Search results for: social network ming

Authors: Sharon Q. Yang, Robert J. Congleton

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Information security is a broad concept that covers any issues and concerns about the proper access and use of information on the Internet, including measures and procedures to protect intellectual property and private data from illegal access and online theft; the act of hacking; and any defensive technologies that contest such cybercrimes. As more research and commercial activities are conducted online, cybercrimes have increased significantly, putting sensitive information at risk. Information security has become critically important for organizations and private citizens alike. Hackers scan for network vulnerabilities on the Internet and steal data whenever they can. Cybercrimes disrupt our daily life, cause financial losses, and instigate fear in the public. Since the start of the pandemic, most data related cybercrimes targets have been either financial or health information from companies and organizations. Libraries also should have a high interest in understanding and adopting information security methods to protect their patron data and copyrighted materials. But according to information security professionals, higher education and cultural organizations, including their libraries, are the least prepared entities for cyberattacks. One recent example is that of Steven’s Institute of Technology in New Jersey in the US, which had its network hacked in 2020, with the hackers demanding a ransom. As a result, the network of the college was down for two months, causing serious financial loss. There are other cases where libraries, colleges, and universities have been targeted for data breaches. In order to build an effective defense, we need to understand the most common types of cybercrimes, including phishing, whaling, social engineering, distributed denial of service (DDoS) attacks, malware and ransomware, and hacker profiles. Our research will focus on each hacking technique and related defense measures; and the social background and reasons/purpose of hacker and hacking. Our research shows that hacking techniques will continue to evolve as new applications, housing information, and data on the Internet continue to be developed. Some cybercrimes can be stopped with effective measures, while others present challenges. It is vital that people understand what they face and the consequences when not prepared.

Keywords: cybercrimes, hacking technologies, higher education, information security, libraries

Procedia PDF Downloads 134

Authors: Jie Liu, Jinqu Cheng, Qiyuan Peng, Yong Yin

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Urban rail transit (URT) plays a significant role in dealing with traffic congestion and environmental problems in cities. However, equipment failure and obstruction of links often lead to URT links’ capacities loss in daily operation. It affects the reliability and transport service quality of URT network seriously. In order to measure the influence of links’ capacities loss on reliability and transport service quality of URT network, passengers are divided into three categories in case of links’ capacities loss. Passengers in category 1 are less affected by the loss of links’ capacities. Their travel is reliable since their travel quality is not significantly reduced. Passengers in category 2 are affected by the loss of links’ capacities heavily. Their travel is not reliable since their travel quality is reduced seriously. However, passengers in category 2 still can travel on URT. Passengers in category 3 can not travel on URT because their travel paths’ passenger flow exceeds capacities. Their travel is not reliable. Thus, the proportion of passengers in category 1 whose travel is reliable is defined as reliability indicator of URT network. The transport service quality of URT network is related to passengers’ travel time, passengers’ transfer times and whether seats are available to passengers. The generalized travel cost is a comprehensive reflection of travel time, transfer times and travel comfort. Therefore, passengers’ average generalized travel cost is used as transport service quality indicator of URT network. The impact of links’ capacities loss on transport service quality of URT network is measured with passengers’ relative average generalized travel cost with and without links’ capacities loss. The proportion of the passengers affected by links and betweenness of links are used to determine the important links in URT network. The stochastic user equilibrium distribution model based on the improved logit model is used to determine passengers’ categories and calculate passengers’ generalized travel cost in case of links’ capacities loss, which is solved with method of successive weighted averages algorithm. The reliability and transport service quality indicators of URT network are calculated with the solution result. Taking Wuhan Metro as a case, the reliability and transport service quality of Wuhan metro network is measured with indicators and method proposed in this paper. The result shows that using the proportion of the passengers affected by links can identify important links effectively which have great influence on reliability and transport service quality of URT network; The important links are mostly connected to transfer stations and the passenger flow of important links is high; With the increase of number of failure links and the proportion of capacity loss, the reliability of the network keeps decreasing, the proportion of passengers in category 3 keeps increasing and the proportion of passengers in category 2 increases at first and then decreases; When the number of failure links and the proportion of capacity loss increased to a certain level, the decline of transport service quality is weakened.

