Search results for: oriented network

Authors: Himanshu Payal, Sachin Maheshwari, Pushpendra S. Bharti

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Electrical discharge machining (EDM), a non-conventional machining process, finds wide applications for shaping difficult-to-cut alloys. Process modeling of EDM is required to exploit the process to the fullest. Process modeling of EDM is a challenging task owing to involvement of so many electrical and non-electrical parameters. This work is an attempt to model the EDM process using artificial neural network (ANN). Experiments were carried out on die-sinking EDM taking Inconel 825 as work material. ANN modeling has been performed using experimental data. The prediction ability of trained network has been verified experimentally. Results indicate that ANN can predict the values of performance measures of EDM satisfactorily.

Keywords: artificial neural network, EDM, metal removal rate, modeling, surface roughness

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Authors: Ehsan Khademi, Mahdi Jalili

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Despite the special features of bipartite networks, they are common in many systems. Real-world bipartite networks may show community structure, similar to what one can find in one-mode networks. However, the interpretation of the community structure in bipartite networks is different as compared to one-mode networks. In this manuscript, we compare a number of available methods that are frequently used to discover community structure of bipartite networks. These networks are categorized into two broad classes. One class is the methods that, first, transfer the network into a one-mode network, and then apply community detection algorithms. The other class is the algorithms that have been developed specifically for bipartite networks. These algorithms are applied on a model network with prescribed community structure.

Keywords: community detection, bipartite networks, co-clustering, modularity, network projection, complex networks

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Authors: Francesco Buccafurri, Celeste Romolo

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Nowadays phishing is the most frequent starting point of cyber-attack vectors. Phishing is implemented both via email and social network messages. While a wide scientific literature exists which addresses the problem of contrasting email spam-phishing, no specific countermeasure has been so far proposed for phishing included into private messages of social network platforms. Unfortunately, the problem is severe. This paper proposes an approach against social network phishing, based on a non invasive collaborative information-sharing approach which leverages blockchain. The detection method works by filtering candidate messages, by distilling them by means of a distance-preserving hash function, and by publishing hashes over a public blockchain through a trusted smart contract (thus avoiding denial of service attacks). Phishing detection exploits social information embedded into social network profiles to identify similar messages belonging to disjoint contexts. The main contribution of the paper is to introduce a new approach to contrasting the problem of social network phishing, which, despite its severity, received little attention by both research and industry.

Keywords: phishing, social networks, information sharing, blockchain

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Authors: Jose L. Oliver, Taras Agryzkov, Leandro Tortosa, Jose F. Vicent, Javier Santacruz

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This paper aims to demonstrate how a topological study of an urban street network can be used as a tool to be applied to some heritage conservation areas in a city. In the last decades, we find different kinds of approaches in the discipline of Architecture and Urbanism based in the so-called Sciences of Complexity. In this context, this paper uses mathematics from the Network Theory. Hence, it proposes a methodology based in obtaining information from a graph, which is created from a network of urban streets. Then, it is used an algorithm that establishes a ranking of importance of the nodes of that network, from its topological point of view. The results are applied to a heritage area in a particular city, confronting the data obtained from the mathematical model, with the ones from the field work in the case study. As a result of this process, we may conclude the necessity of implementing some actions in the area, and where those actions would be more effective for the whole heritage site.

Keywords: graphs, heritage cities, spatial analysis, urban networks

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Authors: Beryl Rene R. Lopez, Lesley Anne M. Lipat, Rhogene Barbette C. Lirio, Laurice Joy H. Llanes, Karl Philippe M. Llapitan, Einstein James R. Lopez, Socorro S. GuanHing

