Search results for: Structured P2P Overlay Networks

Authors: María-Dolores Afonso-Suárez, Nayra Pumar-Carreras, Juan Ruiz-Alzola

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This research addresses the use of an e-Learning creation methodology for learning objects. Throughout the process, indicators are being gathered, to determine if it responds to the main objectives of an engineering discipline. These parameters will also indicate if it is necessary to review the creation cycle and readjust any phase. Within the project developed for this study, apart from the use of structured methods, there has been a central objective: the establishment of a learning atmosphere. A place where all the professionals involved are able to collaborate, plan, solve problems and determine guides to follow in order to develop creative and innovative solutions. It has been outlined as a blended learning program with an assessment plan that proposes face to face lessons, coaching, collaboration, multimedia and web based learning objects as well as support resources. The project has been drawn as a long term task, the pilot teaching actions designed provide the preliminary results object of study. This methodology is been used in the creation of learning content for the African countries of Senegal, Mauritania and Cape Verde. It has been developed within the framework of the MACbioIDi, an Interreg European project for the International cooperation and development. The educational area of this project is focused in the training and advice of professionals of the medicine as well as engineers in the use of applications of medical imaging technology, specifically the 3DSlicer application and the Open Anatomy Browser.

Keywords: Teaching contents engineering, e-learning, blended learning, international cooperation, 3DSlicer, open anatomy browser.

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Authors: Raksha Sharma, Vishnu Kant Soni, Manoj Kumar Mishra, Prachet Bhuyan, Utpal Chandra Dey

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Grid computing is a group of clusters connected over high-speed networks that involves coordinating and sharing computational power, data storage and network resources operating across dynamic and geographically dispersed locations. Resource management and job scheduling are critical tasks in grid computing. Resource selection becomes challenging due to heterogeneity and dynamic availability of resources. Job scheduling is a NP-complete problem and different heuristics may be used to reach an optimal or near optimal solution. This paper proposes a model for resource and job scheduling in dynamic grid environment. The main focus is to maximize the resource utilization and minimize processing time of jobs. Grid resource selection strategy is based on Max Heap Tree (MHT) that best suits for large scale application and root node of MHT is selected for job submission. Job grouping concept is used to maximize resource utilization for scheduling of jobs in grid computing. Proposed resource selection model and job grouping concept are used to enhance scalability, robustness, efficiency and load balancing ability of the grid.

Keywords: Agent, Grid Computing, Job Grouping, Max Heap Tree (MHT), Resource Scheduling.

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Authors: Eric P. Jiang

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As the Internet continues to grow at a rapid pace as the primary medium for communications and commerce and as telecommunication networks and systems continue to expand their global reach, digital information has become the most popular and important information resource and our dependence upon the underlying cyber infrastructure has been increasing significantly. Unfortunately, as our dependency has grown, so has the threat to the cyber infrastructure from spammers, attackers and criminal enterprises. In this paper, we propose a new machine learning based network intrusion detection framework for cyber security. The detection process of the framework consists of two stages: model construction and intrusion detection. In the model construction stage, a semi-supervised machine learning algorithm is applied to a collected set of network audit data to generate a profile of normal network behavior and in the intrusion detection stage, input network events are analyzed and compared with the patterns gathered in the profile, and some of them are then flagged as anomalies should these events are sufficiently far from the expected normal behavior. The proposed framework is particularly applicable to the situations where there is only a small amount of labeled network training data available, which is very typical in real world network environments.

Keywords: classification, data analysis and mining, network intrusion detection, semi-supervised learning.

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Authors: Mariyam Arif, Ye Liu, Israr Ul Haq, Ahsan Ashfaq

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High cost of fossil fuels and intensifying installations of alternate energy generation sources are intimidating main challenges in power systems. Making accurate load forecasting an important and challenging task for optimal energy planning and management at both distribution and generation side. There are many techniques to forecast load but each technique comes with its own limitation and requires data to accurately predict the forecast load. Artificial Neural Network (ANN) is one such technique to efficiently forecast the load. Comparison between two different ranges of input datasets has been applied to dynamic ANN technique using MATLAB Neural Network Toolbox. It has been observed that selection of input data on training of a network has significant effects on forecasted results. Day-wise input data forecasted the load accurately as compared to year-wise input data. The forecasted load is then distributed among the six generators by using the linear programming to get the optimal point of generation. The algorithm is then verified by comparing the results of each generator with their respective generation limits.

