Search results for: miRNA:mRNA interaction network

Authors: Sourav Ray, Christoph Stein, Marcus Weber

Abstract:

A new class of drug candidates, initially derived from mathematical modeling of ligand-receptor interactions, activate the μ-opioid receptor (MOR) preferentially at acidic extracellular pH-levels, as present in injured tissues. This is of commercial interest because it may preclude the adverse effects of conventional MOR agonists like fentanyl, which include but are not limited to addiction, constipation, sedation, and apnea. Animal studies indicate the importance of taking the pH value of the chemical environment of MOR into account when designing new drugs. Hydrogen bonds (HBs) play a crucial role in stabilizing protein secondary structure and molecular interaction, such as ligand-protein interaction. These bonds may depend on the pH value of the chemical environment. For the MOR, antagonist naloxone and agonist [D-Ala2,N-Me-Phe4,Gly5-ol]-enkephalin (DAMGO) form HBs with ionizable residue HIS 297 at physiological pH to modulate signaling. However, such interactions were markedly reduced at acidic pH. Although fentanyl-induced signaling is also diminished at acidic pH, HBs with HIS 297 residue are not observed at either acidic or physiological pH for this strong agonist of the MOR. Molecular dynamics (MD) simulations can provide greater insight into the interaction between the ligand of interest and the HIS 297 residue. Amino acid protonation states are adjusted to the model difference in system acidity. Unbiased and unrestrained MD simulations were performed, with the ligand in the proximity of the HIS 297 residue. Ligand-receptor complexes were embedded in 1-palmitoyl-2-oleoyl-sn glycero-3-phosphatidylcholine (POPC) bilayer to mimic the membrane environment. The occurrence of HBs between the different ligands and the HIS 297 residue of MOR at acidic and physiological pH values were tracked across the various simulation trajectories. No HB formation was observed between fentanyl and HIS 297 residue at either acidic or physiological pH. Naloxone formed some HBs with HIS 297 at pH 5, but no such HBs were noted at pH 7. Interestingly, DAMGO displayed an opposite yet more pronounced HB formation trend compared to naloxone. Whereas a marginal number of HBs could be observed at even pH 5, HBs with HIS 297 were more stable and widely present at pH 7. The HB formation plays no and marginal role in the interaction of fentanyl and naloxone, respectively, with the HIS 297 residue of MOR. However, HBs play a significant role in the DAMGO and HIS 297 interaction. Post DAMGO administration, these HBs might be crucial for the remediation of opioid tolerance and restoration of opioid sensitivity. Although experimental studies concur with our observations regarding the influence of HB formation on the fentanyl and DAMGO interaction with HIS 297, the same could not be conclusively stated for naloxone. Therefore, some other supplementary interactions might be responsible for the modulation of the MOR activity by naloxone binding at pH 7 but not at pH 5. Further elucidation of the mechanism of naloxone action on the MOR could assist in the formulation of cost-effective naloxone-based treatment of opioid overdose or opioid-induced side effects.

Keywords: effect of system acidity, hydrogen bond formation, opioid action, receptor activation

Procedia PDF Downloads 173

Authors: Ismail Abubakar, Hamid Mehrabi, Reg Morton

Abstract:

Currently, extensive signal analysis is performed in order to evaluate structural health of turbomachinery blades. This approach is affected by constraints of time and the availability of qualified personnel. Thus, new approaches to blade dynamics identification that provide faster and more accurate results are sought after. Generally, modal analysis is employed in acquiring dynamic properties of a vibrating turbomachinery blade and is widely adopted in condition monitoring of blades. The analysis provides useful information on the different modes of vibration and natural frequencies by exploring different shapes that can be taken up during vibration since all mode shapes have their corresponding natural frequencies. Experimental modal testing and finite element analysis are the traditional methods used to evaluate mode shapes with limited application to real live scenario to facilitate a robust condition monitoring scheme. For a real time mode shape evaluation, rapid evaluation and low computational cost is required and traditional techniques are unsuitable. In this study, artificial neural network is developed to evaluate the mode shape of a lab scale rotating blade assembly by using result from finite element modal analysis as training data. The network performance evaluation shows that artificial neural network (ANN) is capable of mapping the correlation between natural frequencies and mode shapes. This is achieved without the need of extensive signal analysis. The approach offers advantage from the perspective that the network is able to classify mode shapes and can be employed in real time including simplicity in implementation and accuracy of the prediction. The work paves the way for further development of robust condition monitoring system that incorporates real time mode shape evaluation.

