Search results for: network transformation

Authors: Fariba Ebrahimi, Mehdi Ghorbani

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Achieving to integrated Water management fundamentally needs to effective relation, coordination, collaboration and synergy among various actors who have common but different responsibilities. In this sense, the foundation of comprehensive and integrated management is not compatible with centralization and top-down strategies. The aim of this paper is analysis institutional network of water relevant stakeholders and water policy monitoring in Shemiranat. In this study collaboration networks between informal and formal institutions co-management process have been investigated. Stakeholder network analysis as a quantitative method has been implicated in this research. The results of this study indicate that institutional cohesion is medium; sustainability of institutional network is about 40 percent (medium). Additionally the core-periphery index has measured in this study according to reciprocity index. Institutional capacities for integrated natural resource management in regional level are measured in this study. Furthermore, the necessity of centrality reduction and promote stakeholders relations and cohesion are emphasized to establish a collaborative natural resource governance.

Keywords: policy monitoring, water management, social network, stakeholder, shemiranat

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Authors: Lee Soo-Kyoung, Kim Young-Su

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This study established a network related to metabolic syndrome intervention by conducting a social network analysis of titles, keywords, and abstracts, and it identified emerging topics of research. It visualized an interconnection between critical keywords and investigated their frequency of appearance to construe the trends in metabolic syndrome intervention measures used in studies conducted over 38 years (1979–2017). It examined a collection of keywords from 8,285 studies using text rank analyzer, NetMiner 4.0. The analysis revealed 5 groups of newly emerging keywords in the research. By examining the relationship between keywords with reference to their betweenness centrality, the following clusters were identified. Thus if new researchers refer to existing trends to establish the subject of their study and the direction of the development of future research on metabolic syndrome intervention can be predicted.

Keywords: intervention, metabolic syndrome, network analysis, research, the trend

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Authors: Sanat Ramesh, Tarun Dutt, Abhilasha Aswal, Anushka Chandrababu, G. N. Srinivasa Prasanna

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Optimal Schedule Generation for a large national railway network operating thousands of passenger trains with tens of thousands of kilometers of track is a grand computational challenge in itself. We present heuristics based on a Mixed Integer Program (MIP) formulation for local optimization. These methods provide flexibility in scheduling new trains with varying speed and delays and improve utilization of infrastructure. We propose methods that provide a robust solution with hundreds of trains being scheduled over a portion of the railway network without significant increases in delay. We also provide techniques to validate the nominal schedules thus generated over global correlated variations in travel times thereby enabling us to detect conflicts arising due to delays. Our validation results which assume only the support of the arrival and departure time distributions takes an order of few minutes for a portion of the network and is computationally efficient to handle the entire network.

Keywords: mixed integer programming, optimization, railway network, train scheduling

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Authors: A. S. Hakeem, B. Ahmed, M. Ehsan, A. Ibrahim, H. M. Irshad, T. Laoui

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Alumina (Al2O3) - cubic boron nitride (cBN) ceramic composites were sintered by spark plasma sintering (SPS) using α-Al2O3 particle sizes; 150 µm, 150 nm and cBN particle size of 42 µm. Alumina-cBN composites containing 10, 20 and 30wt% cBN with and without Ni coated were sintering at an elevated temperature of 1400°C at a constant uniaxial pressure of 50 MPa. The effect of matrix particle size, cBN and Ni content on mechanical properties and thermal properties, i.e., thermal conductivity, diffusivity, expansion, densification, phase transformation, microstructure, hardness and toughness of the Al2O3-cBN/(Ni) composites under specific sintering conditions were investigated. The highest relative densification of 150 nm-Al2O3 containing 30wt% cBN (Ni coated) composite was 99% at TSPS = 1400°C. In case of 150 µm- Al2O3 compositions, the phase transformation of cBN to hBN were observed, and the relative densification decreased. Thermal conductivity depicts maximum value in case of 150 nm- Al2O3-30wt% cBN-Ni composition. The Vickers hardness of this composition at TSPS = 1400°C also showed the highest value of 29 GPa.