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Background: Measles remain as one of the most common childhood diseases despite the availability of the vaccine that is safe and cost-effective. Because of morbidity and mortality associated with the recent measles outbreak in the Philippines, there is an increasing concern from the health care professionals. Objective: The purpose of this study is to determine the relationship between the compliance of Filipino mothers to measles vaccination and their health beliefs when grouped according to the given socio-demographic factors using a researcher-made questionnaire. Research Methodology: This research utilized the descriptive-correlational research design. With the use of purposive sampling technique, the study involved 200 Filipino mothers aged 18 years old and above excluding those who are healthcare professionals with children aged 2-3 years old with either urban or rural as their settlements. Pre-testing was done prior to the actual data gathering. A questionnaire composed of 26 items involving socio-demographic, compliance, and health beliefs was distributed to the sample population. Statistical analysis was done with the use of Exploratory Factor Analysis (EFA) for the first research question and Structural Equation Model (SEM) for the second research question. Results: Four dimensions were generated with the use of EFA namely: Vulnerability-Oriented Beliefs (VOB), Knowledge-Oriented Beliefs (KOB), Accessibility-Oriented Beliefs (AOB), and Outcomes-Oriented Beliefs (OOB). These were then correlated with the mothers’ socio-demographic factors (age, educational attainment, the area of residence, the number of children, and family income) and their compliance to the measles vaccination schedule. Results showed significant and direct relationships between area of residence and compliance, family income and compliance, KOB and compliance, education and KOB, KOB and VOB, KOB and OOB, AOB and KOB, AOB and OOB, AOB and VOB, and lastly, OOB and VOB. Conclusion: The Knowledge – Oriented Belief dimension greatly influence compliance to measles vaccination. Other determinants of compliance like the area of residence, educational attainment, and family income significantly increase the Filipino mothers’ likelihood of compliance to measles vaccination, which have implications to health education.

Keywords: socio-demographic, health beliefs, compliance, measles vaccination

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Authors: Klaus Froehlich-Gildhoff, Ullrich Boettinger, Katharina Rauh, Angela Schickler

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The Prevention Network Ortenaukreis, PNO, funded by the German Ministry of Education and Research, aims to promote physical and mental health as well as the social inclusion of 3 to 10 years old children and their families in the Ortenau district. Within a period of four years starting 11/2014 a community network will be established. One regional and five local prevention representatives are building networks with stakeholders of the prevention and health promotion field bridging the health care, educational and youth welfare system in a multidisciplinary approach. The regional prevention representative implements regularly convening prevention and health conferences. On a local level, the 5 local prevention representatives implement round tables in each area as a platform for networking. In the setting approach, educational institutions are playing a vital role when gaining access to children and their families. Thus the project will offer 18 month long organizational development processes with specially trained coaches to 25 kindergarten and 25 primary schools. The process is based on a curriculum of prevention and health promotion which is adapted to the specific needs of the institutions. Also to ensure that the entire region is reached demand oriented advanced education courses are implemented at participating day care centers, kindergartens and schools. Evaluation method: The project is accompanied by an extensive research design to evaluate the outcomes of different project components such as interview data from community prevention agents, interviews and network analysis with families at risk on their support structures, data on community network development and monitoring, as well as data from kindergarten and primary schools. The latter features a waiting-list control group evaluation in kindergarten and primary schools with a mixed methods design using questionnaires and interviews with pedagogues, teachers, parents, and children. Results: By the time of the conference pre and post test data from the kindergarten samples (treatment and control group) will be presented, as well as data from the first project phase, such as qualitative interviews with the prevention coordinators as well as mixed methods data from the community needs assessment. In supporting this project, the Federal Ministry aims to gain insight into efficient components of community prevention and health promotion networks as it is implemented and evaluated. The district will serve as a model region, so that successful components can be transferred to other regions throughout Germany. Accordingly, the transferability to other regions is of high interest in this project.

Keywords: childhood research, health promotion, physical health, prevention network, psychological well-being, social inclusion

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Authors: B. Noruziaan, A. Shvarzman, R. Leahy

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An innovative flooring underlayment was produced and tested. The composite system is made of common OSB boards and a layer of eco-friendly non-cement gypsum based material (GeoGypTM). It was found that the shear bond between the two materials is sufficient to secure the composite interaction between the two. The very high compressive strength and relatively high tensile strength of the non-cement based component together with its high modulus of elasticity provides enough strength and stiffness for the composite product to cover wider spacing between the joists. The initial findings of this study indicate that with joist spacing as wide as 800 mm, the flooring system provides enough strength without compromising the serviceability requirements of the building codes.