Keywords: Artificial neural networks, demand-side management, economic dispatch, linear programming, power generation dispatch.

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Authors: B. Farahani, R. Fadil, A. Aboonabi, B. Hoffmann, J. Loscheider, K. Tavakolian, S. Arzanpour

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Wheelchair users spend long hours in a sitting position, and selecting the right cushion is highly critical in preventing pressure ulcers in that demographic. Pressure Mapping Systems (PMS) are typically used in clinical settings by therapists to identify the sitting profile and pressure points in the sitting area to select the cushion that fits the best for the users. A PMS is a flexible mat composed of arrays of distributed networks of pressure sensors. The output of the PMS systems is a color-coded image that shows the intensity of the pressure concentration. Therapists use the PMS images to compare different cushions fit for each user. This process is highly subjective and requires good visual memory for the best outcome. This paper aims to develop an image processing technique to analyze the images of PMS and provide an objective measure to assess the cushions based on their pressure distribution mappings. In this paper, we first reviewed the skeletal anatomy of the human sitting area and its relation to the PMS image. This knowledge is then used to identify the important features that must be considered in image processing. We then developed an algorithm based on those features to analyze the images and rank them according to their fit to the user's needs. 

Keywords: cushion, image processing, pressure mapping system, wheelchair

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Authors: Selma Sbit, Mohamed Bechir Dadi, Belgacem Chibani Rhaimi

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Incorporating Home eNodeB (HeNB) in cellular networks, e.g. Long Term Evolution Advanced (LTE-A), is beneficial for extending coverage and enhancing capacity at low price especially within the non-line-of sight (NLOS) environments such as homes. HeNB or femtocell is a small low powered base station which provides radio coverage to the mobile users in an indoor environment. This deployment results in a heterogeneous network where the available spectrum becomes shared between two layers. Therefore, a problem of Inter Cell Interference (ICI) appears. This issue is the main challenge in LTE-A. To deal with this challenge, various techniques based on frequency, time and power control are proposed. This paper deals with the impact of carrier aggregation and higher order MIMO (Multiple Input Multiple Output) schemes on the LTE-Advanced performance. Simulation results show the advantages of these schemes on the system capacity (4.10⁹ b/s/Hz when bandwidth B=100 MHz and when applying MIMO 8x8 for SINR=30 dB), maximum theoretical peak data rate (more than 4 Gbps for B=100 MHz and when MIMO 8x8 is used) and spectral efficiency (15 b/s/Hz and 30b/s/Hz when MIMO 4x4 and MIMO 8x8 are applying respectively for SINR=30 dB).

Keywords: LTE-Advanced, carrier aggregation, MIMO, capacity, peak data rate, spectral efficiency.

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Authors: Yi Chao Ma, Cheng Siong Chin, Wai Lok Woo

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Since the last decade, there has been a rapid growth in digital multimedia, such as high-resolution media files and threedimentional movies. Hence, there is a need for large digital storage such as Hard Disk Drive (HDD). As such, users expect to have a quieter HDD in their laptop. In this paper, a jury test has been conducted on a group of 34 people where 17 of them are students who are the potential consumer, and the remaining are engineers who know the HDD. A total 13 HDD sound samples have been selected from over hundred HDD noise recordings. These samples are selected based on an agreed subjective feeling. The samples are played to the participants using head acoustic playback system, which enabled them to experience as similar as possible the same environment as have been recorded. Analysis has been conducted and the obtained results have indicated different group has different perception over the noises. Two neural network-based acoustic annoyance models are established based on back propagation neural network. Four psychoacoustic metrics, loudness, sharpness, roughness and fluctuation strength, are used as the input of the model, and the subjective evaluation results are taken as the output. The developed models are reasonably accurate in simulating both training and test samples.

Keywords: Hard disk drive noise, jury test, neural network model, psychoacoustic annoyance.

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Authors: Siddhant Rao

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Studies estimate that there will be 266,120 new cases of invasive breast cancer and 40,920 breast cancer induced deaths in the year of 2018 alone. Despite the pervasiveness of this affliction, the current process to obtain an accurate breast cancer prognosis is tedious and time consuming. It usually requires a trained pathologist to manually examine histopathological images and identify the features that characterize various cancer severity levels. We propose MITOS-RCNN: a region based convolutional neural network (RCNN) geared for small object detection to accurately grade one of the three factors that characterize tumor belligerence described by the Nottingham Grading System: mitotic count. Other computational approaches to mitotic figure counting and detection do not demonstrate ample recall or precision to be clinically viable. Our models outperformed all previous participants in the ICPR 2012 challenge, the AMIDA 2013 challenge and the MITOS-ATYPIA-14 challenge along with recently published works. Our model achieved an F- measure score of 0.955, a 6.11% improvement in accuracy from the most accurate of the previously proposed models.