Keywords: alumina composite, cubic boron nitride, mechanical properties, phase transformation, Spark plasma sintering

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Authors: Chico Horacio Jose Sambo

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Recently neural network has gained popularity when come to solve complex nonlinear problems. Permeability is one of fundamental reservoir characteristics system that are anisotropic distributed and non-linear manner. For this reason, permeability prediction from well log data is well suited by using neural networks and other computer-based techniques. The main goal of this paper is to predict reservoir permeability from well logs data by using neural network approach. A multi-layered perceptron trained by back propagation algorithm was used to build the predictive model. The performance of the model on net results was measured by correlation coefficient. The correlation coefficient from testing, training, validation and all data sets was evaluated. The results show that neural network was capable of reproducing permeability with accuracy in all cases, so that the calculated correlation coefficients for training, testing and validation permeability were 0.96273, 0.89991 and 0.87858, respectively. The generalization of the results to other field can be made after examining new data, and a regional study might be possible to study reservoir properties with cheap and very fast constructed models.

Keywords: neural network, permeability, multilayer perceptron, well log

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Authors: Nopsarun Vannasirikul

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The purposes of this research paper were to study the current situations of community development, women’s potentials, women’s participation, network, and government policy as well as to study the factors influencing women’s potentials, women’s participation, network, and government policy that have on the community development. The population included the women age of 18 years old who were living in the communities of Bangkok areas. This study was a mix research method of quantitative and qualitative method. A simple random sampling method was utilized to obtain 400 sample groups from 50 districts of Bangkok and to perform data collection by using questionnaire. Also, a purposive sampling method was utilized to obtain 12 informants for an in-depth interview to gain an in-sight information for quantitative method.

Keywords: community development, participation, network, women’s right, management

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Authors: Hanbhin Ryu, Ilkwon Yun

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Automotive industry targets to provide an improvement in safety and convenience through realizing fully autonomous vehicle. For partially realizing fully automated driving, Current vehicles already feature varieties of advanced driver assistance system (ADAS) for safety and infotainment systems for the driver’s convenience. This paper presents Cloud Connected Vehicle (CCV) which connected vehicles with cloud data center via the access network to control the vehicle for achieving next autonomous driving form and describes its features. This paper also describes the shortcoming of the existing In-Vehicle Network (IVN) to be a next generation IVN of CCV and organize the 802.3 Ethernet, the next generation of IVN, related research issue to verify the feasibility of using Ethernet. At last, this paper refers to additional considerations to adopting Ethernet-based IVN for CCV.

Keywords: autonomous vehicle, cloud connected vehicle, ethernet, in-vehicle network

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Authors: Azadeh Bashiri

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Introduction: Lack of proper diagnosis and inadequate treatment of asthma leads to physical and financial complications. This study aimed to use data mining techniques and creating a neural network intelligent system for diagnosis of asthma. Methods: The study population is the patients who had visited one of the Lung Clinics in Tehran. Data were analyzed using the SPSS statistical tool and the chi-square Pearson's coefficient was the basis of decision making for data ranking. The considered neural network is trained using back propagation learning technique. Results: According to the analysis performed by means of SPSS to select the top factors, 13 effective factors were selected, in different performances, data was mixed in various forms, so the different models were made for training the data and testing networks and in all different modes, the network was able to predict correctly 100% of all cases. Conclusion: Using data mining methods before the design structure of system, aimed to reduce the data dimension and the optimum choice of the data, will lead to a more accurate system. Therefore, considering the data mining approaches due to the nature of medical data is necessary.

Keywords: asthma, data mining, Artificial Neural Network, intelligent system

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Authors: Amrah Maryam

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The emergence of online social networks (OSNs) has transformed the way we pursue and share information. On the one hand, OSNs provide great ease for the spreading of positive information while, on the other hand, they may also become a channel for the spreading of malicious rumors and misinformation throughout the social network. Thus, to assure the trustworthiness of OSNs to its users, it is of vital importance to detect the misinformation propagation in the network by placing network monitors. In this paper, we aim to place monitors near the suspected nodes with the intent to limit the diffusion of misinformation in the social network, and then we also detect the most significant nodes in the network for propagating true information in order to minimize the effect of already diffused misinformation. Thus, we initiate two heuristic monitor placement using articulation points and truth propagation using eigenvector centrality. Furthermore, to provide real-time workings of the system, we integrate both the monitor placement and truth propagation entities as well. To signify the effectiveness of the approaches, we have carried out the experiment and evaluation of Stanford datasets of online social networks.

Keywords: online social networks, monitor placement, independent cascade model, spread of misinformation

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Authors: Tapan Jain, Davender Singh Saini

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Clustering is a useful mechanism in wireless sensor networks which helps to cope with scalability and data transmission problems. The basic aim of our research work is to provide efficient clustering using Hierarchical agglomerative clustering (HAC). If the distance between the sensing nodes is calculated using their location then it’s quantitative HAC. This paper compares the various agglomerative clustering techniques applied in a wireless sensor network using the quantitative data. The simulations are done in MATLAB and the comparisons are made between the different protocols using dendrograms.