Keywords: modal analysis, artificial neural network, mode shape, natural frequencies, pattern recognition

Procedia PDF Downloads 153

Authors: Oladapo Tolulope Ibitoye

Abstract:

Neural networks have become prominent and widely engaged in algorithmic-based machine learning networks. They are perfect in solving day-to-day issues to a certain extent. Neural networks are computing systems with several interconnected nodes. One of the numerous areas of application of neural networks is object detection. This is a prominent area due to the coronavirus disease pandemic and the post-pandemic phases. Wearing a face mask in public slows the spread of the virus, according to experts’ submission. This calls for the development of a reliable and effective model for detecting face masks on people's faces during compliance checks. The existing neural network models for facemask detection are characterized by their black-box nature and large dataset requirement. The highlighted challenges have compromised the performance of the existing models. The proposed model utilized Faster R-CNN Model on Inception V3 backbone to reduce system complexity and dataset requirement. The model was trained and validated with very few datasets and evaluation results shows an overall accuracy of 96% regardless of skin tone.

Keywords: convolutional neural network, face detection, face mask, masked faces

Procedia PDF Downloads 62

Authors: Jekaterina Novikova, Leon Watts

Abstract:

In the emerging world of human-robot interaction, people and robots will interact socially in real-world situations. This paper presents the results of an experimental study probing the interaction between situational context and emotional body language in robots. 34 people rated video clips of robots performing expressive behaviours in different situational contexts both for emotional expressivity on Valence-Arousal-Dominance dimensions and by selecting a specific emotional term from a list of suggestions. Results showed that a contextual information enhanced a recognition of emotional body language of a robot, although it did not override emotional signals provided by robot expressions. Results are discussed in terms of design guidelines on how an emotional body language of a robot can be used by roboticists developing social robots.

Keywords: social robotics, non-verbal communication, situational context, artificial emotions, body language

Procedia PDF Downloads 284

Authors: E. M. Hassan, A. L. Kalamkarov

Abstract:

Two micromechanical models for 3D smart composite with embedded periodic or nearly periodic network of generally orthotropic reinforcements and actuators are developed and applied to cubic structures with unidirectional orientation of constituents. Analytical formulas for the effective piezothermoelastic coefficients are derived using the Asymptotic Homogenization Method (AHM). Finite Element Analysis (FEA) is subsequently developed and used to examine the aforementioned periodic 3D network reinforced smart structures. The deformation responses from the FE simulations are used to extract effective coefficients. The results from both techniques are compared. This work considers piezoelectric materials that respond linearly to changes in electric field, electric displacement, mechanical stress and strain and thermal effects. This combination of electric fields and thermo-mechanical response in smart composite structures is characterized by piezoelectric and thermal expansion coefficients. The problem is represented by unit-cell and the models are developed using the AHM and the FEA to determine the effective piezoelectric and thermal expansion coefficients. Each unit cell contains a number of orthotropic inclusions in the form of structural reinforcements and actuators. Using matrix representation of the coupled response of the unit cell, the effective piezoelectric and thermal expansion coefficients are calculated and compared with results of the asymptotic homogenization method. A very good agreement is shown between these two approaches.