Keywords: Object detection, histopathology, breast cancer, mitotic count, deep learning, computer vision.

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Authors: Pragya Paneru

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This paper explores altogether 10 primary teachers’ views on gender representation in primary level textbooks. Data were collected from the teachers who taught in private schools in the Kailali and Kathmandu districts. This research uses a semi-structured interview method to obtain information regarding teachers’ attitudes toward gender representations in textbook contents. The interview data were analysed by using critical skills of qualitative research. The findings revealed that most of the teachers were unaware and regarded gender issues as insignificant to discuss in primary-level classes. Most of them responded to the questions personally and claimed that there were no gender issues in their classrooms. Some of the teachers connected gender issues with contexts other than textbook representations such as school discrimination in the distribution of salary among male and female teachers, school practices of awarding girls rather than boys as the most disciplined students, following girls’ first rule in the assembly marching, encouraging only girls in the stage shows, and involving students in gender-specific activities such as decorating works for girls and physical tasks for boys. The interview also revealed teachers’ covert gendered attitudes in their remarks. Nevertheless, most of the teachers accepted that gender-biased contents have an impact on learners and this problem can be solved with more gender-centred research in the education field, discussions, and training to increase awareness regarding gender issues. Agreeing with the suggestion of teachers, this paper recommends proper training and awareness regarding how to confront gender issues in textbooks.

Keywords: Content analysis, gender equality, school education, critical awareness.

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Authors: Hamid R. S. Mojaveri, Seyed S. Mousavi, Mojtaba Heydar, Ahmad Aminian

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The aim of this paper is to present a methodology in three steps to forecast supply chain demand. In first step, various data mining techniques are applied in order to prepare data for entering into forecasting models. In second step, the modeling step, an artificial neural network and support vector machine is presented after defining Mean Absolute Percentage Error index for measuring error. The structure of artificial neural network is selected based on previous researchers' results and in this article the accuracy of network is increased by using sensitivity analysis. The best forecast for classical forecasting methods (Moving Average, Exponential Smoothing, and Exponential Smoothing with Trend) is resulted based on prepared data and this forecast is compared with result of support vector machine and proposed artificial neural network. The results show that artificial neural network can forecast more precisely in comparison with other methods. Finally, forecasting methods' stability is analyzed by using raw data and even the effectiveness of clustering analysis is measured.

Keywords: Artificial Neural Networks (ANN), bullwhip effect, demand forecasting, Support Vector Machine (SVM).

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Authors: Ranjan Sarukkalige, Dinushi Gamage

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The aim of this study was to develop a storm water quality improvement strategy plan (WQISP) which assists managers and decision makers of local city councils in enhancing their activities to improve regional water quality. City of Gosnells in Western Australia has been considered as a case study. The procedure on developing the WQISP consists of reviewing existing water quality data, identifying water quality issues in the study areas and developing a decision making tool for the officers, managers and decision makers. It was found that land use type is the main factor affecting the water quality. Therefore, activities, sources and pollutants related to different land use types including residential, industrial, agricultural and commercial are given high importance during the study. Semi-structured interviews were carried out with coordinators of different management sections of the regional councils in order to understand the associated management framework and issues. The issues identified from these interviews were used in preparing the decision making tool. Variables associated with the defined “value versus threat" decision making tool are obtained from the intensive literature review. The main recommendations provided for improvement of water quality in local city councils, include non-structural, structural and management controls and potential impacts of climate change.