Keywords: urban rail transit network, reliability, transport service quality, links’ capacities loss, important links

Procedia PDF Downloads 128

Authors: Gautam Kumar Saharia, Sagardeep Talukdar, Riki Dutta, Sudipta Nandy

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The physics-informed neural network (PINN) method opens up an approach for numerically solving nonlinear partial differential equations leveraging fast calculating speed and high precession of modern computing systems. We construct the PINN based on a strong universal approximation theorem and apply the initial-boundary value data and residual collocation points to weekly impose initial and boundary conditions to the neural network and choose the optimization algorithms adaptive moment estimation (ADAM) and Limited-memory Broyden-Fletcher-Golfard-Shanno (L-BFGS) algorithm to optimize learnable parameter of the neural network. Next, we improve the PINN with a weighted loss function to obtain both the bright and dark soliton solutions of the Fokas-Lenells equation (FLE). We find the proposed scheme of adjustable weight coefficients into PINN has a better convergence rate and generalizability than the basic PINN algorithm. We believe that the PINN approach to solve the partial differential equation appearing in nonlinear optics would be useful in studying various optical phenomena.

Keywords: deep learning, optical soliton, physics informed neural network, partial differential equation

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Authors: Kolawole Akinjide Aramide, Razaq Oyewo

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This study investigates social media utilisation and addiction among students in Nigerian universities. Three hundred and twenty seven (327) students were randomly selected across five selected universities in Nigeria but only 215 provided useful responses for the study. The study revealed regular use of social media for the purpose of communicating and connecting with friends only while Picassa, Twitter, Flickr, Youtube, MySpace, Blogger, Linkedln and LibraryThing were found to top the list of social media being used on regular basis by the students. The level of social media addiction among the students was found to be low. A significant difference was established between undergraduate and postgraduate students’ utilization of social media as the undergraduate students were found to utilise social media more than the postgraduate students. However, no significant difference was found in the level of addiction to social media between the undergraduate and postgraduate students.

Keywords: social media utilisation, social media addiction, Nigerian students, universities

Procedia PDF Downloads 506

Authors: Firas M. Tuaimah

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This paper presents an Artificial Neural Network based approach for short-term load forecasting and exactly for two days ahead. Two seasons have been discussed for Iraqi power system, namely summer and winter; the hourly load demand is the most important input variables for ANN based load forecasting. The recorded daily load profile with a lead time of 1-48 hours for July and December of the year 2012 was obtained from the operation and control center that belongs to the Ministry of Iraqi electricity. The results of the comparison show that the neural network gives a good prediction for the load forecasting and for two days ahead.

Keywords: short-term load forecasting, artificial neural networks, back propagation learning, hourly load demand

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Authors: Evridiki Zachopoulou, Vasilis Grammatikopoulos, Michail Vitoulis, Athanasios Gregoriadis

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In the last ten years, the recent economic crisis in Greece has decreased the financial ability and strength of several families when it comes to supporting their children’s studies. As a result, the number of students who are significantly delaying or even dropping out of their university studies is constantly increasing. The students who are at greater risk for academic failure are those who are facing various problems and social disadvantages, like health problems, special needs, family poverty or unemployment, single-parent students, immigrant students, etc. The ‘Umbrella’ project is a volunteer-based initiative to tackle this problem at International Hellenic University. The main purpose of the project is to provide support to disadvantaged students at a socio-emotional, academic, and practical level in order to help them complete their undergraduate studies. More specifically, the ‘Umbrella’ project has the following goals: (a) to develop a consulting-supporting network based on volunteering senior students, called ‘i-mentors’. (b) to train the volunteering i-mentors and create a systematic and consistent support procedure for students at-risk, (c), to develop a service that, parallel to the i-mentor network will be ensuring opportunities for at-risk students to find a job, (d) to support students who are coping with accessibility difficulties, (e) to secure the sustainability of the ‘Umbrella’ project after the completion of the funding of the project. The innovation of the Umbrella project is in its holistic-person-centered approach that will be providing individualized support -via the i-mentors network- to any disadvantaged student that will come ‘under the Umbrella.’

Keywords: peer mentoring, student support, socially disadvantaged students, volunteerism in higher education

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Authors: Xiaoxiao Li, Shuangcheng Jia, Qian Li

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Keypoint detection has always been a challenge in the field of image recognition. This paper proposes a novelty keypoint detection method which is called Multi-Scale Feature Fusion Convolutional Network with Attention (MFFCNA). We verified that the multi-scale features with the attention mechanism module have better feature expression capability. The feature fusion between different scales makes the information that the network model can express more abundant, and the network is easier to converge. On our self-made street sign corner dataset, we validate the MFFCNA model with an accuracy of 97.8% and a recall of 81%, which are 5 and 8 percentage points higher than the HRNet network, respectively. On the COCO dataset, the AP is 71.9%, and the AR is 75.3%, which are 3 points and 2 points higher than HRNet, respectively. Extensive experiments show that our method has a remarkable improvement in the keypoint recognition tasks, and the recognition effect is better than the existing methods. Moreover, our method can be applied not only to keypoint detection but also to image classification and semantic segmentation with good generality.