Keywords: routing, hierarchical clustering, agglomerative, quantitative, wireless sensor network

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Authors: H. Gorine, M. Ramadan Elmezughi

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In this paper, we investigate security issues and challenges facing researchers in wireless sensor networks and countermeasures to resolve them. The broadcast nature of wireless communication makes Wireless Sensor Networks prone to various attacks. Due to resources limitation constraint in terms of limited energy, computation power and memory, security in wireless sensor networks creates different challenges than wired network security. We will discuss several attempts at addressing the issues of security in wireless sensor networks in an attempt to encourage more research into this area.

Keywords: wireless sensor networks, network security, light weight encryption, threats

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Authors: Aisulu Tileukulova, Zhexebay Dauren

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Radar plays an important role because it is widely used in civil and military fields. Target detection is one of the most important radar applications. The accuracy of detecting inconspicuous aerial objects in radar facilities is lower against the background of noise. Convolutional neural networks can be used to improve the recognition of this type of aerial object. The purpose of this work is to develop an algorithm for recognizing aerial objects using convolutional neural networks, as well as training a neural network. In this paper, the structure of a convolutional neural network (CNN) consists of different types of layers: 8 convolutional layers and 3 layers of a fully connected perceptron. ReLU is used as an activation function in convolutional layers, while the last layer uses softmax. It is necessary to form a data set for training a neural network in order to detect a target. We built a Confusion Matrix of the CNN model to measure the effectiveness of our model. The results showed that the accuracy when testing the model was 95.7%. Classification of echo signals using CNN shows high accuracy and significantly speeds up the process of predicting the target.

Keywords: radar, neural network, convolutional neural network, echo signals

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Authors: Neslinur Hizli, Berrak Kirbaş Akyürek

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Beşiktaş, one of the historical coastal district of İstanbul, is on the very edge of the radical transformation because of an approaching ‘Beşiktaş Public Square Project’. At this juncture, due its location, presence on the coast, population density and distance to the other centers of the city, the decisions to be taken are critical to whole Istanbul that will be majorly affected from this transformation. As the new project aims to pedestrianize the area by placing the vehicular traffic under the ground, Beşiktaş and its square will change from top to bottom. Among those considerations, through the advantages and disadvantages the perception of the existing conditions of the Beşiktaş play significant role. The motive of this paper is the lack of determination and clarity on the cognition of the Square. After brief analysis on the historical transformation of the area, prominent studies on the criteria of public square are revised. Through cognitive mapping methodology, characteristics of the Square and the public space in general find a place to discuss from individual views. This study aims to discuss and review Beşiktaş Public Square from perspective, mind and behavior of the users. Cognitive map study with thirty subjects (30) is evaluated and categorized upon the five elements that Kevin Lynch defined as the images of the city. The results obtained digitized and represented with tables and graphs. Findings of the research underline the crucial issues on the approaching change in Beşiktaş. Thus, this study may help to develop comprehensive ideas and new suggestions on the Square.

Keywords: Beşiktaş public square, cognitive map, perception, public space

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Authors: Mostafa Sefidgar, Kaamran Raahemifar, Hossein Bazmara, Madjid Soltani

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The computational methods provide condition for investigation related to the process of drug delivery, such as convection and diffusion of drug in extracellular matrices, and drug extravasation from microvascular. The information of this process clarifies the mechanisms of drug delivery from the injection site to absorption by a solid tumor. In this study, an advanced numerical method is used to solve fluid flow and solute transport equations simultaneously to show how capillary network structure induced by tumor affects drug delivery. The effect of heterogeneous capillary network induced by tumor on interstitial fluid flow and drug delivery is investigated by this multi scale method. The sprouting angiogenesis model is used for generating capillary network induced by tumor. Fluid flow governing equations are implemented to calculate blood flow through the tumor-induced capillary network and fluid flow in normal and tumor tissues. The Starling’s law is used for closing this system of equations and coupling the intravascular and extravascular flows. Finally, convection-diffusion-reaction equation is used to simulate drug delivery. The dynamic approach which changes the capillary network structure based on signals sent by hemodynamic and metabolic stimuli is used in this study for more realistic assumption. The study indicates that drug delivery to solid tumors depends on the tumor induced capillary network structure. The dynamic approach generates the irregular capillary network around the tumor and predicts a higher interstitial pressure in the tumor region. This elevated interstitial pressure with irregular capillary network leads to a heterogeneous distribution of drug in the tumor region similar to in vivo observations. The investigation indicates that the drug transport properties have a significant role against the physiological barrier of drug delivery to a solid tumor.

