Search results for: thermal network

Authors: Afif Saleh Abugharsa

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The IEEE 802.15.4 standard is designed for low-rate wireless personal area networks (LR-WPAN) with focus on enabling wireless sensor networks. It aims to give a low data rate, low power consumption, and low cost wireless networking on the device-level communication. The objective of this study is to investigate the performance of IEEE 802.15.4 based networks using simulation tool. In this project the network simulator 2 NS2 was used to several performance measures of wireless sensor networks. Three scenarios were considered, multi hop network with a single coordinator, star topology, and an ad hoc on demand distance vector AODV. Results such as packet delivery ratio, hop delay, and number of collisions are obtained from these scenarios.

Keywords: ZigBee, wireless sensor networks, IEEE 802.15.4, low power, low data rate

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Authors: Humeyra Bolakar Tosun

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The transport sector has an undisputed place in human life. People need transport access to continuous increase day by day with growing population. The number of rail network, urban transport planning, infrastructure improvements, transportation management and other related areas is a key factor affecting our country made it quite necessary to improve the work of transportation. In this context, it plays an important role in domestic rail freight demand planning. Alternatives that the increase in the transportation field and has made it mandatory requirements such as the demand for improving transport quality. In this study generally is known and used in studies by the definition, rail freight transport, railway line length, population, energy consumption. In this study, Iron Road Load Net Demand was modeled by multiple regression and ANN methods. In this study, model dependent variable (Output) is Iron Road Load Net demand and 6 entries variable was determined. These outcome values extracted from the model using ANN and regression model results. In the regression model, some parameters are considered as determinative parameters, and the coefficients of the determinants give meaningful results. As a result, ANN model has been shown to be more successful than traditional regression model.

Keywords: railway load carrying, neural network, modelling transport, transportation

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Authors: Sawsan M. H. Domi

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Approximately 50% of the energy used in buildings, including houses, provide environmental comfortable levels of thermal living. In Khartoum - the city under study- cooling uses the largest portion of energy and the basic idea of Low energy houses is to minimize energy consumption. Therefore, houses are designed to use natural climate strategies to provide thermal comfort. Strategies such as semi-open spaces, shading devices, small high windows and thick walls. The study aims to review these strategies and then, apply them. It aims to change house microclimate by using vegetation, green areas, and other components. A low energy house is being designed s. It will be the first low energy house in Khartoum designed to create a low-cost energy efficient building without any mechanical systems. Three different types of houses in Khartoum are examined and evaluated according to their energy loads which provides the basis for the designed house. The designed house uses passive design strategies to reduce the need for cooling. These results show that the house reduced energy cooling loads by more than 60% compared to the average of the three given types. The design house is economically viable when taking into consideration the energy prices in Sudan.

Keywords: building envelope, climate, energy loads, ventilation

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Authors: John M. Lyne, K. Andrea Scott

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In fluid dynamics, direct numerical simulations (DNS) of turbulent flows require large amounts of nodes to appropriately resolve all scales of energy transfer. Due to the size of these databases, sharing these datasets amongst the academic community is a challenge. Recent work has been done to investigate the use of super-resolution to enable database sharing, where a low-resolution flow field is super-resolved to high resolutions using a neural network. Recently, Generative Adversarial Networks (GAN) have grown in popularity with impressive results in the generation of faces, landscapes, and more. This work investigates the generation of unique high-resolution channel flow velocity fields from a low-dimensional latent space using a GAN. The training objective of the GAN is to generate samples in which the distribution of the generated samplesis ideally indistinguishable from the distribution of the training data. In this study, the network is trained using samples drawn from a statistically stationary channel flow at a Reynolds number of 560. Results show that the turbulent statistics and energy spectra of the generated flow fields are within reasonable agreement with those of the DNS data, demonstrating that GANscan produce the intricate multi-scale phenomena of turbulence.

Keywords: computational fluid dynamics, channel flow, turbulence, generative adversarial network

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Authors: Nadia Allouache

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Solar radiation is by far the largest and the most world’s abundant, clean and permanent energy source. The amount of solar radiation intercepted by the Earth is much higher than annual global energy use. The energy available from the sun is greater than about 5200 times the global world’s need in 2006. In recent years, many promising technologies have been developed to harness the sun's energy. These technologies help in environmental protection, economizing energy, and sustainable development, which are the major issues of the world in the 21st century. One of these important technologies is the solar cooling systems that make use of either absorption or adsorption technologies. The solar adsorption cooling systems are good alternative since they operate with environmentally benign refrigerants that are natural, free from CFCs, and therefore they have a zero ozone depleting potential (ODP). A numerical analysis of thermal and solar performances of an adsorption solar refrigerating system using different adsorbent/adsorbate pairs such as activated carbon AC35 and activated carbon BPL/Ammoniac; is undertaken in this study. The modeling of the adsorption cooling machine requires the resolution of the equation describing the energy and mass transfer in the tubular adsorber that is the most important component of the machine. The Wilson and Dubinin- Astakhov models of the solid-adsorbat equilibrium are used to calculate the adsorbed quantity. The porous medium is contained in the annular space and the adsorber is heated by solar energy. Effect of key parameters on the adsorbed quantity and on the thermal and solar performances are analysed and discussed. The performances of the system that depends on the incident global irradiance during a whole day depends on the weather conditions: the condenser temperature and the evaporator temperature. The AC35/methanol pair is the best pair comparing to the BPL/Ammoniac in terms of system performances.