Keywords: keypoint detection, feature fusion, attention, semantic segmentation

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Authors: A. Assad, T. Zayed

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Water networks (WNs) are responsible of providing adequate amounts of safe, high quality, water to the public. As other critical infrastructure systems, WNs are subjected to deterioration which increases the number of breaks and leaks and lower water quality. In Canada, 35% of water assets require critical attention and there is a significant gap between the needed and the implemented investments. Thus, the need for efficient rehabilitation programs is becoming more urgent given the paradigm of aging infrastructure and tight budget. The first step towards developing such programs is to formulate a Performance Index that reflects the current condition of water assets along with its criticality. While numerous studies in the literature have focused on various aspects of condition assessment and reliability, limited efforts have investigated the criticality of such components. Critical water mains are those whose failure cause significant economic, environmental or social impacts on a community. Inclusion of criticality in computing the performance index will serve as a prioritizing tool for the optimum allocating of the available resources and budget. In this study, several social, economic, and environmental factors that dictate the criticality of a water pipelines have been elicited from analyzing the literature. Expert opinions were sought to provide pairwise comparisons of the importance of such factors. Subsequently, Fuzzy Logic along with Analytical Network Process (ANP) was utilized to calculate the weights of several criteria factors. Multi Attribute Utility Theories (MAUT) was then employed to integrate the aforementioned weights with the attribute values of several pipelines in Montreal WN. The result is a criticality index, 0-1, that quantifies the severity of the consequence of failure of each pipeline. A novel contribution of this approach is that it accounts for both the interdependency between criteria factors as well as the inherited uncertainties in calculating the criticality. The practical value of the current study is represented by the automated tool, Excel-MATLAB, which can be used by the utility managers and decision makers in planning for future maintenance and rehabilitation activities where high-level efficiency in use of materials and time resources is required.

Keywords: water networks, criticality assessment, asset management, fuzzy analytical network process

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Authors: Mohammad Ehson Rangiha, Bill Karakostas

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Social Business Process Management (SBPM) promises to overcome limitations of traditional BPM by allowing flexible process design and enactment through the involvement of users from a social community. This paper proposes a meta-model and architecture for socially driven business process management systems. It discusses the main facets of the architecture such as goal-based role assignment that combines social recommendations with user profile, and process recommendation, through a real example of a charity organization.

Keywords: business process management, goal-based modelling, process recommendation social collaboration, social BPM

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Authors: Mohammad Izadkhah, Mojtaba Hoseini, Alireza Khalili Tehrani

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In this paper we presented a new method for tracking flying targets in color video sequences based on contour and kernel. The aim of this work is to overcome the problem of losing target in changing light, large displacement, changing speed, and occlusion. The proposed method is made in three steps, estimate the target location by particle filter, segmentation target region using neural network and find the exact contours by greedy snake algorithm. In the proposed method we have used both region and contour information to create target candidate model and this model is dynamically updated during tracking. To avoid the accumulation of errors when updating, target region given to a perceptron neural network to separate the target from background. Then its output used for exact calculation of size and center of the target. Also it is used as the initial contour for the greedy snake algorithm to find the exact target's edge. The proposed algorithm has been tested on a database which contains a lot of challenges such as high speed and agility of aircrafts, background clutter, occlusions, camera movement, and so on. The experimental results show that the use of neural network increases the accuracy of tracking and segmentation.

Keywords: video tracking, particle filter, greedy snake, neural network

Procedia PDF Downloads 342

Authors: Cristian F.T. Montenegro, Luís F. N. Lourenço, Maurício B. C. Salles, Renato M. Monaro

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More PV systems have been connected to the electrical network each year. As the number of PV systems increases, some issues affecting grid operations have been identified. This paper studied the impacts related to changes in solar irradiance on a distribution/sub-transmission network, considering variations due to moving clouds and daily cycles. Using MATLAB/Simulink software, a solar farm of 30 MWp was built and then implemented to a test network. From simulations, it has been determined that irradiance changes can have a significant impact on the grid by causing voltage fluctuations outside the allowable thresholds. This work discussed some local control strategies and grid reinforcements to mitigate the negative effects of the irradiance changes on the grid.