Keywords: solid tumor, physiological barriers to drug delivery, angiogenesis, microvascular network, solute transport

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Authors: Cuma Şimşek

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Nowadays our globalized world is in a very rapid and sophisticated change. In the information age, notion of ‘information’ has begun to spread faster than ever also in this age, changes and transformation has gained tremendous momentum with technology boom. This continuous change and transformation, increased the competition between existing organizations and corporations. Besides, the organizations which show resistance to change has been put out of action in this competitive environment. It is not possible to sustain the existence of organizations without adapting to change and transformation by isolating itself from developments. As a consequence of improved communication and dialog possibilities by means of increasing knowledge level, there has been made a change of scene in administrative mentality, style and activation, especially in 21th century. Leaders emerge as the most important factor in this process of perception and success. At the same time it is not enough to adapt the alteration with conventional leadership abilities and behaviors. In parallel with alteration, new types of leadership are coming up. The optimal leadership type for our era and a trending topic "Transformational Leadership" is in great demand now. In this research, current situation of the Air Forces which use high-technology weapons efficiently, operates in an environment full of threats and is analyzed. It is evaluated that in order to be ready for war continuously and adjusting itself to changing terms of warfare atmosphere , Air Forces need ‘transformational leaders’ who are innovative, foreseeing and having a vision so that they can develop new methods and strategies for complex problems. Because it is the Air Force which is responsible for being the deterrent force of its country.

Keywords: transformational, change, air force, leadership

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Authors: Sayed Hadi Sadeghi

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This research study examines the current state of support services for e-network practice in an Australian and an American university. It identifies information that will be of assistance to Australian and American universities to improve their existing online programs. The study investigated the two universities using a quantitative methodological approach. Participants were students, lecturers and admins of universities engaged with online courses and learning management systems. The support services for e-network practice variables, namely academic support services, administrative support and technical support, were investigated for e-practice. Evaluations of e-network support service and its sub factors were above average and excellent in both countries, although the American admins and lecturers tended to evaluate this factor higher than others did. Support practice was evaluated higher by all participants of an American university than by Australians. One explanation for the results may be that most suppliers of the Australian university e-learning system were from eastern Asian cultural backgrounds with a western networking support perspective about e-learning.

Keywords: support services, e-Network practice, Australian universities, United States universities

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Authors: Bülent Kantar, Numan Ünaldı

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This paper presents a new method which includes robust and invisible digital watermarking on images that is colored. Colored images are used as watermark. Frequency region is used for digital watermarking. Discrete wavelet transform and discrete stationary wavelet transform are used for frequency region transformation. Low, medium and high frequency coefficients are obtained by applying the two-level discrete wavelet transform to the original image. Low frequency coefficients are obtained by applying one level discrete stationary wavelet transform separately to all frequency coefficient of the two-level discrete wavelet transformation of the original image. For every low frequency coefficient obtained from one level discrete stationary wavelet transformation, watermarks are added. Watermarks are added to all frequency coefficients of two-level discrete wavelet transform. Totally, four watermarks are added to original image. In order to get back the watermark, the original and watermarked images are applied with two-level discrete wavelet transform and one level discrete stationary wavelet transform. The watermark is obtained from difference of the discrete stationary wavelet transform of the low frequency coefficients. A total of four watermarks are obtained from all frequency of two-level discrete wavelet transform. Obtained watermark results are compared with real watermark results, and a similarity result is obtained. A watermark is obtained from the highest similarity values. Proposed methods of watermarking are tested against attacks of the geometric and image processing. The results show that proposed watermarking method is robust and invisible. All features of frequencies of two level discrete wavelet transform watermarking are combined to get back the watermark from the watermarked image. Watermarks have been added to the image by converting the binary image. These operations provide us with better results in getting back the watermark from watermarked image by attacking of the geometric and image processing.