Keywords: activated carbon-methanol pair, activated carbon-ammoniac pair, adsorption, performance coefficients, numerical analysis, solar cooling system

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Authors: Valentina V. Zhurikhina, Mihail I. Petrov, Alexandra A. Rtischeva, Mark Dussauze, Thierry Cardinal, Andrey A. Lipovskii

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Polarization of glass, often referred to as thermal poling, is a well-known method to modify the glass physical and chemical properties, that manifest themselves in loosing central symmetry of the medium, glass structure and refractive index modification. The usage of the poling for second optical harmonic generation, fabrication of optical waveguides and electrooptic modulators was also reported. Nevertheless, the detailed description of the poling of glasses, containing multiple charge carriers is still under discussion. In particular, the role of possible migration of electrons in the space charge formation usually remains out of the question. In this work, we performed the numerical simulation of thermal poling of a silicate glass, containing Na, K, Mg, and Ca. We took into consideration the contribution of electrons in the polarization process. The possible explanation of migration of electrons can be the break of non-bridging oxygen bonds. It was found, that the modeled depth of the space charge region is about 10 times higher if the migration of the negative charges is taken under consideration. The simulated profiles of cations, participating in the polarization process, are in a good agreement with the experimental data, obtained by glow discharge spectroscopy.

Keywords: glass poling, charge transport, modeling, concentration profiles

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Authors: Atwari Rawani, Hari Narayan Singh, K. D. P. Singh

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In this paper, an analytical investigation of the enhancement of thermal performance of parabolic trough collector (PTC) with twisted tape inserts in the absorber tube is being reported. A comparative study between the absorber with various types of twisted tape inserts and plain tube collector has been performed in turbulent flows conditions. The parametric studies were conducted to investigate the effects of system and operating parameters on the performance of the collector. The parameters such as heat gain, overall heat loss coefficient, air rise temperature and efficiency are used to analyze the relative performance of PTC. The results show that parabolic through collector with serrated twisted tape insert shows the best performance under same set of conditions under range of parameters investigated. Results reveal that for serrated twisted tape with x=1, Nusselt number/heat transfer coefficient is found to be 4.38 and 3.51 times over plain absorber of PTC at mass flow rate of 0.06 kg/s and 0.16 kg/s respectively; while corresponding enhancement in thermal efficiency is 15.7% and 5.41% respectively.

Keywords: efficiency, heat transfer, twisted tape ratio, turbulent flow

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Authors: Ammarah Irum, Muhammad Ali Tahir

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Document-level sentiment analysis in Urdu is a challenging Natural Language Processing (NLP) task due to the difficulty of working with lengthy texts in a language with constrained resources. Deep learning models, which are complex neural network architectures, are well-suited to text-based applications in addition to data formats like audio, image, and video. To investigate the potential of deep learning for Urdu sentiment analysis, we implemented five different deep learning models, including Bidirectional Long Short Term Memory (BiLSTM), Convolutional Neural Network (CNN), Convolutional Neural Network with Bidirectional Long Short Term Memory (CNN-BiLSTM), and Bidirectional Encoder Representation from Transformer (BERT). In this study, we developed a hybrid deep learning model called BiLSTM-Single Layer Multi Filter Convolutional Neural Network (BiLSTM-SLMFCNN) by fusing BiLSTM and CNN architecture. The proposed and baseline techniques are applied on Urdu Customer Support data set and IMDB Urdu movie review data set by using pre-trained Urdu word embedding that are suitable for sentiment analysis at the document level. Results of these techniques are evaluated and our proposed model outperforms all other deep learning techniques for Urdu sentiment analysis. BiLSTM-SLMFCNN outperformed the baseline deep learning models and achieved 83%, 79%, 83% and 94% accuracy on small, medium and large sized IMDB Urdu movie review data set and Urdu Customer Support data set respectively.

Keywords: urdu sentiment analysis, deep learning, natural language processing, opinion mining, low-resource language

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Authors: Lin Cheng, Zijiang Yang

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Program synthesis is the task to automatically generate programs based on user specification. In this paper, we present a framework that synthesizes programs from flow charts that serve as accurate and intuitive specification. In order doing so, we propose a deep neural network called GRCNN that recognizes graph structure from its image. GRCNN is trained end-to-end, which can predict edge and node information of the flow chart simultaneously. Experiments show that the accuracy rate to synthesize a program is 66.4%, and the accuracy rates to recognize edge and node are 94.1% and 67.9%, respectively. On average, it takes about 60 milliseconds to synthesize a program.