Keywords: reactive power control, solar irradiance, utility-scale PV systems, voltage fluctuations

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Authors: Martins Y. Otache, John J. Musa, Abayomi I. Kuti, Mustapha Mohammed

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The performance of an artificial neural network (ANN) is contingent on a host of factors, for instance, the network optimisation scheme. In view of this, the study examined the general implications of the ANN training optimisation algorithm on its forecast performance. To this end, the Bayesian regularisation (Br), Levenberg-Marquardt (LM), and the adaptive learning gradient descent: GDM (with momentum) algorithms were employed under different ANN structural configurations: (1) single-hidden layer, and (2) double-hidden layer feedforward back propagation network. Results obtained revealed generally that the gradient descent with momentum (GDM) optimisation algorithm, with its adaptive learning capability, used a relatively shorter time in both training and validation phases as compared to the Levenberg- Marquardt (LM) and Bayesian Regularisation (Br) algorithms though learning may not be consummated; i.e., in all instances considering also the prediction of extreme flow conditions for 1-day and 5-day ahead, respectively especially using the ANN model. In specific statistical terms on the average, model performance efficiency using the coefficient of efficiency (CE) statistic were Br: 98%, 94%; LM: 98 %, 95 %, and GDM: 96 %, 96% respectively for training and validation phases. However, on the basis of relative error distribution statistics (MAE, MAPE, and MSRE), GDM performed better than the others overall. Based on the findings, it is imperative to state that the adoption of ANN for real-time forecasting should employ training algorithms that do not have computational overhead like the case of LM that requires the computation of the Hessian matrix, protracted time, and sensitivity to initial conditions; to this end, Br and other forms of the gradient descent with momentum should be adopted considering overall time expenditure and quality of the forecast as well as mitigation of network overfitting. On the whole, it is recommended that evaluation should consider implications of (i) data quality and quantity and (ii) transfer functions on the overall network forecast performance.

Keywords: streamflow, neural network, optimisation, algorithm

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Authors: Edward Guillén, Jeisson Sánchez, Carlos Omar Ramos

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In recent years, a wide variety of applications are developed with Support Vector Machines -SVM- methods and Artificial Neural Networks -ANN-. In general, these methods depend on intrusion knowledge databases such as KDD99, ISCX, and CAIDA among others. New classes of detectors are generated by machine learning techniques, trained and tested over network databases. Thereafter, detectors are employed to detect anomalies in network communication scenarios according to user’s connections behavior. The first detector based on training dataset is deployed in different real-world networks with mobile and non-mobile devices to analyze the performance and accuracy over static detection. The vulnerabilities are based on previous work in telemedicine apps that were developed on the research group. This paper presents the differences on detections results between some network scenarios by applying traditional detectors deployed with artificial neural networks and support vector machines.

Keywords: anomaly detection, back-propagation neural networks, network intrusion detection systems, support vector machines

Procedia PDF Downloads 357

Authors: Lata Shakya, Toshio Otsuki, Saori Imoto, Bijaya Krishna Shrestha, Umesh Bahadur Malla

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2015 Gorkha Nepal earthquake have damaged the human settlements in 14 districts of Nepal. Historic core areas of three principal cities namely Kathmandu, Lalitpur and Bhaktapur including numerous traditional ‘newari’ settlements in the peripheral areas have been either collapsed or severely damaged. Despite Government of Nepal and (international) non-government organisations’ attempt towards disaster risk management through the preparation of policies and guidelines and implementation of community-based activities, the recent ‘Gorkha’ earthquake has demonstrated the inadequate preparedness, poor implementation of a legal instrument, resource constraints, and managerial weakness. However, the social capital through community based institutions, self-help attitude, and community bond has helped a lot not only in rescue and relief operation but also in a post-disaster temporary shelter living thereby exhibiting the resilient power of the local community. Conducting a detailed case study of ‘Kaminani’ community with 42 houses at ward no. 16 of Bhaktapur municipality, this paper analyses the local community’s response and activities on the Gorkha earthquake in rescue and relief operation as well as in post disaster work. Leadership, the existence of internal/external aid, physical and human support are also analyzed. Social resource and networking are also explained through critical review of the existing community organisation and their activities. The research methodology includes literature review, field survey, and interview with community leaders and residents based on a semi-structured questionnaire. The study reveals that community carried their recovery process in four different phases: (i) management of emergency evacuation, (ii) constructing community owed temporary shelter for individuals, (iii) demolishing upper floors of the damaged houses, and (iv) planning for collaborative housing reconstruction. As territorial based organization, religion based agency and aim based institution exist in the survey area from pre-disaster time, it can be assumed that the community activists including leaders are well experienced to create aim-based group and manage teamwork to deal with various issues and problems collaboratively. Physical and human support including partial financial aid from external source as a result of community leader’s personal networking is extended to the community members. Thus, human/social resource and personal/social network play a crucial role in the recovery process. And to build such social capital, community should have potential from pre-disaster time.