Keywords: watermarking, DWT, DSWT, copy right protection, RGB

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Authors: Elif D. Oguz Erkal, David Krackhardt, Erica Cochran-Hameen

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Even though social network analysis (SNA) is an abundantly studied concept for many organizations and industries, a clear SNA approach to the project teams has not yet been adopted by the construction industry. The main challenges for performing SNA in construction and the apparent reason for this gap is the unique and complex structure of each construction project, the comparatively high circulation of project team members/contributing parties and the variety of authentic problems for each project. Additionally, there are stakeholders from a variety of professional backgrounds collaborating in a high-stress environment fueled by time and cost constraints. Within this case study on Project RE, a design & build project performed at the Urban Design Build Studio of Carnegie Mellon University, social network analysis of the project design team will be performed with the main goal of applying social network theory to construction project environments. The research objective is to determine a correlation between the network of how individuals relate to each other on one’s perception of their own professional strengths and weaknesses and the communication patterns within the team and the group dynamics. Data is collected through a survey performed over four rounds conducted monthly, detailed follow-up interviews and constant observations to assess the natural alteration in the network with the effect of time. The data collected is processed by the means of network analytics and in the light of the qualitative data collected with observations and individual interviews. This paper presents the full ethnography of this construction design team of fourteen architecture students based on an elaborate social network data analysis over time. This study is expected to be used as an initial step to perform a refined, targeted and large-scale social network data collection in construction projects in order to deduce the impacts of social networks on project performance and suggest better collaboration structures for construction project teams henceforth.

Keywords: construction design teams, construction project management, social network analysis, team collaboration, network analytics

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Authors: Yang Geng, Anqi Zhao

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The misreading theory of Harold Bloom provides a new diachronic perspective as an approach to the consistency between rhetoric of digital technology, dynamic movement of digital images and uncertain meaning of text. Reinterpreting the diachroneity of 'intertextuality' in the context of misreading theory extended the range of the 'intermediality' of transmedia to the intense tension between digital images and symbolic images throughout history of images. With the analogy between six categories of revisionary ratios and six steps of digital transformation, digital rhetoric might be illustrated as a linear process reflecting dynamic, intensive relations between digital moving images and original static images. Finally, it was concluded that two-way framework of the rhetoric of transformation of digital images and reversed served as a renarrative structure to revive static images by reconnecting them with digital moving images.

Keywords: rhetoric, digital art, intermediality, misreading theory

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Authors: Perttu Heino

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Universities of applied sciences (UASs) were introduced as part of the Finnish higher education system in the early 1990’s. Research, development and innovation (RDI) were mentioned in the legislation on UASs from the very beginning, but very little attention was paid to it in the early years of UASs due to reasons that are easy to understand. Required changes in the organization of the UAS and its educational offering were a big challenge, and it took several years to get things in order. There were RDI projects already then, but their number was low and there was no systematical coordination or management of those activities. In this paper, the institutional transformation of UASs is discussed based on how Tampere University of Applied Sciences has transformed during the years from a traditional tertiary level school to a modern higher education institution with a strong RDI activity, characterized by lively university-industry interaction and tight integration to education.

Keywords: research, development, management, practices

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Authors: Qin Ou, Yanghui Xu, Xintu Wang, Kim Maren Lompe, Gang Liu, Jan Peter Van Der Hoek

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Microplastics (MPs) can undergo the photooxidation process under ultraviolet (UV) exposure, which determines their transformation and fate in environments. The presence of dissolved organic matter (DOM) can interact with MPs and take participate in the photo-degradation of MPs. As an important DOM component, dissolved black carbon (DBC), widely distributed in aquatic environments, can accelerate or inhibit the sunlight-driven photo-transformation of environmental pollutants. However, the role and underlying mechanism of DBC in the photooxidation of MPs are not clear. Herein, the DBC (< 0.45 µm) was extracted from wood biochar and fractionated by molecular weight (i.e., <3 KDa, 3 KDa−30 KDa, 30 KDa−0.45 µm). The effects of DBC chemical composition (i.e., molecular weight and chemical structure) in DBC-mediated photo-transformation of polystyrene (PS) MPs were investigated. The results showed that DBC initially inhibited the photo-degradation of MPs due to light shielding. Under UV exposure for 6−24 h, the presence of 5 mg/L DBC decreased the carbonyl index of MPs compared to the control. This inhibitory effect of DBC was found to decrease with increasing irradiation time. Notably, DBC initially decreased but then increased the hydroxyl index with aging time, suggesting that the role of DBC may shift from inhibition to acceleration. In terms of the different DBC fractions, the results showed that the smallest fraction of DBC (<3 KDa) significantly accelerated the photooxidation of PS MPs since it acted as reactive oxygen species (ROS) generators, especially in promoting the production of ¹O₂ and ³DBC* and •OH. With the increase in molecular weight, the acceleration effect of DBC on the degradation of MPs was decreased due to the increase of light shielding and possible decrease of photosensitization ability. This study thoroughly investigated the critical role of DBC chemical composition in the photooxidation process, which helps to assess the duration of aging and transformation of MPs during long-term weathering in natural waters.

