewable energy sources. It also shows that small hybrid energy systems have promising practical applications.

Keywords: energy harvesting, fuel cell, hybrid energy system, hydrogen, wind turbine

Procedia PDF Downloads 139

Authors: Sufia Maroof

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This empirical study investigated the web-translations of five Pakistani public offices (FPSC, FIA, HEC, USB, and Ministry of Finance) offering Urdu tab as an option to access information on their official websites. Triangulation of quantitative and qualitative research design informed the researcher of the semantic, lexical and syntactic caveats in these translations. The study hypothesized that majority of the Pakistani population is oblivious of the Supreme Court’s amendments in language policy concerning national and official language; hence, Urdu web-translations of the public departments have not been accessed effectively. Firstly, the researcher conducted an online survey, comprising of two sections, close ended and short answer based questions. Secondly, the researcher compiled corpus of the five selected websites in a tabular form to compare the data. Thirdly, the administrators of the departments had been contacted regarding the methods of translation and the expertise of the personnel involved. The corpus was assessed for TQA after examining the lexical, semantic, syntactical and technical alignment inaccuracies and imperfections. The study suggests the public offices to invest in their Urdu webs by either hiring expert translators or engaging expertise of a translation agency for this project to offer quality translation to public.

Keywords: machine translations, public offices, Urdu translations, websites

Procedia PDF Downloads 128

Authors: Saim Iftikhar Awan, Farhan Ali

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Wind power has now become one of the most important resources of renewable energy. The machine which extracts kinetic energy from wind is wind turbine. This work is all about the electrical power analysis of horizontal axis wind turbine to check the efficiency of different configurations of wind turbines to get maximum output and comparison of experimental and Computational Fluid Dynamics (CFD) results. Different experiments have been performed to obtain that configuration with the help of which we can get the maximum electrical power output by changing the different parameters like the number of blades, blade shape, wind speed, etc. in first step experimentation is done, and then the similar configuration is designed in 3D CAD software. After a series of experiments, it has been found that the turbine with four blades at an angle of 75° gives maximum power output and increase in wind speed increases the power output. The models designed on CAD software are imported on ANSYS-FLUENT to predict mechanical power. This mechanical power is then converted into electrical power, and the results were approximately the same in both cases. In the end, a comparison has been done to compare the results of experiments and ANSYS-FLUENT.

Keywords: computational analysis, power efficiency, wind energy, wind turbine

Procedia PDF Downloads 162

Authors: Yassamina Berkane, Leila Kloul, Yoann Demoli

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Quality of life, environmental impact, congestion of mobility means, and infrastructures remain significant challenges for urban mobility. Solutions like car sharing, spatial redesign, eCommerce, and autonomous vehicles will likely increase the unit veh-km and the density of cars in urban traffic, thus reducing congestion. However, the impact of such solutions is not clear for researchers. Congestion arises from growing populations that must travel greater distances to arrive at similar locations (e.g., workplaces, schools) during the same time frame (e.g., rush hours). This paper first reviews the research and application cases of urban congestion methods through recent years. Rethinking the question of time, it then investigates people’s willingness and flexibility to adapt their arrival and departure times from workplaces. We use neural networks and methods of supervised learning to apply a new methodology for predicting peoples' intentions from their responses in a questionnaire. We created and distributed a questionnaire to more than 50 companies in the Paris suburb. Obtained results illustrate that our methodology can predict peoples' intentions to reschedule their activities (work, study, commerce, etc.).

Keywords: urban mobility, decongestion, machine learning, neural network

Procedia PDF Downloads 195

Authors: Nety Trisnawaty, Mirna Febriani

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The use of stainless steel wires in the field of dentistry is widely used, especially for orthodontic and prosthodontic treatment using stainless steel wire. The oral cavity is the ideal environment for corrosion, which can be caused by saliva. Prevention of corrosion on stainless steel wires can be done by using an organic or non-organic corrosion inhibitor. One of the organic inhibitors that can be used to prevent corrosion is black tea leaves extracts. To explain the comparison of chromium ions release for stainlees steel between artificial saliva and black tea leaves extracts. In this research we used artificial saliva, black tea leaves extracts, stainless steel wire and using Atomic Absorption Spectrophometric testing machine. The samples were soaked for 1, 3, 7 and 14 days in the artificial saliva and black tea leaves extracts. The results showed the difference of chromium ion release soaked in artificial saliva and black tea leaves extracts on days 1, 3, 7 and 14. Statistically, calculation with independent T-test with p < 0,05 showed a significant difference. The longer the duration of days, the more ion chromium were released. The conclusion of this study shows that black tea leaves extracts can inhibit the corrosion rate of stainless steel wires.