Keywords: Composite, floor deck, gypsum based, lumber joist, non-cement, oriented strandboard, shear bond

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Authors: Chen-Sheng Chen, Tsung-Yi Huang, Pi-Chang Sun

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Chronic stroke patients often have complaints about hand dysfunction due to flexor hypertonia and extensor weakness, which makes it difficult to open their affected hand for functional grasp. Hand rehabilitation after stroke is essential for restoring functional independence. Constraint-induced movement therapy has shown to be a successful treatment for patients who have acquired certain level of wrist and finger extension. The goal of this study was to investigate the feasibility of task-oriented approach incorporating 3D-printed dynamic hand device by evaluating hand functional performance. This study manufactured a hand device using 3d printer for chronic stroke. The experimental group engaged task-oriented approach with dynamic hand device, but the control group only received task-oriented approach. Outcome measurements include palmar pinch force (PPF), lateral pinch force (LPF), grip force (GF), and Box and Blocks Test (BBT). The results of study revealed the improvement of PPF in experimental group but not in control group. Meanwhile, improvement in LPF, GF and BBT can be found in both groups. This study demonstrates that the 3D-printed dynamic hand device is an effective therapeutic assistive device to improve pinch force, grasp force, and dexterity and facilitate motivation during home program in individuals with chronic stroke.

Keywords: 3D printing, biomechanics, hand orthosis, stroke

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Authors: Zhaofeng Li, Jun Kang Chow, Yu-Hsing Wang

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This paper attempts to bridge the soil properties and the mechanical response of soil in the discrete element method (DEM) simulation. The artificial neural network (ANN) was therefore adopted, aiming to reproduce the stress-strain-volumetric response when soil properties are given. 31 biaxial shearing tests with varying soil parameters (e.g., initial void ratio and interparticle friction coefficient) were generated using the DEM simulations. Based on these 45 sets of training data, a three-layer neural network was established which can output the entire stress-strain-volumetric curve during the shearing process from the input soil parameters. Beyond the training data, 2 additional sets of data were generated to examine the validity of the network, and the stress-strain-volumetric curves for both cases were well reproduced using this network. Overall, the ANN was found promising in predicting the soil behavior and reducing repetitive simulation work.

Keywords: artificial neural network, discrete element method, soil properties, stress-strain-volumetric response

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Authors: Vrince Vimal, Madhav J. Nigam

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Wireless sensor networks have enticed much of the spotlight from researchers all around the world, owing to its extensive applicability in agricultural, industrial and military fields. Energy conservation node deployment stratagems play a notable role for active implementation of Wireless Sensor Networks. Clustering is the approach in wireless sensor networks which improves energy efficiency in the network. The clustering algorithm needs to have an optimum size and number of clusters, as clustering, if not implemented properly, cannot effectively increase the life of the network. In this paper, an algorithm has been proposed to address connectivity issues with the aim of ensuring the uniform energy consumption of nodes in every part of the network. The results obtained after simulation showed that the proposed algorithm has an edge over existing algorithms in terms of throughput and networks lifetime.

Keywords: Wireless Sensor network (WSN), Random Deployment, Clustering, Isolated Nodes, Networks Lifetime

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Authors: Kevin D. Manalo, Jumelyn L. Torres, Noel B. Linsangan

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This paper focuses on a wireless myoelectric prosthesis of the upper-limb that uses a Multilayer Perceptron Neural network with back propagation. The algorithm is widely used in pattern recognition. The network can be used to train signals and be able to use it in performing a function on their own based on sample inputs. The paper makes use of the Neural Network in classifying the electromyography signal that is produced by the muscle in the amputee’s skin surface. The gathered data will be passed on through the Classification Stage wirelessly through Zigbee Technology. The signal will be classified and trained to be used in performing the arm positions in the prosthesis. Through programming using Verilog and using a Field Programmable Gate Array (FPGA) with Zigbee, the EMG signals will be acquired and will be used for classification. The classified signal is used to produce the corresponding Hand Movements (Open, Pick, Hold, and Grip) through the Zigbee controller. The data will then be processed through the MLP Neural Network using MATLAB which then be used for the surface myoelectric prosthesis. Z-test will be used to display the output acquired from using the neural network.