Keywords: Storm water quality, Storm water Management, Land use, Strategy plan

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Authors: Izaz Ahmad, Farhatullah, Shahbaz Ali, Farhad Ali, Faiza, Hazrat Junaid, Farhan Zaid

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Current trends in remote health monitoring to monetize on the Internet of Things applications have been raised in efficient and interference free communications in Wireless Body Area Network (WBAN) scenario. Co-existence interference in WBANs have aggravates the over-congested radio bands, thereby requiring efficient Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) strategies and improve interference management. Existing solutions utilize simplistic heuristics to approach interference problems. The scope of this research article is to investigate reinforcement learning for efficient interference management under co-existing scenarios with an emphasis on homogenous interferences. The aim of this paper is to suggest a smart CSMA/CA mechanism based on reinforcement learning called QIM-MAC that effectively uses sense slots with minimal interference. Simulation results are analyzed based on scenarios which show that the proposed approach maximized Average Network Throughput and Packet Delivery Ratio and minimized Packet Loss Ratio, Energy Consumption and Average Delay.

Keywords: WBAN, IEEE 802.15.4 Standard, CAP Super-frame, Q-Learning.

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Authors: Rampal Singh, S. Balasundaram

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In this paper, we study the application of Extreme Learning Machine (ELM) algorithm for single layered feedforward neural networks to non-linear chaotic time series problems. In this algorithm the input weights and the hidden layer bias are randomly chosen. The ELM formulation leads to solving a system of linear equations in terms of the unknown weights connecting the hidden layer to the output layer. The solution of this general system of linear equations will be obtained using Moore-Penrose generalized pseudo inverse. For the study of the application of the method we consider the time series generated by the Mackey Glass delay differential equation with different time delays, Santa Fe A and UCR heart beat rate ECG time series. For the choice of sigmoid, sin and hardlim activation functions the optimal values for the memory order and the number of hidden neurons which give the best prediction performance in terms of root mean square error are determined. It is observed that the results obtained are in close agreement with the exact solution of the problems considered which clearly shows that ELM is a very promising alternative method for time series prediction.

Keywords: Chaotic time series, Extreme learning machine, Generalization performance.

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Authors: Md. Ashikur Rahman Khan, M. M. Rahman, K. Kadirgama, M.A. Maleque, Rosli A. Bakar

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Conventionally the selection of parameters depends intensely on the operator-s experience or conservative technological data provided by the EDM equipment manufacturers that assign inconsistent machining performance. The parameter settings given by the manufacturers are only relevant with common steel grades. A single parameter change influences the process in a complex way. Hence, the present research proposes artificial neural network (ANN) models for the prediction of surface roughness on first commenced Ti-15-3 alloy in electrical discharge machining (EDM) process. The proposed models use peak current, pulse on time, pulse off time and servo voltage as input parameters. Multilayer perceptron (MLP) with three hidden layer feedforward networks are applied. An assessment is carried out with the models of distinct hidden layer. Training of the models is performed with data from an extensive series of experiments utilizing copper electrode as positive polarity. The predictions based on the above developed models have been verified with another set of experiments and are found to be in good agreement with the experimental results. Beside this they can be exercised as precious tools for the process planning for EDM.

Keywords: Ti-15l-3, surface roughness, copper, positive polarity, multi-layered perceptron.

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Authors: Lin Yang, Zhian Mi, Jiacheng Xiao, Rong Li

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Swimming with the tide of deep learning, the field of music information retrieval (MIR) experiences parallel development and a sheer variety of feature-learning models has been applied to music classification and tagging tasks. Among those learning techniques, the deep convolutional neural networks (CNNs) have been widespreadly used with better performance than the traditional approach especially in music genre classification and prediction. However, regarding the music recommendation, there is a large semantic gap between the corresponding audio genres and the various aspects of a song that influence user preference. In our study, aiming to bridge the gap, we strive to construct an automatic music aesthetic annotation model with MIDI format for better comparison and measurement of the similarity between music pieces in the way of harmonic analysis. We use the matrix of qualification converted from MIDI files as input to train two different classifiers, support vector machine (SVM) and Decision Tree (DT). Experimental results in performance of a tag prediction task have shown that both learning algorithms are capable of extracting high-level properties in an end-to end manner from music information. The proposed model is helpful to learn the audience taste and then the resulting recommendations are likely to appeal to a niche consumer.

Keywords: Harmonic analysis, machine learning, music classification and tagging, MIDI.

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Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: Crime prediction, machine learning, public safety, smart city.