Keywords: microplastics, photo-degradation, dissolved black carbon, molecular weight, photosensitization

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Authors: S. M. Anowarul Haque, Md. Asiful Islam

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Electrical load forecasting is considered to be one of the most indispensable parts of a modern-day electrical power system. To ensure a reliable and efficient supply of electric energy, special emphasis should have been put on the predictive feature of electricity supply. Artificial Neural Network-based approaches have emerged to be a significant area of interest for electric load forecasting research. This paper proposed an Artificial Neural Network model based on the particle swarm optimization algorithm for improved electric load forecasting for Mymensingh, Bangladesh. The forecasting model is developed and simulated on the MATLAB environment with a large number of training datasets. The model is trained based on eight input parameters including historical load and weather data. The predicted load data are then compared with an available dataset for validation. The proposed neural network model is proved to be more reliable in terms of day-wise load forecasting for Mymensingh, Bangladesh.

Keywords: load forecasting, artificial neural network, particle swarm optimization

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Authors: Masoud Safarishaal

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Detecting the location of pole-to-earth faults is essential for the safe operation of the electrical system of the railroad. This paper aims to use a combination of evolutionary algorithms and neural networks to increase the accuracy of single pole-to-earth fault detection and location on the Tehran railroad power supply system. As a result, the Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO) are used to train the neural network to improve the accuracy and convergence of the learning process. Due to the system's nonlinearity, fault detection is an ideal application for the proposed method, where the 600 Hz harmonic ripple method is used in this paper for fault detection. The substations were simulated by considering various situations in feeding the circuit, the transformer, and typical Tehran metro parameters that have developed the silicon rectifier. Required data for the network learning process has been gathered from simulation results. The 600Hz component value will change with the change of the location of a single pole to the earth's fault. Therefore, 600Hz components are used as inputs of the neural network when fault location is the output of the network system. The simulation results show that the proposed methods can accurately predict the fault location.

Keywords: single pole-to-pole fault, Tehran railway, ICA, PSO, artificial neural network

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Authors: Taehan Lee

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We consider a network design problem which has shortest routing restriction based on the values determined by the installed facilities on each arc. In conventional multicommodity network design problem, a commodity can be routed through any possible path when the capacity is available. But, we consider a problem in which the commodity between two nodes must be routed on a path which has shortest metric value and the link metric value is determined by the installed facilities on the link. By this routing restriction, the problem has a distinct characteristic. We present an integer programming formulation containing the primal-dual optimality conditions to the shortest path routing. We give some computational results for the model.

Keywords: integer programming, multicommodity network design, routing, shortest path

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Authors: B. V. Feujofack Kemda, N. Barka, M. Jahazi, D. Osmani

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Automobile manufacturers are constantly seeking means to reduce the weight of car bodies. The usage of several steel grades in auto body assembling has been found to be a good technique to enlighten vehicles weight. This few years, the usage of dual phase (DP) steels, transformation induced plasticity (TRIP) steels and boron steels in some parts of the auto body have become a necessity because of their lightweight. However, these steels are martensitic, when they undergo a fast heat treatment, the resultant microstructure is essential, made of martensite. Resistance spot welding (RSW), one of the most used techniques in assembling auto bodies, becomes problematic in the case of these steels. RSW being indeed a process were steel is heated and cooled in a very short period of time, the resulting weld nugget is mostly fully martensitic, especially in the case of DP, TRIP and boron steels but that also holds for plain carbon steels as AISI 1010 grade which is extensively used in auto body inner parts. Martensite in its turn must be avoided as most as possible when welding steel because it is the principal source of brittleness and it weakens weld nugget. Thus, this work aims to find a mean to reduce martensite fraction in weld nugget when using RSW for assembling. The prediction of phase transformation kinetics during RSW has been done. That phase transformation kinetics prediction has been made possible through the modelling of the whole welding process, and a technique called post weld heat treatment (PWHT) have been applied in order to reduce martensite fraction in the weld nugget. Simulation has been performed for AISI 1010 grade, and results show that the application of PWHT leads to the formation of not only martensite but also ferrite, bainite and pearlite during the cooling of weld nugget. Welding experiments have been done in parallel and micrographic analyses show the presence of several phases in the weld nugget. Experimental weld geometry and phase proportions are in good agreement with simulation results, showing here the validity of the model.