Keywords: field programmable gate array, multilayer perceptron neural network, verilog, zigbee

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Authors: Earleen Jane Fuentes, Regeene Melarese Lim, Franklin Benjamin Tapia, Alexis Pantola

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Mobile Ad-hoc Network (MANET) is an infrastructure-less network of mobile devices, also known as nodes. These nodes heavily rely on each other’s resources such as memory, computing power, and energy. Thus, some nodes may become selective in forwarding packets so as to conserve their resources. These nodes are called misleading nodes. Several reputation-based techniques (e.g. CORE, CONFIDANT, LARS, SORI, OCEAN) and acknowledgment-based techniques (e.g. TWOACK, S-TWOACK, EAACK) have been proposed to detect such nodes. These techniques do not appropriately punish misleading nodes. Hence, this paper addresses the limitations of these techniques using a system called MINDRA.

Keywords: acknowledgment-based techniques, mobile ad-hoc network, selfish nodes, reputation-based techniques

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Authors: Yang Xiong, Hua Cheng

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In this paper, for the basic problem of wireless sensor network topology control and deployment, the Roesser model in rectangular grid sensor networks is presented. In addition, a general constructive realization procedure will be proposed. The procedure enables a distributed implementation of linear systems on a sensor network. A non-trivial example is illustrated.

Keywords: grid sensor networks, Roesser model, state-space realization, multidimensional systems

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Authors: Fatma Ülkü Kavruk

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The comparison of the old and new approaches reveals that the concept of equivalence has been interpreted and categorized in different ways by different scholars throughout the history. The aim of this study is to trace the concept of equivalence in translation theories from the linguistics-oriented era to present, referring to various translation scholars and to provide a critical evaluation of the nature and applicability of the concept of equivalence in today’s world of translation studies. Within the study, various interpretations of equivalence proposed by international scholars in translation studies are to be presented. In order to find out the reflections of these scholars’ approaches to the Turkish scholars’ research, the interpretations of equivalence by various Turkish scholars are to be examined. At the end of the paper, the applicability of the concept of equivalence in real life is to be discussed in light of these approaches.

Keywords: translation studies, equivalence, translation theories, evaluation

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Authors: Frederic Jumelle, Kelvin So, Didan Deng

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In this paper, we are introducing a model of artificial general intelligence (AGI), the functional neural network (FNN), for modeling human decision-making processes. The FNN is composed of multiple artificial mirror neurons (AMN) racing in the network. Each AMN has a similar structure programmed independently by the users and composed of an intention wheel, a motor core, and a sensory core racing at a specific velocity. The mathematics of the node’s formulation and the racing mechanism of multiple nodes in the network will be discussed, and the group decision process with fuzzy logic and the transformation of these conceptual methods into practical methods of simulation and in operations will be developed. Eventually, we will describe some possible future research directions in the fields of finance, education, and medicine, including the opportunity to design an intelligent learning agent with application in AGI. We believe that FNN has a promising potential to transform the way we can compute decision-making and lead to a new generation of AI chips for seamless human-machine interactions (HMI).

Keywords: neural computing, human machine interation, artificial general intelligence, decision processing

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Authors: Han Bing, Yin Zhenjie

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In order to analyze the status of a diesel engine, as well as conduct fault prediction, a new prediction model based on a gray system is proposed in this paper, which takes advantage of the neural network and the genetic algorithm. The proposed GBPGA prediction model builds on the GM (1.5) model and uses a neural network, which is optimized by a genetic algorithm to construct the error compensator. We verify our proposed model on the diesel faulty simulation data and the experimental results show that GBPGA has the potential to employ fault prediction on diesel.