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Authors: Shidvash Vakilipour, Masoud Mohammadi, Rouzbeh Riazi, Scott Ormiston, Kimia Amiri, Sahar Barati

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An essential component of a finite volume method (FVM) is the advection scheme that estimates values on the cell faces based on the calculated values on the nodes or cell centers. The most widely used advection schemes are upwind schemes. These schemes have been developed in FVM on different kinds of structured and unstructured grids. In this research, the physical influence scheme (PIS) is developed for a cell-centered FVM that uses an implicit coupled solver. Results are compared with the exponential differencing scheme (EDS) and the skew upwind differencing scheme (SUDS). Accuracy of these schemes is evaluated for a lid-driven cavity flow at Re = 1000, 3200, and 5000 and a backward-facing step flow at Re = 800. Simulations show considerable differences between the results of EDS scheme with benchmarks, especially for the lid-driven cavity flow at high Reynolds numbers. These differences occur due to false diffusion. Comparing SUDS and PIS schemes shows relatively close results for the backward-facing step flow and different results in lid-driven cavity flow. The poor results of SUDS in the lid-driven cavity flow can be related to its lack of sensitivity to the pressure difference between cell face and upwind points, which is critical for the prediction of such vortex dominant flows.

Keywords: Cell-centered finite volume method, physical influence scheme, exponential differencing scheme, skew upwind differencing scheme, false diffusion.

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Authors: Tania Elena González Alvarado, Ma. Antonieta Martín Granados

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The aim of this paper is to explain what a multienterprise tie is, what evidence its analysis provides and how does the cooperation mechanism influence the establishment of a multienterprise tie. The study focuses on businesses of smaller dimension, geographically dispersed and whose businessmen are learning to cooperate in an international environment. The empirical evidence obtained at this moment permits to conclude the following: The tie is not long-lasting, it has an end; opportunism is an opportunity to learn; the multi-enterprise tie is a space to learn about the cooperation mechanism; the local tie permits a businessman to alternate between competition and cooperation strategies; the disappearance of a tie is an experience of learning for a businessman, diminishing the possibility of failure in the next tie; the cooperation mechanism tends to eliminate hierarchical relations; the multienterprise tie diminishes the asymmetries and permits SME-s to have a better position when they negotiate with large companies; the multi-enterprise tie impacts positively on the local system. The collection of empirical evidence was done trough the following instruments: direct observation in a business encounter to which the businesses attended in 2003 (202 Mexican agro industry SME-s), a survey applied in 2004 (129), a questionnaire applied in 2005 (86 businesses), field visits to the businesses during the period 2006-2008 and; a survey applied by telephone in 2008 (55 Mexican agro industry SME-s).

Keywords: Cooperation, multi-enterprise tie, links, networks.

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Authors: A. F. Ali, D. M. Shawky

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Discovering new biological knowledge from the highthroughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed a new approach for protein classification. Proteins that are evolutionarily- and thereby functionally- related are said to belong to the same classification. Identifying protein classification is of fundamental importance to document the diversity of the known protein universe. It also provides a means to determine the functional roles of newly discovered protein sequences. Our goal is to predict the functional classification of novel protein sequences based on a set of features extracted from each protein sequence. The proposed technique used datasets extracted from the Structural Classification of Proteins (SCOP) database. A set of spectral domain features based on Fast Fourier Transform (FFT) is used. The proposed classifier uses multilayer back propagation (MLBP) neural network for protein classification. The maximum classification accuracy is about 91% when applying the classifier to the full four levels of the SCOP database. However, it reaches a maximum of 96% when limiting the classification to the family level. The classification results reveal that spectral domain contains information that can be used for classification with high accuracy. In addition, the results emphasize that sequence similarity measures are of great importance especially at the family level.

Keywords: Bioinformatics, Artificial Neural Networks, Protein Sequence Analysis, Feature Extraction.

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Authors: Mauro Giacomini, Stefania Bertone, Federico Caneva Soumetz, Carmelina Ruggiero

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Environmental micro-organisms include a large number of taxa and some species that are generally considered nonpathogenic, but can represent a risk in certain conditions, especially for elderly people and immunocompromised individuals. Chemotaxonomic identification techniques are powerful tools for environmental micro-organisms, and cellular fatty acid methyl esters (FAME) content is a powerful fingerprinting identification technique. A system based on an unsupervised artificial neural network (ANN) was set up using the fatty acid profiles of standard bacterial strains, obtained by gas-chromatography, used as learning data. We analysed 45 certified strains belonging to Acinetobacter, Aeromonas, Alcaligenes, Aquaspirillum, Arthrobacter, Bacillus, Brevundimonas, Enterobacter, Flavobacterium, Micrococcus, Pseudomonas, Serratia, Shewanella and Vibrio genera. A set of 79 bacteria isolated from a drinking water line (AMGA, the major water supply system in Genoa) were used as an example for identification compared to standard MIDI method. The resulting ANN output map was found to be a very powerful tool to identify these fresh isolates.