Keywords: resistance spot welding, AISI 1010, modeling, post weld heat treatment, phase transformation, kinetics

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Authors: Mustafa Alhamdi

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Industrial application to classify gamma rays and neutron events is investigated in this study using deep machine learning. The identification using a convolutional neural network and recursive neural network showed a significant improvement in predication accuracy in a variety of applications. The ability to identify the isotope type and activity from spectral information depends on feature extraction methods, followed by classification. The features extracted from the spectrum profiles try to find patterns and relationships to present the actual spectrum energy in low dimensional space. Increasing the level of separation between classes in feature space improves the possibility to enhance classification accuracy. The nonlinear nature to extract features by neural network contains a variety of transformation and mathematical optimization, while principal component analysis depends on linear transformations to extract features and subsequently improve the classification accuracy. In this paper, the isotope spectrum information has been preprocessed by finding the frequencies components relative to time and using them as a training dataset. Fourier transform implementation to extract frequencies component has been optimized by a suitable windowing function. Training and validation samples of different isotope profiles interacted with CdTe crystal have been simulated using Geant4. The readout electronic noise has been simulated by optimizing the mean and variance of normal distribution. Ensemble learning by combing voting of many models managed to improve the classification accuracy of neural networks. The ability to discriminate gamma and neutron events in a single predication approach using deep machine learning has shown high accuracy using deep learning. The paper findings show the ability to improve the classification accuracy by applying the spectrogram preprocessing stage to the gamma and neutron spectrums of different isotopes. Tuning deep machine learning models by hyperparameter optimization of neural network models enhanced the separation in the latent space and provided the ability to extend the number of detected isotopes in the training database. Ensemble learning contributed significantly to improve the final prediction.

Keywords: machine learning, nuclear physics, Monte Carlo simulation, noise estimation, feature extraction, classification

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Authors: Muhammad Arif Khan, Haroon Idrees, Imran Aziz, Sidra Mushtaq

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Social platforms like online discussion and mailing groups are well aligned with academic as well as professional learning spaces. Professional communities are increasingly moving to online forums for sharing and capturing the intellectual abilities. This study investigated dynamics of social connectivity of yahoo mailing groups of Pakistani Library and Information Science (LIS) professionals using Graph Theory technique. Design/Methodology: Social Network Analysis is the increasingly concerned domain for scientists in identifying whether people grow together through online social interaction or, whether they just reflect connectivity. We have conducted a longitudinal study using Network Graph Theory technique to analyze the large data-set of email communication. The data was collected from three yahoo mailing groups using network analysis software over a period of six months i.e. January to June 2016. Findings of the network analysis were reviewed through focus group discussion with LIS experts and selected respondents of the study. Data were analyzed in Microsoft Excel and network diagrams were visualized using NodeXL and ORA-Net Scene package. Findings: Findings demonstrate that professionals and students exhibit intellectual growth the more they get tied within a network by interacting and participating in communication through online forums. The study reports on dynamics of the large network by visualizing the email correspondence among group members in a network consisting vertices (members) and edges (randomized correspondence). The model pair wise relationship between group members was illustrated to show characteristics, reasons, and strength of ties. Connectivity of nodes illustrated the frequency of communication among group members through examining node coupling, diffusion of networks, and node clustering has been demonstrated in-depth. Network analysis was found to be a useful technique in investigating the dynamics of the large network.

Keywords: emailing networks, network graph theory, online social platforms, yahoo mailing groups

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Authors: Tero Huhtala, Minna Pikkarainen, Saila Saraniemi

Abstract:

Information technology has long been used as an enabler of exchange for goods and services. Services are evolving from generic to personalized, and the reverse use of customer data has been discussed in both academia and industry for the past few years. This article presents the results of an empirical case study in the area of preventive health care services. The primary data were gathered in workshops, in which future personal data-based services were conceptualized by analyzing future scenarios from a business perspective. The aim of this study is to understand business model transformation in emerging personal data ecosystems. The work was done as a case study in the context of occupational healthcare. The results have implications to theory and practice, indicating that adopting personal data management principles requires transformation of the business model, which, if successfully managed, may provide access to more resources, potential to offer better value, and additional customer channels. These advantages correlate with the broadening of the business ecosystem. Expanding the scope of this study to include more actors would improve the validity of the research. The results draw from existing literature and are based on findings from a case study and the economic properties of the healthcare industry in Finland.