Keywords: program synthesis, flow chart, specification, graph recognition, CNN

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Authors: Maciej Szeląg, Stanisław Fic

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The impact of elevated temperatures on the construction materials manifests in change of their physical and mechanical characteristics. Stresses and thermal deformations that occur inside the volume of the material cause its progressive degradation as temperature increase. Finally, the reactions and transformations of multiphase structure of cementitious composite cause its complete destruction. A particularly dangerous phenomenon is the impact of thermal shock – a sudden high temperature load. The thermal shock leads to a high value of the temperature gradient between the outer surface and the interior of the element in a relatively short time. The result of mentioned above process is the formation of the cracks and scratches on the material’s surface and inside the material. The article describes the use of computer image analysis techniques to identify and assess the structure of the cluster cracks on the surfaces of modified cement pastes, caused by thermal shock. Four series of specimens were tested. Two Portland cements were used (CEM I 42.5R and CEM I 52,5R). In addition, two of the series contained metakaolinite as a replacement for 10% of the cement content. Samples in each series were made in combination of three w/b (water/binder) indicators of respectively 0.4; 0.5; 0.6. Surface cracks of the samples were created by a sudden temperature load at 200°C for 4 hours. Images of the cracked surfaces were obtained via scanning at 1200 DPI; digital processing and measurements were performed using ImageJ v. 1.46r software. In order to examine the cracked surface of the cement paste as a system of closed clusters – the dispersal systems theory was used to describe the structure of cement paste. Water is used as the dispersing phase, and the binder is used as the dispersed phase – which is the initial stage of cement paste structure creation. A cluster itself is considered to be the area on the specimen surface that is limited by cracks (created by sudden temperature loading) or by the edge of the sample. To describe the structure of cracks two stereological parameters were proposed: A ̅ – the cluster average area, L ̅ – the cluster average perimeter. The goal of this study was to compare the investigated stereological parameters with the mechanical properties of the tested specimens. Compressive and tensile strength testes were carried out according to EN standards. The method used in the study allowed the quantitative determination of defects occurring in the examined modified cement pastes surfaces. Based on the results, it was found that the nature of the cracks depends mainly on the physical parameters of the cement and the intermolecular interactions on the dispersal environment. Additionally, it was noted that the A ̅/L ̅ relation of created clusters can be described as one function for all tested samples. This fact testifies about the constant geometry of the thermal cracks regardless of the presence of metakaolinite, the type of cement and the w/b ratio.

Keywords: cement paste, cluster cracks, elevated temperature, image analysis, metakaolinite, stereological parameters

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Authors: Moh Hanif Mubarok, Muhammad Rivai

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The public's response to quality rice is very high. So it is necessary to set minimum standards in checking the quality of rice. Most rice quality measurements still use manual methods, which are prone to errors due to limited human vision and the subjectivity of testers. So, a gas detection system can be a solution that has high effectiveness and subjectivity for solving current problems. The use of gas sensors in testing rice quality must pay attention to several parameters. The parameters measured in this research are the percentage of rice water content, gas concentration, output voltage, and measurement time. Therefore, this research was carried out to identify carbon dioxide (CO₂), nitrous oxide (N₂O) and methane (CH₄) gases in rice quality using a series of gas sensors using the Neural Network method.

Keywords: carbon dioxide, dinitrogen oxide, methane, semiconductor gas sensor, neural network

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Authors: Lalit Ahuja, Nancy Das, Yashas Shetty