Keywords: Cellular fatty acid methyl esters, environmental bacteria, gas-chromatography, unsupervised ANN.

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Authors: Muhammad Atif, Cang Yan

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The enhancement of low-light images is a significant area of study aimed at enhancing the quality of captured images in challenging lighting environments. Recently, methods based on Convolutional Neural Networks (CNN) have gained prominence as they offer state-of-the-art performance. However, many approaches based on CNN rely on increasing the size and complexity of the neural network. In this study, we propose an alternative method for improving low-light images using an Autoencoders-based multiscale knowledge transfer model. Our method leverages the power of three autoencoders, where the encoders of the first two autoencoders are directly connected to the decoder of the third autoencoder. Additionally, the decoder of the first two autoencoders is connected to the encoder of the third autoencoder. This architecture enables effective knowledge transfer, allowing the third autoencoder to learn and benefit from the enhanced knowledge extracted by the first two autoencoders. We further integrate the proposed model into the Pix-to-Pix GAN framework. By integrating our proposed model as the generator in the GAN framework, we aim to produce enhanced images that not only exhibit improved visual quality but also possess a more authentic and realistic appearance. These experimental results, both qualitative and quantitative, show that our method is better than the state-of-the-art methodologies.

Keywords: Low light image enhancement, deep learning, convolutional neural network, image processing.

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Authors: Georgios Linardatos, Miltiadis Zamparas, Vlasoula Bekiari, Georgios Bokias, Georgios Hotos

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Hydrogels are three-dimensional, hydrophilic, polymeric networks composed of homopolymers or copolymers and are insoluble in water due to the presence of chemical or physical cross-links. When hydrogels come in contact with aqueous solutions, they can effectively sorb and retain the dissolved substances, depending on the nature of the monomeric units comprising the hydrogel. For this reason, hydrogels have been proposed in several studies as water purification agents. At the present work anionic hydrogels bearing negatively charged –COO- groups were prepared and investigated. These gels are based on sodium acrylate (ANa), either homopolymerized (poly(sodiumacrylate), PANa) or copolymerized (P(DMAM-co-ANa)) with N,N Dimethylacrylamide (DMAM). The hydrogels were used to extract some model organic dyes from water. It is found that cationic dyes are strongly sorbed and retained by the hydrogels, while sorption of anionic dyes was negligible. In all cases it was found that both maximum sorption capacity and equilibrium binding constant varied from one dye to the other depending on the chemical structure of the dye, the presence of functional chemical groups and the hydrophobic-hydrophilic balance. Finally, the nonionic hydrogel of the homopolymer poly(N,Ndimethylacrylamide), PDMAM, was also used for reasons of comparison.

Keywords: Anionic organic hydrogels, sorption, organic dyes, water purification agents.

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Authors: Aziz, Nur Mardhiyah, Salleh, Hafez

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Implementing Information Technology/ Information System (IT/IS) is critical for every industry as its potential benefits have been to motivate many industries including the Malaysian construction industry to invest in it. To successfully implement IT/IS has become the major concern for every organisation. Identifying the critical success factors (CSFs) has become the main agenda for researchers, academicians and practitioners due to the wide number of failures reported. This research paper seeks to identify the CSFs that influence the successful implementation of IT/IS in construction industry in Malaysia. Limited factors relating to people issue will be highlighted here to showcase some as it becomes one of the major contributing factors to the failure. Three (3) organisations have participated in this study. Semi-structured interviews are employed as they offer sufficient flexibility to ensure that all relevant factors are covered. Several key issues contributing to successful implementations of IT/IS are identified. The results of this study reveal that top management support, communication, user involvement, IT staff roles and responsibility, training/skills, leader/ IT Leader, organisation culture, knowledge/ experience, motivation, awareness, focus and ambition, satisfaction, teamwork/ collaboration, willingness to change, attitude, commitment, management style, interest in IT, employee behaviour towards collaborative environment, trust, interpersonal relationship, personal characteristic and competencies are significantly associated with the successful implementations of IT/IS. It is anticipated that this study will create awareness and contribute to a better understanding amongst construction industry players and will assist them to successfully implement IT/IS.