Keywords: ecosystem, business model, personal data, preventive healthcare

Procedia PDF Downloads 232

Authors: Lukas J. Evans, Danilo D. Fernando

Abstract:

Shrub willows (Salix spp.) have many characteristics which make them suitable for a variety of applications such as riparian zone buffers, environmental contaminant sequestration, living snow fences, and biofuel production. In some cases, these functions are limited due to physical or financial obstacles associated with the number of individuals needed to reasonably satisfy that purpose. One way to increase the efficiency of willows is to bioengineer them with the genetic improvements suitable for the desired use. To accomplish this goal, an optimized in vitro transformation protocol via Agrobacterium tumefaciens is necessary to reliably express genes of interest. Therefore, the aim of this study is to observe the influence of tissue culture with different willow cultivars, hormones, and explants on the percentage of calli expressing reporter gene green florescent protein (GFP) to find ideal transformation conditions. Each callus was produced from 1 month old open-pollinated seedlings of three Salix miyabeana cultivars (‘SX61’, ‘WT1’, and ‘WT2’) from three different explants (lamina, petiole, and internodes). Explants were cultured for 1 month on an MS media with different concentrations of 6-Benzylaminopurine (BAP) and 1-Naphthaleneacetic acid (NAA) (No hormones, 1 mg⁻¹L BAP only, 3 mg⁻¹L NAA only, 1 mg⁻¹L BAP and 3 mg⁻¹L NAA, and 3 mg⁻¹L BAP and 1 mg⁻¹L NAA) to produce a callus. Samples were then treated with Agrobacterium tumefaciens at an OD600 of 0.6-0.8 to insert the transgene GFP for 30 minutes, co-cultivated for 72 hours, and selected on the same media type they were cultured on with added 7.5 mg⁻¹L of Hygromycin for 1 week before GFP visualization under a UV dissecting scope. Percentage of GFP expressing calli as well as the average number of fluorescing GFP units per callus were recorded and results were evaluated through an ANOVA test (α = 0.05). The WT1 internode-derived calli on media with 3 mg-1L NAA+1 mg⁻¹L BAP and mg⁻¹L BAP alone produced a significantly higher percentage of GFP expressing calli than each other group (19.1% and 19.4%, respectively). Additionally, The WT1 internode group cultured with 3 mg⁻¹L NAA+1 mg⁻¹L BAP produced an average of 2.89 GFP units per callus while the group cultivated with 1 mg⁻¹L BAP produced an average of 0.84 GFP units per callus. In conclusion, genotype, explant choice, and hormones all play a significant role in increasing successful transformation in willows. Future studies to produce whole callus GFP expression and subsequent plantlet regeneration are necessary for a complete willow transformation protocol.

Keywords: agrobacterium, callus, Salix, tissue culture

Procedia PDF Downloads 108

Authors: Christian Kapuku, Seung-Young Kho

Abstract:

An accurate representation of the transportation system serving the region is one of the important aspects of transportation modeling. Such representation often requires developing an abstract model of the system elements, which also requires important amount of data, surveys and time. However, in some cases such as in developing countries, data deficiencies, time and budget constraints do not always allow such accurate representation, leaving opportunities to assumptions that may negatively affect the quality of the analysis. With the emergence of Internet open source data especially in the mapping technologies as well as the advances in Geography Information System, opportunities to tackle these issues have raised. Therefore, the objective of this paper is to demonstrate such application through a practical case of the development of the transportation network for the city of Kinshasa. The GIS geo-referencing was used to construct the digitized map of Transportation Analysis Zones using available scanned images. Centroids were then dynamically placed at the center of activities using an activities density map. Next, the road network with its characteristics was built using OpenStreet data and other official road inventory data by intersecting their layers and cleaning up unnecessary links such as residential streets. The accuracy of the final network was then checked, comparing it with satellite images from Google and Bing. For the validation, the final network was exported into Emme3 to check for potential network coding issues. Results show a high accuracy between the built network and satellite images, which can mostly be attributed to the use of open source data.

Keywords: geographic information system (GIS), network construction, transportation database, open source data

Procedia PDF Downloads 153