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LED lighting has been increasingly adopted for vehicles in both domestic and foreign automotive markets. Although this miniaturized technology gives the best light output, low energy consumption, and cost-efficient solutions for driving, the same is the need of the hour. In this paper, we present a methodology for driving the highest class two-wheeler headlamp with regulator and rectifier (RR) output. Unlike usual LED headlamps, which are driven by a battery, regulator, and rectifier (RR) driven, a low-cost and highly efficient LED Driver Module (LDM) is proposed. The positive half of magneto output is regulated and used to charge batteries used for various peripherals. While conventionally, the negative half was used for operating bulb-based exterior lamps. But with advancements in LED-based headlamps, which are driven by a battery, this negative half pulse remained unused in most of the vehicles. Our system uses negative half-wave rectified DC output from RR to provide constant light output at all RPMs of the vehicle. With the negative rectified DC output of RR, we have the advantage of pulsating DC input which periodically goes to zero, thus helping us to generate a constant DC output equivalent to the required LED load, and with a change in RPM, additional active thermal bypass circuit help us to maintain the efficiency and thermal rise. The methodology uses the negative half wave output of the RR along with a linear constant current driver with significantly higher efficiency. Although RR output has varied frequency and duty cycles at different engine RPMs, the driver is designed such that it provides constant current to LEDs with minimal ripple. In LED Headlamps, a DC-DC switching regulator is usually used, which is usually bulky. But with linear regulators, we’re eliminating bulky components and improving the form factor. Hence, this is both cost-efficient and compact. Presently, output ripple-free amplitude drivers with fewer components and less complexity are limited to lower-power LED Lamps. The focus of current high-efficiency research is often on high LED power applications. This paper presents a method of driving LED load at both High Beam and Low Beam using the negative half wave rectified pulsating DC from RR with minimum components, maintaining high efficiency within the thermal limitations. Linear regulators are significantly inefficient, with efficiencies typically about 40% and reaching as low as 14%. This leads to poor thermal performance. Although they don’t require complex and bulky circuitry, powering high-power devices is difficult to realise with the same. But with the input being negative half wave rectified pulsating DC, this efficiency can be improved as this helps us to generate constant DC output equivalent to LED load minimising the voltage drop on the linear regulator. Hence, losses are significantly reduced, and efficiency as high as 75% is achieved. With a change in RPM, DC voltage increases, which can be managed by active thermal bypass circuitry, thus resulting in better thermal performance. Hence, the use of bulky and expensive heat sinks can be avoided. Hence, the methodology to utilize the unused negative pulsating DC output of RR to optimize the utilization of RR output power and provide a cost-efficient solution as compared to costly DC-DC drivers.

Keywords: class D LED headlamp, regulator and rectifier, pulsating DC, low cost and highly efficient, LED driver module

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Authors: Yoshimoto Kurihara, Tad Gonsalves

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Currently, in the field of object posture estimation, there is research on estimating the position and angle of an object by storing a 3D model of the object to be estimated in advance in a computer and matching it with the model. However, in this research, we have succeeded in creating a module that is much simpler, smaller in scale, and faster in operation. Our 6D pose estimation model consists of two different networks – a classification network and a regression network. From a single RGB image, the trained model estimates the class of the object in the image, the coordinates of the object, and its rotation angle in 3D space. In addition, we compared the estimation accuracy of each camera position, i.e., the angle from which the object was captured. The highest accuracy was recorded when the camera position was 75°, the accuracy of the classification was about 87.3%, and that of regression was about 98.9%.

Keywords: 6D posture estimation, image recognition, deep learning, AlexNet

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Authors: Noxolo Mafu

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This study presents an overview of strategic planning in South African higher education institutions by tracing its trends and mystique in order to identify its impact. Over the democratic decades, strategic planning has become integral to institutional survival. It has been used as a potent tool by several institutions to catch up and surpass counterparts. While planning has always been part of higher education, strategic planning should be considered different. Strategic planning is primarily about development and maintenance of a strategic fitting between an institution and its dynamic opportunities. This presupposes existence of sets of stages that institutions pursue of which, can be regarded for assessment of the impact of strategic planning in an institution. The network theory serves guides the study in demystifying apparent organisational networks in strategic planning processes.

Keywords: network theory, strategy, planning, strategic planning, assessment, impact

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Authors: Lee Jeong Min, Lee Mi Hee, Eo Yang Dam

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Geospatial feature extraction is a very important issue in the remote sensing research. In the meantime, the image classification based on statistical techniques, but, in recent years, data mining and machine learning techniques for automated image processing technology is being applied to remote sensing it has focused on improved results generated possibility. In this study, artificial neural network and decision tree technique is applied to classify the high-resolution satellite images, as compared to the MLC processing result is a statistical technique and an analysis of the pros and cons between each of the techniques.

Keywords: remote sensing, artificial neural network, decision tree, maximum likelihood classification

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Authors: Ravi Prasad Darapureddi

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The use of industrial waste in making concrete reduce the consumption of natural resources and pollution of the environment. These materials possess problems of disposal and health hazards. An attempt has been made to use paper and thermal industrial wastes such as lime sludge and flyash. Present investigation is aimed at the utilization of Lime Sludge and Flyash as Supplementary Cementitious Materials (SCM) and influence of these materials on strength properties of concrete. Thermal industry waste fly ash is mixed with lime sludge and used as a replacement to cement at different proportions to obtain the strength properties and compared with ordinary concrete prepared without any additives. Grade of concrete prepared was M₂₅ designed according to Indian standard method. Cement has been replaced by paper industry waste and fly ash in different proportions such as 0% (normal concrete), 10%, 20%, and 30% by weight. Mechanical properties such as compressive strength, splitting tensile strength and flexural strength were assessed. Test results indicated that the use of lime sludge and Fly ash in concrete had improved the properties of concrete. Better results were observed at 20% replacement of cement with these additives.