Keywords: Gorkha Nepal earthquake, local community, recovery process, social resource, social network

Procedia PDF Downloads 255

Authors: Zhang Shuqi, Liu Dan

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For the problems in intelligent network anomaly traffic detection models, such as low detection accuracy caused by the lack of training samples, poor effect with small sample attack detection, a classification model enhancement method, F-ACGAN(Flow Auxiliary Classifier Generative Adversarial Network) which introduces generative adversarial network and adversarial training, is proposed to solve these problems. Generating adversarial data with category labels could enhance the training effect and improve classification accuracy and model robustness. FACGAN consists of three steps: feature preprocess, which includes data type conversion, dimensionality reduction and normalization, etc.; A generative adversarial network model with feature learning ability is designed, and the sample generation effect of the model is improved through adversarial iterations between generator and discriminator. The adversarial disturbance factor of the gradient direction of the classification model is added to improve the diversity and antagonism of generated data and to promote the model to learn from adversarial classification features. The experiment of constructing a classification model with the UNSW-NB15 dataset shows that with the enhancement of FACGAN on the basic model, the classification accuracy has improved by 8.09%, and the score of F1 has improved by 6.94%.

Keywords: data imbalance, GAN, ACGAN, anomaly detection, adversarial training, data augmentation

Procedia PDF Downloads 104

Authors: Elabed Saud

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This paper introduces the new ability of Artificial Neural Networks (ANNs) in estimating speed and controlling the separately excited DC motor. The neural control scheme consists of two parts. One is the neural estimator which is used to estimate the motor speed. The other is the neural controller which is used to generate a control signal for a converter. These two neutrals are training by Levenberg-Marquardt back-propagation algorithm. ANNs are the standard three layers feed-forward neural network with sigmoid activation functions in the input and hidden layers and purelin in the output layer. Simulation results are presented to demonstrate the effectiveness of this neural and advantage of the control system DC motor with ANNs in comparison with the conventional scheme without ANNs.

Keywords: Artificial Neural Network (ANNs), excited DC motor, convenional controller, speed Controller

Procedia PDF Downloads 726

Authors: Jorge Fernandes Teixeira Filho, André Oliveira Alcantara Fontenele, Érick Aragão Ribeiro

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The constant emergence of new technologies used in automated processes makes it necessary for teachers and traders to apply new technologies in their classes. This paper presents an application of a new technology that will be employed in a didactic plant, which represents an effluent treatment process located in a laboratory of a federal educational institution. At work were studied in the first place, all components to be placed on automation laboratory in order to determine ways to program, parameterize and organize the plant. New technologies that have been implemented to the process are basically an AS-i network and a Profinet network, a SCADA system, which represented a major innovation in the laboratory. The project makes it possible to carry out in the laboratory various practices of industrial networks and SCADA systems.

Keywords: automation, industrial networks, SCADA systems, lab automation

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Authors: Mehdi Montakhabrazlighi, Ercan Balikci

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The neural network (NN) method is applied to alloy development of single crystal Ni-base Superalloys with low density and improved mechanical strength. A set of 1200 dataset which includes chemical composition of the alloys, applied stress and temperature as inputs and density and time to rupture as outputs is used for training and testing the network. Thermodynamic phase diagram modeling of the screened alloys is performed with Thermocalc software to model the equilibrium phases and also microsegregation in solidification processing. The model is first trained by 80% of the data and the 20% rest is used to test it. Comparing the predicted values and the experimental ones showed that a well-trained network is capable of accurately predicting the density and time to rupture strength of the Ni-base superalloys. Modeling results is used to determine the effect of alloying elements, stress, temperature and gamma-prime phase volume fraction on rupture strength of the Ni-base superalloys. This approach is in line with the materials genome initiative and integrated computed materials engineering approaches promoted recently with the aim of reducing the cost and time for development of new alloys for critical aerospace components. This work has been funded by TUBITAK under grant number 112M783.

Keywords: neural network, rupture strength, superalloy, thermocalc

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Authors: Desmond Okorie, Emmanuel Prince

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Availability of portable water has been a global challenge especially to the developing continents/nations such as Africa/Nigeria. The World Health Organization WHO has produced the guideline for drinking water quality GDWQ which aims at ensuring water safety from source to consumer. Portable water parameters test include physical (colour, odour, temperature, turbidity), chemical (PH, dissolved solids) biological (algae, plytoplankton). This paper discusses the use of wireless sensor networks to monitor water quality using efficient and effective sensors that have the ability to sense, process and transmit sensed data. The integration of wireless sensor network to a portable sensing device offers the feasibility of sensing distribution capability, on site data measurements and remote sensing abilities. The current water quality tests that are performed in government water quality institutions in Benue State Nigeria are carried out in problematic locations that require taking manual water samples to the institution laboratory for examination, to automate the entire process based on wireless sensor network, a system was designed. The system consists of sensor node containing one PH sensor, one temperature sensor, a microcontroller, a zigbee radio and a base station composed by a zigbee radio and a PC. Due to the advancement of wireless sensor network technology, unexpected contamination events in water environments can be observed continuously. local area network (LAN) wireless local area network (WLAN) and internet web-based also commonly used as a gateway unit for data communication via local base computer using standard global system for mobile communication (GSM). The improvement made on this development show a water quality monitoring system and prospect for more robust and reliable system in the future.