Keywords: critical success factors, construction industry , IT/IS, people

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Authors: Joseph S. Ojo, Vincent A. Akpan, Oladayo G. Ajileye, Olalekan L, Ojo

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In this paper, the intelligent algorithm (IA) that is capable of adapting to dynamical tropical weather conditions is proposed based on fuzzy logic techniques. The IA effectively interacts with the quality of service (QoS) criteria irrespective of the dynamic tropical weather to achieve improvement in the satellite links. To achieve this, an adaptive network-based fuzzy inference system (ANFIS) has been adopted. The algorithm is capable of interacting with the weather fluctuation to generate appropriate improvement to the satellite QoS for efficient services to the customers. 5-year (2012-2016) rainfall rate of one-minute integration time series data has been used to derive fading based on ITU-R P. 618-12 propagation models. The data are obtained from the measurement undertaken by the Communication Research Group (CRG), Physics Department, Federal University of Technology, Akure, Nigeria. The rain attenuation and signal-to-noise ratio (SNR) were derived for frequency between Ku and V-band and propagation angle with respect to different transmitting power. The simulated results show a substantial reduction in SNR especially for application in the area of digital video broadcast-second generation coding modulation satellite networks.

Keywords: Fuzzy logic, intelligent algorithm, Nigeria, QoS, satellite applications, tropical weather.

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Authors: E. Kalcheva, A. Taki, Y. Hadi

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High-rises and placemaking is an understudied combination which receives more and more interest with the proliferation of this typology in many British cities. The reason for studying three major cities in England: London, Birmingham and Manchester, is to learn from the latest advances in urban design in well-developed and prominent urban environment. The analysis of several high-rise sites reveals the weaknesses in urban design of contemporary British cities and presents an opportunity to study from the implemented examples. Therefore, the purpose of this research is to analyze design approaches towards creating a sustainable and varied urban environment when high-rises are involved. The research questions raised by the study are: what is the quality of high-rises and their surroundings; what facilities and features are deployed in the research area; what is the role of the high-rise buildings in the placemaking process; what urban design principles are applicable in this context. The methodology utilizes observation of the researched area by structured questions, developed by the author to evaluate the outdoor qualities of the high-rise surroundings. In this context, the paper argues that the quality of the public realm around the high-rises is quite low, missing basic but vital elements such as plazas, public art, and seating, along with landscaping and pocket parks. There is lack of coherence, the rhythm of the streets is often disrupted, and even though the high-rises are very aesthetically appealing, they fail to create a sense of place on their own. The implications of the study are that future planning can take into consideration the critique in this article and provide more opportunities for urban design interventions around high-rise buildings in the British cities.

Keywords: High-rises, placemaking, urban design, townscape.

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Authors: Hiroshi Yamakawa, Michiko Yoshida, Motohiro Tsuchiya

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The recent development of Information and Communication Technology (ICT) enables new ways of "democratic" decision-making such as a page-ranking system, which estimates the importance of a web page based on indirect trust on that page shared by diverse group of unorganized individuals. These kinds of "democracy" have not been acclaimed yet in the world of real politics. On the other hand, a large amount of data about personal relations including trust, norms of reciprocity, and networks of civic engagement has been accumulated in a computer-readable form by computer systems (e.g., social networking systems). We can use these relations as a new type of social capital to construct a new democratic decision-making system based on a delegation network. In this paper, we propose an effective decision-making support system, which is based on empowering someone's vote whom you trust. For this purpose, we propose two new techniques: the first is for estimating entire vote distribution from a small number of votes, and the second is for estimating active voter choice to promote voting using a delegation network. We show that these techniques could increase the voting ratio and credibility of the whole decision by agent-based simulations.

Keywords: Delegation, network centrality, social network, voting ratio.

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Authors: Debolina Chatterjee, Suhita Chopra Chatterjee

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Imprisonment is an expansive concept, as it is regulated by laws under criminal justice system of the state. The state sets principles of punishment to control offenders and also puts limits to excess punitive control. One significant way through which it exercises control is through rules governing healthcare of imprisoned population. Prisons signify specialized settings which accommodate both medical and legal concerns. The provision of care operates within the institutional paradigm of punishment. This requires the state to negotiate adequately between goals of punishment and fulfilment of basic human rights of offenders. The present study is based on a critical analysis of prison healthcare standards in India, which include government policies and guidelines. It also demonstrates how healthcare is delivered by drawing insights from a primary study conducted in a correctional home in the state of West Bengal, India, which houses both male and female inmates. Forty women were interviewed through semi-structured interviews, followed by focus group discussions. Doctors and administrative personnel were also interviewed. Findings show how institutional practices control women through subversion of the role of doctors to prison administration. Also, poor healthcare infrastructure, unavailability of specialized services, hierarchies between personnel and inmates make prisons unlikely sites for therapeutic intervention. The paper further discusses how institutional practices foster gender-based discriminatory practices.