Keywords: supplementary cementitious materials, lime sludge, fly ash, strength properties

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Authors: Ioanna Skoufali

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The present study is directed towards adaptation to climate change closely related to the phenomenon of the urban heat island (UHI). This issue is widespread and common to different urban realities, but particularly in Mediterranean cities that are characterized by dense urban. The attention of this work of redevelopment of the open space is focused on mitigation techniques aiming to solve local problems such as microclimatic parameters and the conditions of thermal comfort in summer, related to urban morphology. This quantitative analysis, evaluation, and verification survey involves the methodological elaboration applied in a real study case by Serres, through the experimental support of the ENVImet Pro V4.1 and BioMet software developed: i) in two phases concerning the anteoperam (phase a1 # 2013) and the post-operam (phase a2 # 2016); ii) in scenario A (+ 25% of green # 2017). The first study tends to identify the main intervention strategies, namely: the application of cool pavements, the increase of green surfaces, the creation of water surface and external fans; moreover, it obtains the minimum results achieved by the National Program 'Bioclimatic improvement project for public open space', EPPERAA (ESPA 2007-2013) related to the four environmental parameters illustrated below: the TAir = 1.5 o C, the TSurface = 6.5 o C, CDH = 30% and PET = 20%. In addition, the second study proposes a greater potential for improvement than postoperam intervention by increasing the vegetation within the district towards the SW/SE. The final objective of this in-depth design is to be transferable in homogeneous cases of urban regeneration processes with obvious effects on the efficiency of microclimatic mitigation and thermal comfort.

Keywords: cool pavements, microclimate parameters (TAir, Tsurface, Tmrt, CDH), mitigation strategies, outdoor thermal comfort (PET & UTCI)

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Authors: Ayse Ozbil, Gorsev Argin, Demet Yesiltepe

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Data for this cross-sectional study were drawn from questionnaires conducted in 10 elementary schools (1000 students, ages 12-14) located in Istanbul, Turkey. School environments (1600 meter buffers around the school) were evaluated through GIS-based land-use data (parcel level land use density) and street-level topography. Street networks within the same buffers were evaluated by using angular segment analysis (Integration and Choice) implemented in Depthmap as well as two segment-based connectivity measures, namely Metric and Directional Reach implemented in GIS. Segment Angular Integration measures how accessible each space from all the others within the radius using the least angle measure of distance. Segment Angular Choice which measures how many times a space is selected on journeys between all pairs of origins and destinations. Metric Reach captures the density of streets and street connections accessible from each individual road segment. Directional Reach measures the extent to which the entire street network is accessible with few direction changes. In addition, socio-economic characteristics (annual income, car ownership, education-level) of parents, obtained from parental questionnaires, were also included in the analysis. It is shown that surrounding street network configuration is strongly associated with both walk-mode shares and average walking distances to/from schools when controlling for parental socio-demographic attributes as well as land-use compositions and topographic features in school environments. More specifically, findings suggest that the scale at which urban form has an impact on pedestrian travel is considerably larger than a few blocks around the school.

Keywords: Istanbul, street network layout, urban form, walking to/from school

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Authors: Ali Reza Tahavvor, Saeed Hosseini, Nazli Jowkar, Afshin Karimzadeh Fard

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Experimental & numeral study of temperature distribution during milling process, is important in milling quality and tools life aspects. In the present study the milling cross-section temperature is determined by using Artificial Neural Networks (ANN) according to the temperature of certain points of the work piece and the points specifications and the milling rotational speed of the blade. In the present work, at first three-dimensional model of the work piece is provided and then by using the Computational Heat Transfer (CHT) simulations, temperature in different nods of the work piece are specified in steady-state conditions. Results obtained from CHT are used for training and testing the ANN approach. Using reverse engineering and setting the desired x, y, z and the milling rotational speed of the blade as input data to the network, the milling surface temperature determined by neural network is presented as output data. The desired points temperature for different milling blade rotational speed are obtained experimentally and by extrapolation method for the milling surface temperature is obtained and a comparison is performed among the soft programming ANN, CHT results and experimental data and it is observed that ANN soft programming code can be used more efficiently to determine the temperature in a milling process.

Keywords: artificial neural networks, milling process, rotational speed, temperature

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Authors: Kaewpradap Amornrat, Endo Takahiro, Kadowaki Satoshi

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The combustion of hydrogen-oxygen (H2-O2) mixtures was investigated to consider the reduction of carbon dioxide (CO2) and nitrogen oxide (NOx) as the greenhouse emission. Normally, the flame speed of combustion H2-O2 mixtures are very fast thus it is necessary to control the limit of mixtures with CO2 addition as H2-O2-CO2 combustion. The limit of hydrogen was set and replaced by CO2 with O2:CO2 ratio as 1:3.76, 1:4 and 1:5 for this study. In this study, the combustion of H2-O2 -CO2 on flat burner at equivalence ratio =0.5 was investigated for 10, 15 and 20 L/min of flow rate mixtures. When the ratio of CO2 increases, the power spectral density is lower, the size of attractor and cellular flame become larger because the decrease of hydrogen replaced by CO2 affects the diffusive-thermal instability. Moreover, the flow rate mixtures increases, the power spectral density increases, the size of reconstructed attractor and cell size become smaller due to decreasing of instability. The results show that the variation of CO2 and mixture flow rate affects the instability of cellular premixed flames on flat burner.