Keywords: local area network, Ph measurement, wireless sensor network, zigbee

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Authors: Natalia Espinosa, Arthur Amorim, Rudolf Huebner

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Epilepsy is a chronic neural disease and around 50 million people in the world suffer from this disease, however, in many cases, the individual acquires resistance to the medication, which is known as drug-resistant epilepsy, where a detection system is necessary. This paper showed the development of an automatic system for seizure detection based on artificial neural networks (ANN), which are common techniques of machine learning. Discrete Wavelet Transform (DWT) is used for decomposing electroencephalogram (EEG) signal into main brain waves, with these frequency bands is extracted features for training a feedforward neural network with backpropagation, finally made a pattern classification, seizure or non-seizure. Obtaining 95% accuracy in epileptic EEG and 100% in normal EEG.

Keywords: Artificial Neural Network (ANN), Discrete Wavelet Transform (DWT), Epilepsy Detection , Seizure.

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Authors: Alexandre Barbosa de Almeida, Telma Woerle de Lima Soares

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Protein structure prediction is a challenging task in the bioinformatics field. The biological function of all proteins majorly relies on the shape of their three-dimensional conformational structure, but less than 1% of all known proteins in the world have their structure solved. This work proposes a deep learning model to address this problem, attempting to predict some aspects of the protein conformations. Throughout a process of multiobjective dominance, a recurrent neural network was trained to abstract the particular bias of each individual multiobjective algorithm, generating a heuristic that could be useful to predict some of the relevant aspects of the three-dimensional conformation process formation, known as protein folding.

Keywords: Ab initio heuristic modeling, multiobjective optimization, protein structure prediction, recurrent neural network

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Authors: Arshia Ayoub, Zahid Ashraf Wani

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With the introduction of online social Q&A sites, people are able to reach each other efficiently for information seeking and simultaneously creating social bonds. There prevails an issue of low or no response for some questions posed by an information seeker on these sites. So this study tries to understand the effect of question type on responses in Social Q & A sites. The study found that among the answered queries, majority of them were answered within 24 hours of posting the questions and surprisingly most replies were received within one hour of posting. It was observed that questions of general information type were most likely to be answered followed by verification type.

Keywords: community‐based services, information seeking, social search, social Q&A site

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Authors: Latif Adnane, Benaatou Wafa, Pla Vicent

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Handover is of great significance to achieve seamless connectivity in wireless networks. This paper gives an impression of the main factors which are being affected by the soft and the hard handovers techniques. To know and understand the handover process in The Universal Mobile Telecommunications System (UMTS) network, different statistics are calculated. This paper focuses on the quality of service (QoS) of soft and hard handover in UMTS network, which includes the analysis of received power, signal to noise radio, throughput, delay traffic, traffic received, delay, total transmit load, end to end delay and upload response time using OPNET simulator.

Keywords: handover, UMTS, mobility, simulation, OPNET modeler

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Authors: Zhenyu Zhang, Hsi-Hsien Wei

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Highway networks play a vital role in post-disaster recovery for disaster-damaged areas. Damaged bridges in such networks can disrupt the recovery activities by impeding the transportation of people, cargo, and reconstruction resources. Therefore, rapid restoration of damaged bridges is of paramount importance to long-term disaster recovery. In the post-disaster recovery phase, the key to restoration scheduling for a highway network is prioritization of bridge-repair tasks. Resilience is widely used as a measure of the ability to recover with which a network can return to its pre-disaster level of functionality. In practice, highways will be temporarily blocked during the downtime of bridge restoration, leading to the decrease of highway-network functionality. The failure to take downtime effects into account can lead to overestimation of network resilience. Additionally, post-disaster recovery of highway networks is generally divided into emergency bridge repair (EBR) in the response phase and long-term bridge repair (LBR) in the recovery phase, and both of EBR and LBR are different in terms of restoration objectives, restoration duration, budget, etc. Distinguish these two phases are important to precisely quantify highway network resilience and generate suitable restoration schedules for highway networks in the recovery phase. To address the above issues, this study proposes a novel resilience quantification method for the optimization of long-term bridge repair schedules (LBRS) taking into account the impact of EBR activities and restoration downtime on a highway network’s functionality. A time-dependent integer program with recursive functions is formulated for optimally scheduling LBR activities. Moreover, since uncertainty always exists in the LBRS problem, this paper extends the optimization model from the deterministic case to the stochastic case. A hybrid genetic algorithm that integrates a heuristic approach into a traditional genetic algorithm to accelerate the evolution process is developed. The proposed methods are tested using data from the 2008 Wenchuan earthquake, based on a regional highway network in Sichuan, China, consisting of 168 highway bridges on 36 highways connecting 25 cities/towns. The results show that, in this case, neglecting the bridge restoration downtime can lead to approximately 15% overestimation of highway network resilience. Moreover, accounting for the impact of EBR on network functionality can help to generate a more specific and reasonable LBRS. The theoretical and practical values are as follows. First, the proposed network recovery curve contributes to comprehensive quantification of highway network resilience by accounting for the impact of both restoration downtime and EBR activities on the recovery curves. Moreover, this study can improve the highway network resilience from the organizational dimension by providing bridge managers with optimal LBR strategies.