Keywords: fault prediction, neural network, GM(1, 5) genetic algorithm, GBPGA

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Authors: Abhisek Dash

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This study had three neural networks, one for number segmentation, one for number detection and one for number recognition all of which are coupled to one another. All networks were trained on the MNIST dataset and were convolutional. It was assumed that the images had lighter background and darker foreground. The segmentation network took 28x28 images as input and had sixteen outputs. Segmentation training starts when a dark pixel is encountered. Taking a window(7x7) over that pixel as focus, the eight neighborhood of the focus was checked for further dark pixels. The segmentation network was then trained to move in those directions which had dark pixels. To this end the segmentation network had 16 outputs. They were arranged as “go east”, ”don’t go east ”, “go south east”, “don’t go south east”, “go south”, “don’t go south” and so on w.r.t focus window. The focus window was resized into a 28x28 image and the network was trained to consider those neighborhoods which had dark pixels. The neighborhoods which had dark pixels were pushed into a queue in a particular order. The neighborhoods were then popped one at a time stitched to the existing partial image of the number one at a time and trained on which neighborhoods to consider when the new partial image was presented. The above process was repeated until the image was fully covered by the 7x7 neighborhoods and there were no more uncovered black pixels. During testing the network scans and looks for the first dark pixel. From here on the network predicts which neighborhoods to consider and segments the image. After this step the group of neighborhoods are passed into the detection network. The detection network took 28x28 images as input and had two outputs denoting whether a number was detected or not. Since the ground truth of the bounds of a number was known during training the detection network outputted in favor of number not found until the bounds were not met and vice versa. The recognition network was a standard CNN that also took 28x28 images and had 10 outputs for recognition of numbers from 0 to 9. This network was activated only when the detection network votes in favor of number detected. The above methodology could segment connected and overlapping numbers. Additionally the recognition unit was only invoked when a number was detected which minimized false positives. It also eliminated the need for rules of thumb as segmentation is learned. The strategy can also be extended to other characters as well.

Keywords: convolutional neural networks, OCR, text detection, text segmentation

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Authors: Assem I. El-Ansary

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This paper illustrates the event-oriented Behavioral Pattern Analysis (BPA) modeling approach in developing an Multi-Agent Railway Control System (MARCS). The Event defined in BPA is a real-life conceptual entity that is unrelated to any implementation. The major contributions of this research are the Behavioral Pattern Analysis (BPA) modeling methodology, and the development of an interactive software tool (DECISION), which is based on a combination of the Analytic Hierarchy Process (AHP) and the ELECTRE Multi-Criteria Decision Making (MCDM) methods.

Keywords: analysis, multi-agent, railway control, modeling methodology, software modeling, event-oriented, behavioral pattern, use cases

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Authors: John Gibbs, Benjamin Flanders, Dylan Pozorski, Weixuan Liu

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Weight Painting–adjusting the influence a skeletal joint has on a given vertex in a character mesh–is an arduous and time con- suming part of the 3D animation pipeline. This process generally requires a trained technical animator and many hours of work to complete. Our skiNNer plug-in, which works within Autodesk’s Maya 3D animation software, uses Machine Learning and data pro- cessing techniques to create a deep neural network model that can accomplish the weight painting task in seconds rather than hours for bipedal quasi-humanoid character meshes. In order to create a properly trained network, a number of challenges were overcome, including curating an appropriately large data library, managing an arbitrary 3D mesh size, handling arbitrary skeletal architectures, accounting for extreme numeric values (most data points are near 0 or 1 for weight maps), and constructing an appropriate neural network model that can properly capture the high frequency alter- ation between high weight values (near 1.0) and low weight values (near 0.0). The arrived at neural network model is a cross between a traditional CNN, deep residual network, and fully dense network. The resultant network captures the unusually hard-edged features of a weight map matrix, and produces excellent results on many bipedal models.

Keywords: 3d animation, animation, character, rigging, skinning, weight painting, machine learning, artificial intelligence, neural network, deep neural network

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Authors: Muhammad Azam, Micheal Olaolu Arowolo, Fei He, Mihail Popescu, Dong Xu

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The number of biological papers is growing quickly, which means that the number of biological pathway figures in those papers is also increasing quickly. Each pathway figure shows extensive biological information, like the names of genes and how the genes are related. However, manually annotating pathway figures takes a lot of time and work. Even though using advanced image understanding models could speed up the process of curation, these models still need to be made more accurate. To improve gene name recognition from pathway figures, we applied a Siamese network to map image segments to a library of pictures containing known genes in a similar way to person recognition from photos in many photo applications. We used a triple loss function and a triplet spatial pyramid pooling network by combining the triplet convolution neural network and the spatial pyramid pooling (TSPP-Net). We compared VGG19 and VGG16 as the Siamese network model. VGG16 achieved better performance with an accuracy of 93%, which is much higher than OCR results.