Keywords: instability, H2-O2-CO2 combustion, flat burner, diffusive-thermal instability

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Authors: Chenxiao Yang, Li Li

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In this decade, the sportswear market rapidly expanded while numerous sports brands are conducting fierce competitions to hold their market shares and trying to act as a leader in professional competition sports areas to set the trends. Thus, various advancing sports equipment is being deeply explored to improving athletes’ performance in fierce competitions. Although there is plenty protective equipment such as cuff, running legging, etc., on the market, there is still blank in the field of sportswear during prerace warm-up this important time gap, especially for those competitions host in cold environment. Because there is always time gaps between warm-up and race due to event logistics or unexpected weather factors. Athletes will be exposed to chilly condition for an unpredictable long period of time. As a consequence, the effects of warm-up will be negated, and the competition performance will be degraded. However, reviewing the current market, there is none effective sports equipment provided to help athletes against this harsh environment or the rare existing products are so blocky or heavy to restrict the actions. An ideal thermal-protective sportswear should be light, flexible, comfort and aesthetic at the same time. Therefore, this design research adopted the textile circular knitting methodology to integrate soft silver-coated conductive yarns (ab. SCCYs), elastic nylon yarn and polyester yarn to develop the proposed electrical, thermal sportswear, with the strengths aforementioned. Meanwhile, the relationship between heating performance, stretch load, and energy consumption were investigated. Further, a simulation model was established to ensure providing sufficient warm and flexibility at lower energy cost and with an optimized production, parameter determined. The proposed circular knitting technology and simulation model can be directly applied to instruct prototype developments to cater different target consumers’ needs and ensure prototypes’’ safety. On the other hand, high R&D investment and time consumption can be saved. Further, two prototypes: a kneecap and an elbow guard, were developed to facilitate the transformation of research technology into an industrial application and to give a hint on the blur future blueprint.

Keywords: cold environment, silver-coated conductive yarn, electrical thermal textile, stretchable

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Authors: Stefan K. Behfar

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The rapid growth of the Ethereum network has brought forth the urgent need for scalable analysis methods to handle the increasing volume of blockchain data. In this research, we propose efficient methodologies for making Ethereum network analysis scalable. Our approach leverages a combination of graph-based data representation, probabilistic sampling, and parallel processing techniques to achieve unprecedented scalability while preserving critical network insights. Data Representation: We develop a graph-based data representation that captures the underlying structure of the Ethereum network. Each block transaction is represented as a node in the graph, while the edges signify temporal relationships. This representation ensures efficient querying and traversal of the blockchain data. Probabilistic Sampling: To cope with the vastness of the Ethereum blockchain, we introduce a probabilistic sampling technique. This method strategically selects a representative subset of transactions and blocks, allowing for concise yet statistically significant analysis. The sampling approach maintains the integrity of the network properties while significantly reducing the computational burden. Graph Convolutional Networks (GCNs): We incorporate GCNs to process the graph-based data representation efficiently. The GCN architecture enables the extraction of complex spatial and temporal patterns from the sampled data. This combination of graph representation and GCNs facilitates parallel processing and scalable analysis. Distributed Computing: To further enhance scalability, we adopt distributed computing frameworks such as Apache Hadoop and Apache Spark. By distributing computation across multiple nodes, we achieve a significant reduction in processing time and enhanced memory utilization. Our methodology harnesses the power of parallelism, making it well-suited for large-scale Ethereum network analysis. Evaluation and Results: We extensively evaluate our methodology on real-world Ethereum datasets covering diverse time periods and transaction volumes. The results demonstrate its superior scalability, outperforming traditional analysis methods. Our approach successfully handles the ever-growing Ethereum data, empowering researchers and developers with actionable insights from the blockchain. Case Studies: We apply our methodology to real-world Ethereum use cases, including detecting transaction patterns, analyzing smart contract interactions, and predicting network congestion. The results showcase the accuracy and efficiency of our approach, emphasizing its practical applicability in real-world scenarios. Security and Robustness: To ensure the reliability of our methodology, we conduct thorough security and robustness evaluations. Our approach demonstrates high resilience against adversarial attacks and perturbations, reaffirming its suitability for security-critical blockchain applications. Conclusion: By integrating graph-based data representation, GCNs, probabilistic sampling, and distributed computing, we achieve network scalability without compromising analytical precision. This approach addresses the pressing challenges posed by the expanding Ethereum network, opening new avenues for research and enabling real-time insights into decentralized ecosystems. Our work contributes to the development of scalable blockchain analytics, laying the foundation for sustainable growth and advancement in the domain of blockchain research and application.