Keywords: disaster management, highway network, long-term bridge repair schedule, resilience, restoration downtime

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Authors: A. Majidian

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The life prediction of thermal power plant components is necessary to prevent the unexpected outages, optimize maintenance tasks in periodic overhauls and plan inspection tasks with their schedules. One of the main critical components in a power plant is condenser because its failure can affect many other components which are positioned in downstream of condenser. This paper deals with factors affecting life of condenser. Failure rates dependency vs. these factors has been investigated using Artificial Neural Network (ANN) and fuzzy logic algorithms. These algorithms have shown their capabilities as dynamic tools to evaluate life prediction of power plant equipments.

Keywords: life prediction, condenser tube, neural network, fuzzy logic

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Authors: Awatif Habeeb Al-Shamare

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The number of social media users amongst special education teachers and parents of children with Down Syndrome (DS) is increasing annually. This is also the case in the Kingdom of Saudi Arabia (KSA). However, according to the best of the author’s knowledge, there are no qualitative studies which testify to the true nature of the interaction between teachers and parents when using social media, nor the role of social media in supporting and assisting parents and teachers with regards to the children’s educational needs in KSA. Therefore, this ongoing study aims to identify the role of social media in supporting parents and teachers of DS students, with a special emphasis on early intervention services in KSA. By bridging the knowledge gap on social media and special education in KSA and presenting socially relevant and applied information on the topic, this research provides a theoretical and practical base for the establishment of appropriate and effective programmes between the ministries of Information and Special Education in particular. A qualitative approach was selected because it was the most suitable approach for exploring the participants’ experiences, which could not be determined through scientific tests. Interviewing, chosen as the research instrument, allowed the researcher to obtain a detailed understanding of the topic linked to the study objectives. Initially, a pilot study was conducted at the Daycare Center in May 2016. Its aim was to examine and refine the methodology and assess whether the questions were understood with the potential for re-drafting them, if necessary. The main study consists of five teachers and five mothers with experience of using social media and with links to the Daycare Center. Thematic Analysis has been chosen for analysing the findings because it is a flexible method that allows themes to emerge from the data. Results of the current study are still in the initial stages, but the preliminary findings are as follows: (1) social media is an important tool in encouraging parents and teachers to access the necessary information and knowledge about, and experience in, early intervention services; (2) it acts as a support network for the parents; (3) it helps raise awareness about DS and the need for early intervention; (4) it can be used to put pressure on the government for an expansion in early intervention services, and finally (5) its use can be problematic in that parents and teachers face some difficulties and challenges when using the different platforms. It can be concluded that social media plays a significant role in the lives of teachers and parents with special needs children in KSA.

Keywords: down syndrome, early intervention services, social media, support parents and teachers

Procedia PDF Downloads 146

Authors: Chuan-Ming Tung, Tzu-Chiao Yuan

Abstract:

This research aims to construct the supply indicators and weights of shelter space from a perspective of the needs of the elderly by virtue of literature review, a systematical compilation of related regulations, and the use of the Analytical Hierarchy Process method, the questionnaires regarding the indicators filled out by 16 experts and scholars. The researcher then used 3 schools and 2 activity centers in Banqiao District, New Taipei City, as study cases to evaluate the ‘friendliness’ degree/level for the supply of shelters meeting the needs of elderly people. The supply evaluation indicators of friendly shelters meeting the needs of the elderly include "Administrative Operations and Service Needs" and "Residence-related and Living Needs"; under the "Administrative Operations and Service Needs" are "Management Operations and Information Provision", "Shelter Space Preparedness and Logistics Support", "Medical Care and Social Support", and "Shelters and Medical Environment", a total of 17 assessment items in four indicators, while under the "Residence-related and Living Needs" are "Dietary Needs", "Sleep Needs", "Hygiene and Sanitation Needs", "Accessibility and Convenience Needs ", etc., a total of 18 assessment items in four indicators. The results show that "Residence-related and Living Needs" is the most important item in the main levels of the supply indicators of the needs for friendly shelters to elderly people (weigh value 0.5504), followed by "Administrative Operations and Service Needs" (0.4496). The order of importance of the supply indicators of friendly shelters for the needs of elderly people is as follows: "Hygiene and Sanitation Needs" (0.1721), "Dietary Needs" (0.1340), "Medical Care and Social Support" (0.1300), "Sleep Needs" (0.1277), "Accessibility and Convenience Needs" (0.1166), "Basic Environment of Shelters" (0.1145), "Shelter Space Preparedness and Logistics Support" (0.1115) and "Management Operations and Information Provision" (0.0936). In addition, it can be noticed from the results of the case evaluation that the provision of refuges and shelters, mainly from schools and activity centers, is extremely inadequate for the needs of the elderly. In a set of comprehensive comparisons and contrasts, the evaluation indicators of refuges and shelters that need to be improved are "Medical Care and Social Support", "Hygiene and Sanitation Needs", "Sleep Needs", "Dietary Needs", and "Shelter Space Preparedness and Logistics Support".

Keywords: needs of the elderly people, urban shelters, evaluation indicators/indices., taiwan

Procedia PDF Downloads 80

Authors: Asmir Gogic, Aljo Mujcic, Sandra Ibric, Nermin Suljanovic

Abstract:

Ubiquity of natural disasters during last few decades have risen serious questions towards the prediction of such events and human safety. Every disaster regardless its proportion has a precursor which is manifested as a disruption of some environmental parameter such as temperature, humidity, pressure, vibrations and etc. In order to anticipate and monitor those changes, in this paper we propose an overall system for disaster prediction and monitoring, based on wireless sensor network (WSN). Furthermore, we introduce a modified and simplified WSN routing protocol built on the top of the trickle routing algorithm. Routing algorithm was deployed using the bluetooth low energy protocol in order to achieve low power consumption. Performance of the WSN network was analyzed using a real life system implementation. Estimates of the WSN parameters such as battery life time, network size and packet delay are determined. Based on the performance of the WSN network, proposed system can be utilized for disaster monitoring and prediction due to its low power profile and mesh routing feature.

Keywords: bluetooth low energy, disaster prediction, mesh routing protocols, wireless sensor networks

Procedia PDF Downloads 385

Authors: Tolga Yazıcı

Abstract:

As a matter of the fact that online social networks like Twitter, Facebook and MySpace have experienced an extensive growth in recent years. Social media offers individuals with a tool for communicating and interacting with one another. These social networks enable people to stay in touch with other people and express themselves. This process makes the users of online social networks active creators of content rather than being only consumers of traditional media. That’s why millions of people show strong desire to learn the methods and tools of digital content production and necessary communication skills. However, the booming interest in communication and interaction through online social networks and high level of eagerness to invent and implement the ways to participate in content production raise some privacy and security concerns. This presentation aims to open the assumed revolutionary, democratic and liberating nature of the online social media up for discussion by reviewing some recent political developments in Turkey. Firstly, the role of Internet and online social networks in mobilizing collective movements through social interactions and communications will be questioned. Secondly, some cases from Gezi and Okmeydanı Protests and also December 17-25 period will be presented in order to illustrate misinformation and manipulation in social media and violation of individual privacy through online social networks in order to damage social unity and stability contradictory to democratic nature of online social networking.

Keywords: online social media networks, democratic participation, social movements, social polarization, privacy of individuals, Turkey

Procedia PDF Downloads 341

Authors: Pankhuri Dagaonkar, Charumani Singh, Poornima Krothapalli, Krishna Karthik

Abstract:

The backbone of any retail e-commerce success story is a unique design of supply chain network, providing the business an unparalleled speed and scalability. Primary goal of the supply chain strategy is to meet customer expectation by offering fastest deliveries while keeping the cost minimal. Meeting this objective at the large market that India provides is the problem statement that we have targeted here. There are many models and optimization techniques focused on network design to identify the ideal facility location and size, optimizing cost and speed. In this paper we are presenting a tactical approach to optimize cost of an existing network for a predefined speed. We have considered both forward and reverse logistics of a retail e-commerce supply chain consisting of multiple fulfillment (warehouse) and delivery centers, which are connected via sortation nodes. The mathematical model presented here determines if the shipment from a node should get sorted directly for the last mile delivery center or it should travel as consolidated package to another node for further sortation (resort). The objective function minimizes the total cost by varying the resort percentages between nodes and provides the optimal resource allocation and number of sorts at each node.

Keywords: distribution strategy, mathematical model, network design, supply chain management

Procedia PDF Downloads 297