Keywords: biological pathway, image understanding, gene name recognition, object detection, Siamese network, VGG

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Authors: Henok Mezemr Besfat, Haftom Gebreslassie Gebregwergs

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wireless network is one of the most important mediums of transmission of information from one device to another devices. Wireless communication has a broad range of applications, including mobile communications through cell phones and satellites, Internet of Things (IoT) connecting several devices, wireless sensor networks for traffic management and environmental monitoring, satellite communication for weather forecasting and TV without requiring any cable or wire or other electronic conductors, by using electromagnetic waves like IR, RF, satellite, etc. This paper summarizes different wireless network technologies, applications of different wireless technologies and different types of wireless networks. Generally, wireless technology will further enhance operations and experiences across sectors with continued innovation. This paper suggests different strategies that can improve wireless networks and technologies.

Keywords: wireless senser, wireless technology, wireless network, internet of things

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Authors: Vitalijs Lusis, Andrejs Krasnikovs

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Fiber reinforced concrete is important material for load bearing structural elements. Usually fibers are homogeneously distributed in a concrete body having arbitrary spatial orientations. At the same time, in many situations, fiber concrete with oriented fibers is more optimal. Is obvious, that is possible to create constructions with oriented short fibers in them, in different ways. Present research is devoted to one of such approaches- fiber reinforced concrete prisms having dimensions 100 mm×100 mm×400 mm with layers of non-homogeneously distributed fibers inside them were fabricated. Simultaneously prisms with homogeneously dispersed fibers were produced for reference as well. Prisms were tested under four point bending conditions. During the tests vertical deflection at the center of every prism and crack opening were measured (using linear displacements transducers in real timescale). Prediction results were discussed.

Keywords: fiber reinforced concrete, 4-point bending, steel fiber, construction engineering

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Authors: Oluwaponmile David Alao

Abstract:

Misdiagnosis has been the major problem in health sector. Heart attack has been one of diseases that have high level of misdiagnosis recorded on the part of physicians. In this paper, an intelligent system has been developed for diagnosis of heart attack in the health sector. Dataset of heart attack obtained from UCI repository has been used. This dataset is made up of thirteen attributes which are very vital in diagnosis of heart disease. The system is developed on the multilayer perceptron trained with back propagation neural network then simulated with feed forward neural network and a recognition rate of 87% was obtained which is a good result for diagnosis of heart attack in medical field.

Keywords: heart attack, artificial neural network, diagnosis, intelligent system

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Authors: İkbal Eski

Abstract:

This investigation is researched on design of robust neural network predictors for analyzing vibration effects on moving parts of a drilling machine. Moreover, the research is divided two parts; first part is experimental investigation, second part is simulation analysis with neural networks. Therefore, a real time the drilling machine is used to vibrations during working conditions. The measured real vibration parameters are analyzed with proposed neural network. As results: Simulation approaches show that Radial Basis Neural Network has good performance to adapt real time parameters of the drilling machine.

Keywords: artificial neural network, vibration analyses, drilling machine, robust

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Authors: Safak Baykal

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The purpose of this study is analyzing the relationship between self leadership and social capital of people with using Social Network Analysis. In this study, two aspects of social capital will be focused: bonding, homophilous social capital (BoSC) which implies better, strong, dense or closed network ties, and bridging, heterophilous social capital (BrSC) which implies weak ties, bridging the structural holes. The other concept of the study is Trust (Tr), namely interpersonal trust, willingness to ascribe good intentions to and have confidence in the words and actions of other people. In this study, the sample group, 61 people, was selected from a private firm from the defense industry. The relation between BoSC/BrSC and Tr is shown by using Social Network Analysis (SNA) and statistical analysis with Likert type-questionnaire. The results of the analysis show the Cronbach’s alpha value is 0.73 and social capital values (BoSC/BrSC) is highly correlated with Tr values of the people.

Keywords: bonding social capital, bridging social capital, trust, social network analysis (SNA)

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Authors: Ghorab Sara, Meziani Lila, Rubin Harvey Stuart

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The rapid growth of deep learning has led to intricate and resource-intensive deep neural networks widely used in computer vision tasks. However, their complexity results in high computational demands and memory usage, hindering real-time application. To address this, research focuses on model compression techniques. The paper provides an overview of recent advancements in compressing neural networks and categorizes the various methods into four main approaches: network pruning, quantization, network decomposition, and knowledge distillation. This paper aims to provide a comprehensive outline of both the advantages and limitations of each method.