Keywords: Imprisonment, imprisoned women, prison healthcare, prison policies.

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Authors: Ahmed Ebian, Mona Shokair, Kamal Awadalla

Abstract:

Mobile WiMAX is a broadband wireless solution that enables convergence of mobile and fixed broadband networks through a common wide area broadband radio access technology and flexible network architecture. It adopts Orthogonal Frequency Division Multiple Access (OFDMA) for improved multi-path performance in Non-Line-Of-Sight (NLOS) environments. Scalable OFDMA (SOFDMA) is introduced in the IEEE 802e[1]. WIMAX system uses one of different types of channel coding but The mandatory channel coding scheme is based on binary nonrecursive Convolutional Coding (CC). There are other several optional channel coding schemes such as block turbo codes, convolutional turbo codes, and low density parity check (LDPC). In this paper a comparison between the performance of WIMAX using turbo code and using convolutional product code (CPC) [2] is made. Also a combination between them had been done. The CPC gives good results at different SNR values compared to both the turbo system, and the combination between them. For example, at BER equal to 10-2 for 128 subcarriers, the amount of improvement in SNR equals approximately 3 dB higher than turbo code and equals approximately 2dB higher than the combination respectively. Several results are obtained at different modulating schemes (16QAM and 64QAM) and different numbers of sub-carriers (128 and 512).

Keywords: Turbo Code, Convolutional Product Code (CPC), Convolutional Product Code (CPC).

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Authors: Beatriz dos Anjos Furtado, Marina Barreiros Lamim, Camila Avozani Zago, Julianne Alvim Milward-de-Azevedo, Luís Cláudio Meirelles de Medeiros

Abstract:

The intense process of economic acceleration, expansion of industrial activities and capitalism, combined with population growth, while promoting the development, bring environmental consequences and dynamics of locations. It can be seen that society is seeking to break with old paradigms of capitalist society, seeking to reconcile growth with sustainable development, with a change of mentality of the stakeholders of the production process (shareholders, employees, suppliers, customers, governments, and neighbors, groups citizens and the public in general). In this context, this research aims to map the stakeholders interested in environmental management in industries located in the city of Três Rios/RJ. The city of Três Rios is located in South-Central region of the state of Rio de Janeiro - Brazil. Methodological resources used refer to descriptive and field research, whose nature is qualitative and quantitative. It is also of multicases studies in the study area, and the data collection occurred by means of semi-structured questionnaires and interviews with employees related to the environmental area of the industries located in Três Rios and registered at the Federation of Industries the State of Rio de Janeiro - FIRJAN in the version of 2013 and active in federal revenue. Through this research it observed, among other things, the stakeholders involved in the environmental management process of “Três Rios” industry respondents, and those responding to the demands of environmental management.

Keywords: Environmental management, environmental practices, industry, stakeholders.

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Authors: Bassma Aldahlan, Zongming Fei

Abstract:

Recently, there is a great interest in smart parking application. Theses applications are enhanced by a vehicular ad-hoc network, which helps drivers find and reserve satiable packing spaces for a period of time ahead of time. Named Data Networking (NDN) is a future Internet architecture that benefits vehicular ad-hoc networks because of its clean-slate design and pure communication model. In this paper, we proposed an NDN-based frame-work for smart parking that involved a fog computing architecture. The proposed application had two main directions: First, we allowed drivers to query the number of parking spaces in a particular parking lot. Second, we introduced a technique that enabled drivers to make intelligent reservations before their arrival time. We also introduced a “push-based” model supporting the NDN-based framework for smart parking applications. To evaluate the proposed solution’s performance, we analyzed the function for finding parking lots with available parking spaces and the function for reserving a parking space. Our system showed high performance results in terms of response time and push overhead. The proposed reservation application performed better than the baseline approach.

Keywords: Cloud Computing, Vehicular Named Data Networking, Smart Parking Applications, Fog Computing.

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