Keywords: Ethereum, scalable network, GCN, probabilistic sampling, distributed computing

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Authors: Fulya Ozbey, Ecehan Ozmehmet

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Health and living in a healthy environment are important for all the living creatures. Healthy buildings are the part of the healthy environment and the ones that people and sometimes the animals spend most of their times in it. Therefore, healthy buildings are important subject for everybody. There are many elements of the healthy buildings from material choice to the thermal comfort including indoor air quality. The aim of this study is, to simulate two natural ventilation strategies which are used as a cooling method in Mediterranean climate, by applying to a residential building and compare the results for Asian climate. Fulltime natural and night-time ventilation strategies are simulated for three days during the summertime in Mediterranean climate. The results show that one of the chosen passive cooling strategies worked on both climates good enough without using additional shading element and cooling device, however, the other ventilation strategy did not provide comfortable indoor temperature enough. Finally, both of the ventilation strategies worked better on the Asian climate than the Mediterranean in terms of the total overheating hours during the chosen period of year.

Keywords: Asian climate, indoor air quality, Mediterranean climate, natural ventilation simulation, thermal comfort

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Authors: A. Graa, I. Ziane, F. Benhamida, S. Souag

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This paper presents a comparative analysis study of an efficient and reliable quadratic programming (QP) to solve economic load dispatch (ELD) problem with considering transmission losses in a power system. The proposed QP method takes care of different unit and system constraints to find optimal solution. To validate the effectiveness of the proposed QP solution, simulations have been performed using Algerian test system. Results obtained with the QP method have been compared with other existing relevant approaches available in literatures. Experimental results show a proficiency of the QP method over other existing techniques in terms of robustness and its optimal search.

Keywords: economic dispatch, quadratic programming, Algerian network, dynamic load

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Authors: Nonlapun Meenil, Pisitpong Intarapong, Thitima Wongsheree, Pranchalee Samanpiboon

Abstract:

In Thailand, once pineapples are harvested, they must be classified into two classes based on their sweetness: sweet and unsweet. This paper has studied and developed the assessment of internal quality of pineapples using a low-cost compact spectroscopy sensor according to the Spectroscopy approach and Neural Network (NN). During the experiments, Batavia pineapples were utilized, generating 100 samples. The extracted pineapple juice of each sample was used to determine the Soluble Solid Content (SSC) labeling into sweet and unsweet classes. In terms of experimental equipment, the sensor cover was specifically designed to install the sensor and light source to read the reflectance at a five mm depth from pineapple flesh. By using a spectroscopy sensor, data on visible and near-infrared reflectance (Vis-NIR) were collected. The NN was used to classify the pineapple classes. Before the classification step, the preprocessing methods, which are Class balancing, Data shuffling, and Standardization were applied. The 510 nm and 900 nm reflectance values of the middle parts of pineapples were used as features of the NN. With the Sequential model and Relu activation function, 100% accuracy of the training set and 76.67% accuracy of the test set were achieved. According to the abovementioned information, using a low-cost compact spectroscopy sensor has achieved favorable results in classifying the sweetness of the two classes of pineapples.

Keywords: neural network, pineapple, soluble solid content, spectroscopy

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Authors: Lauren A. Bailey, Amber R. Childs, Nicola C. James, Murray I. Duncan, Alexander Winkler, Warren M. Potts

Abstract:

For the persistence of fished populations in the Anthropocene, it is critical to predict how fished populations will respond to the coupled threats of exploitation and climate change for adaptive management. The resilience of fished populations will depend on their capacity for physiological plasticity and acclimatization in response to environmental shifts. However, there is evidence for the selection of physiological traits by capture fisheries. Hence, fish populations may have a limited scope for the rapid expansion of their tolerance ranges or physiological adaptation under fishing pressures. To determine the physiological vulnerability of fished populations in the Anthropocene, the metabolic performance was compared between a fished and spatially protected Chrysoblephus laticeps population in response to thermal variability. Individual aerobic scope phenotypes were quantified using intermittent flow respirometry by comparing changes in energy expenditure of each individual at ecologically relevant temperatures, mimicking variability experienced as a result of upwelling and downwelling events. The proportion of high and low-performance individuals were compared between the fished and spatially protected population. The fished population had limited aerobic scope phenotype diversity and fewer high-performance phenotypes, resulting in a significantly lower aerobic scope curve across low (10 °C) and high (24 °C) thermal treatments. The performance of fished populations may be compromised with predicted future increases in cold upwelling events. This requires the conservation of the physiologically fittest individuals in spatially protected areas, which can recruit into nearby fished areas, as a climate resilience tool.