Keywords: model compression, deep neural network, pruning, knowledge distillation, quantization, low-rank decomposition

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Authors: Emrehan Ozcan, Dilek Beyazlı

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The development of tourism depends on an accurate planning approach as well as on the right planning process. This dependency is also a key factor in ensuring sustainability of tourism. The types of tourism, social expectations, planning practice, the socio-economic and the cultural structure of the region are determinants of planning approaches for tourism development. The tourism plans prepared for the historic city centers are usually based on the revitalization of cultural and historical values. The preservation and development of the tourism potentials of the historic city centers are important for providing an economic contribution to the locality, creating livable solutions for local residents and also the sustainability of tourism. This research is about experiencing and discussing a planning approach that will provide tourism development based on historical and cultural values. Historical and cultural values in the historical city center of Trabzon -which has a settlement history of approximately 4000 years, is located on the Black Sea coast of Turkey- wear out over years and lose their tourism potential. A planning study has been experienced with strategic spatial planning approach for Trabzon, which has not done a tourism-oriented planning study until now. The stages of the planning process provided by strategic spatial planning approach are an assessment of the current situation; vision, strategies, and actions; action planning; designing and implementation of actions and monitoring-evaluation. In the discussion section, the advantages, planning process, methods and techniques of the approach are discussed for the possibilities and constraints in terms of tourism planning. In this context, it is aimed to put forth tourism planning process, stages, and implementation tools within the scope of strategic spatial planning approach by comparing approaches used in the tourism-oriented/priority planning of historical city centers. Suggestions on the position and effect of the preferred planning approach in the existing spatial planning practice are the outputs of the study.

Keywords: cultural heritage, tourism oriented planning, Trabzon, strategic spatial Planning

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Authors: Mary Anne Roa

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Congestion in network occurs due to exceed in aggregate demand as compared to the accessible capacity of the resources. Network congestion will increase as network speed increases and new effective congestion control methods are needed, especially for today’s very high speed networks. To address this undeniably global issue, the study focuses on the development of a fuzzy-based congestion control model concerned with allocating the resources of a computer network such that the system can operate at an adequate performance level when the demand exceeds or is near the capacity of the resources. Fuzzy logic based models have proven capable of accurately representing a wide variety of processes. The model built is based on bandwidth, the aggregate incoming traffic and the waiting time. The theoretical analysis and simulation results show that the proposed algorithm provides not only good utilization but also low packet loss.

Keywords: congestion control, queue management, computer networks, fuzzy logic

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Authors: Alexander Hempfing

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The desire to understand business cycle fluctuations, trade interdependencies and co-movement has a long tradition in economic thinking. From input-output economics to business cycle theory, researchers aimed to find appropriate answers from an empirical as well as a theoretical perspective. This paper empirically analyses how the production structure of the global economy and several states developed over time, what their distributional properties are and if there are network specific metrics that allow identifying structurally important nodes, on a global, national and sectoral scale. For this, the World Input-Output Database was used, and different statistical methods were applied. Empirical evidence is provided that the importance of the Eastern hemisphere in the global production network has increased significantly between 2000 and 2014. Moreover, it was possible to show that the sectoral eigenvector centrality indices on a global level are power-law distributed, providing evidence that specific national sectors exist which are more critical to the world economy than others while serving as a hub within the global production network. However, further findings suggest, that global production cannot be characterized as a scale-free network.

Keywords: economic integration, industrial organization, input-output economics, network economics, production networks

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Authors: M. R. Martinez-Torres, S. L. Toral, M. Olmedilla

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Typically, virtual communities exhibit the well-known phenomenon of participation inequality, which means that only a small percentage of users is responsible of the majority of contributions. However, the sustainability of the community requires that the group of active users must be continuously nurtured with new users that gain expertise through a participation process. This paper analyzes the time evolution of Open Source Software (OSS) communities, considering users that join/abandon the community over time and several topological properties of the network when modeled as a social network. More specifically, the paper analyzes the role of those users rejoining the community and their influence in the global characteristics of the network.

Keywords: open source communities, social network Analysis, time series, virtual communities

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