Keywords: chromium ion, stainless steel, artificial saliva, black tea leaves extracts

Procedia PDF Downloads 281

Authors: Simon Caba, Raja Abou Ackl, Svend Rasmussen, Nicholas E. Pedersen

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It is a challenge to directly monitor cavitation in a pump application during operation because of a lack of visual access to validate the presence of cavitation and its form of appearance. In this work, experimental investigations are carried out in an inline single-stage centrifugal pump with optical access. Hence, it gives the opportunity to enhance the value of CFD tools and standard cavitation measurements. Experiments are conducted using two impellers running in the same volute at 3000 rpm and the same flow rate. One of the impellers used is optimized for lower NPSH₃% by its blade design, whereas the other one is manufactured using a standard casting method. The cavitation is detected by pump performance measurements, vibration measurements and high-speed image recordings. The head drop and the pump casing vibration caused by cavitation are correlated with the visual appearance of the cavitation. The vibration data is recorded in an axial direction of the impeller using accelerometers recording at a sample rate of 131 kHz. The vibration frequency domain data (up to 20 kHz) and the time domain data are analyzed as well as the root mean square values. The high-speed recordings, focusing on the impeller suction side, are taken at 10,240 fps to provide insight into the flow patterns and the cavitation behavior in the rotating impeller. The videos are synchronized with the vibration time signals by a trigger signal. A clear correlation between cloud collapses and abrupt peaks in the vibration signal can be observed. The vibration peaks clearly indicate cavitation, especially at higher NPSHA values where the hydraulic performance is not affected. It is also observed that below a certain NPSHA value, the cavitation started in the inlet bend of the pump. Above this value, cavitation occurs exclusively on the impeller blades. The impeller optimized for NPSH₃% does show a lower NPSH₃% than the standard impeller, but the head drop starts at a higher NPSHA value and is more gradual. Instabilities in the head drop curve of the optimized impeller were observed in addition to a higher vibration level. Furthermore, the cavitation clouds on the suction side appear more unsteady when using the optimized impeller. The shape and location of the cavitation are compared to 3D fluid flow simulations. The simulation results are in good agreement with the experimental investigations. In conclusion, these investigations attempt to give a more holistic view on the appearance of cavitation by comparing the head drop, vibration spectral data, vibration time signals, image recordings and simulation results. Data indicates that a criterion for cavitation detection could be derived from the vibration time-domain measurements, which requires further investigation. Usually, spectral data is used to analyze cavitation, but these investigations indicate that the time domain could be more appropriate for some applications.

Keywords: cavitation, centrifugal pump, head drop, high-speed image recordings, pump vibration

Procedia PDF Downloads 181

Authors: H. K. Shivanand, Ranjith R. Hombal, Paraveej Shirahatti, Gujjalla Anil Babu, S. ShivaPrakash

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When it comes to the selection of materials the knowledge of materials science plays a vital role in selection and enhancements of materials properties. In the world of material science a composite material has the significant role based on its application. The composite materials are those in which two or more components having different physical and chemical properties are combined to create a new enhanced property substance. In this study three different materials (Kenaf, Kevlar and Graphene) been chosen based on their properties and a composite material is developed with help of vacuum bagging process. The fibers (Kenaf and Kevlar) and Resin(vinyl ester) ratio was maintained at 70:30 during the process and 0.5% 1% and 1.5% of Graphene was added during fabrication process. The material was machined to thedimension ofASTM standards(300×300mm and thickness 3mm)with help of water jet cutting machine. The composite materials were tested for Mechanical properties such as Interlaminar shear strength(ILSS) and Flexural strength. It is found that there is significant increase in material properties in the developed composite material.

Keywords: Kevlar, Kenaf, graphene, vacuum bagging process, Interlaminar shear strength test, flexural test

Procedia PDF Downloads 95

Authors: Rawaa H. El-Bidweihy, Hisham M. Abdelsalam, Ihab A. El-Khodary

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In an era where machines run and shape our world, the need for a stable, non-ending source of energy emerges. In this study, the focus was on the solar energy in Egypt as a renewable source, the most important factors that could affect the solar energy’s market share throughout its life cycle production were analyzed and filtered, the relationships between them were derived before structuring a Bayesian network. Also, forecasted models were built for multiple factors to predict the states in Egypt by 2035, based on historical data and patterns, to be used as the nodes’ states in the network. 37 factors were found to might have an impact on the use of solar energy and then were deducted to 12 factors that were chosen to be the most effective to the solar energy’s life cycle in Egypt, based on surveying experts and data analysis, some of the factors were found to be recurring in multiple stages. The presented Bayesian network could be used later for scenario and decision analysis of using solar energy in Egypt, as a stable renewable source for generating any type of energy needed.

Keywords: ARIMA, auto correlation, Bayesian network, forecasting models, life cycle, partial correlation, renewable energy, SARIMA, solar energy

Procedia PDF Downloads 157

Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

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In the precision agriculture area, one of the main tasks is the automated detection of diseases in crops. Machine Learning algorithms have been studied in recent decades for such tasks in view of their potential for improving economic outcomes that automated disease detection may attain over crop fields. The latest generation of deep learning convolution neural networks has presented significant results in the area of image classification. In this way, this work has tested the implementation of an architecture of deep learning convolution neural network for the detection of diseases in different types of crops. A data augmentation strategy was used to meet the requirements of the algorithm implemented with a deep learning framework. Two test scenarios were deployed. The first scenario implemented a neural network under images extracted from a controlled environment while the second one took images both from the field and the controlled environment. The results evaluated the generalisation capacity of the neural networks in relation to the two types of images presented. Results yielded a general classification accuracy of 59% in scenario 1 and 96% in scenario 2.

Keywords: convolutional neural networks, deep learning, disease detection, precision agriculture

Procedia PDF Downloads 260