Keywords: climate change, fish physiology, metabolic shifts, over-fishing, respirometry

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Authors: Ahmad Mansoor Stanikzai, Yoshitaka Kajita

Abstract:

This research is a very essential towards transportation planning of Kabul New City. In this research, the travel demand of Kabul metropolitan area (Existing and Kabul New City) are evaluated for three different target years (2015, current, 2025, mid-term, 2040, long-term). The outcome of this study indicates that, though currently the vehicle volume is less the capacity of existing road networks, Kabul city is suffering from daily traffic congestions. This is mainly due to lack of transportation management, the absence of proper policies, improper public transportation system and violation of traffic rules and regulations by inhabitants. On the other hand, the observed result indicates that the current vehicle to capacity ratio (VCR) which is the most used index to judge traffic status in the city is around 0.79. This indicates the inappropriate traffic condition of the city. Moreover, by the growth of population in mid-term (2025) and long-term (2040) and in the case of no development in the road network and transportation system, the VCR value will dramatically increase to 1.40 (2025) and 2.5 (2040). This can be a critical situation for an urban area from an urban transportation perspective. Thus, by introducing high-capacity public transportation system and the development of road network in Kabul New City and integrating these links with the existing city road network, significant improvements were observed in the value of VCR.

Keywords: Afghanistan, Kabul new city, planning, policy, urban transportation

Procedia PDF Downloads 331

Authors: Zixian Zhang, Shanliang Yao, Zile Huang, Zhaodong Wu, Xiaohui Zhu, Yong Yue, Jieming Ma

Abstract:

Unmanned Surface Vehicles (USVs) are valuable due to their ability to perform dangerous and time-consuming tasks on the water. Object detection tasks are significant in these applications. However, inherent challenges, such as the complex distribution of obstacles, reflections from shore structures, water surface fog, etc., hinder the performance of object detection of USVs. To address these problems, this paper provides a fusion method for USVs to effectively detect objects in the inland surface environment, utilizing vision sensors and 3+1D Millimeter-wave radar. MMW radar is complementary to vision sensors, providing robust environmental information. The radar 3D point cloud is transferred to 2D radar pseudo image to unify radar and vision information format by utilizing the point transformer. We propose a multi-source object detection network (RV-YOLOX )based on radar-vision fusion for inland waterways environment. The performance is evaluated on our self-recording waterways dataset. Compared with the YOLOX network, our fusion network significantly improves detection accuracy, especially for objects with bad light conditions.

Keywords: inland waterways, YOLO, sensor fusion, self-attention

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Authors: A. Sharma, S. S. Sandhu, A. Shahi, A. Kumar

Abstract:

The aim of present investigation was to carry out Finite element modeling of distortion in the case of butt weld. 12mm thick AISI 304L plates were butt welded using three different combinations of groove design namely Double U, Double V and Composite. A full simulation of shielded metal arc welding (SMAW) of nonlinear heat transfer is carried out. Aspects like, temperature-dependent thermal properties of AISI stainless steel above liquid phase, the effect of thermal boundary conditions, were included in the model. Since welding heat dissipation characteristics changed due to variable groove design significant changes in the microhardness tensile strength and impact toughness of the joints were observed. The cumulative distortion was found to be least in double V joint followed by the Composite and Double U-joints. All the joints have joint efficiency more than 100%. CVN value of the Double V-groove weld metal was highest. The experimental results and the FEM results were compared and reveal a very good correlation for distortion and weld groove design for a multipass joint with a standard analogy of 83%.

Keywords: AISI 304 L, Butt joint, distortion, FEM, groove design, SMAW

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Authors: H. R. Goshayeshi, M. Khalouei, S. Azarberamman

Abstract:

This paper presents an experimental investigation regarding the use of Fe2O3 nano particles added to kerosene as a working fluid, under magnetic field. The experiment was made on Oscillating Heat Pipe (OHP). The experiment was performed in order to measure the temperature distribution and compare the heat transfer rate of the oscillating heat pipe with and without magnetic Field. Results showed that the addition of Fe2o3 nano particles under magnetic field improved thermal performance of OHP, compare with non-magnetic field. Furthermore applying a magnetic field enhance the heat transfer characteristic of Fe2O3 in both start up and steady state conditions. This paper presents an experimental investigation regarding the use of Fe2O3 nano particles added to kerosene as a working fluid, under magnetic field. The experiment was made on Oscillating Heat Pipe (OHP). The experiment was performed in order to measure the temperature distribution and compare the heat transfer rate of the oscillating heat pipe with and without magnetic Field. Results showed that the addition of Fe2o3 nano particles under magnetic field improved thermal performance of OHP, compare with non-magnetic field. Furthermore applying a magnetic field enhance the heat transfer characteristic of Fe2O3 in both start up and steady state conditions.

Keywords: experimental, oscillating heat pipe, heat transfer, magnetic field

Procedia PDF Downloads 264