Keywords: asymptotic homogenization method, finite element analysis, effective piezothermoelastic coefficients, 3D smart network composite structures

Procedia PDF Downloads 398

Authors: Meiling Yu, Nadia Rouatbi, Khuloud T. Al-Jamal

Abstract:

Background: Treatment of neurological disorders with modern medical and surgical approaches remains difficult. Gene therapy, allowing the delivery of genetic materials that encodes potential therapeutic molecules, represents an attractive option. The treatment of brain diseases with gene therapy requires the gene-editing tool to be delivered efficiently to the central nervous system. In this study, we explored the efficiency of different delivery routes, namely intravenous (i.v.), intra-cranial (i.c.), and intra-nasal (i.n.), to deliver stable nucleic acid-lipid particles (SNALPs) containing gene-editing tools namely Cas9 mRNA and sgRNA encoding for GFP as a reporter protein. We hypothesise that SNALPs can reach the brain and perform gene-editing to different extents depending on the administration route. Intranasal administration (i.n.) offers an attractive and non-invasive way to access the brain circumventing the blood–brain barrier. Successful delivery of gene-editing tools to the brain offers a great opportunity for therapeutic target validation and nucleic acids therapeutics delivery to improve treatment options for a range of neurodegenerative diseases. In this study, we utilised Rosa26-Cas9 knock-in mice, expressing GFP, to study brain distribution and gene-editing efficiency of SNALPs after i.v.; i.c. and i.n. routes of administration. Methods: Single guide RNA (sgRNA) against GFP has been designed and validated by in vitro nuclease assay. SNALPs were formulated and characterised using dynamic light scattering. The encapsulation efficiency of nucleic acids (NA) was measured by RiboGreen™ assay. SNALPs were incubated in serum to assess their ability to protect NA from degradation. Rosa26-Cas9 knock-in mice were i.v., i.n., or i.c. administered with SNALPs to test in vivo gene-editing (GFP knockout) efficiency. SNALPs were given as three doses of 0.64 mg/kg sgGFP following i.v. and i.n. or a single dose of 0.25 mg/kg sgGFP following i.c.. knockout efficiency was assessed after seven days using Sanger Sequencing and Inference of CRISPR Edits (ICE) analysis. In vivo, the biodistribution of DiR labelled SNALPs (SNALPs-DiR) was assessed at 24h post-administration using IVIS Lumina Series III. Results: Serum-stable SNALPs produced were 130-140 nm in diameter with ~90% nucleic acid loading efficiency. SNALPs could reach and stay in the brain for up to 24h following i.v.; i.n. and i.c. administration. Decreasing GFP expression (around 50% after i.v. and i.c. and 20% following i.n.) was confirmed by optical imaging. Despite the small number of mice used, ICE analysis confirmed GFP knockout in mice brains. Additional studies are currently taking place to increase mice numbers. Conclusion: Results confirmed efficient gene knockout achieved by SNALPs in Rosa26-Cas9 knock-in mice expressing GFP following different routes of administrations in the following order i.v.= i.c.> i.n. Each of the administration routes has its pros and cons. The next stages of the project involve assessing gene-editing efficiency in wild-type mice and replacing GFP as a model target with therapeutic target genes implicated in Motor Neuron Disease pathology.

Keywords: CRISPR, nanoparticles, brain diseases, administration routes

Procedia PDF Downloads 96

Authors: Beier Zhu, Rui Zhang, Qi Song

Abstract:

Text contained within fixed-layout documents can be of great semantic value and so requires a high localization accuracy, such as ID cards, invoices, cheques, and passports. Recently, algorithms based on deep convolutional networks achieve high performance on text detection tasks. However, for text localization in fixed-layout documents, such algorithms detect word bounding boxes individually, which ignores the layout information. This paper presents a novel architecture built on convolutional neural networks (CNNs). A global text localization network and a regional bounding-box regression network are introduced to tackle the problem in a coarse-to-fine manner. The text localization network simultaneously locates word bounding points, which takes the layout information into account. The bounding-box regression network inputs the features pooled from arbitrarily sized RoIs and refine the localizations. These two networks share their convolutional features and are trained jointly. A typical type of fixed-layout documents: ID cards, is selected to evaluate the effectiveness of the proposed system. These networks are trained on data cropped from nature scene images, and synthetic data produced by a synthetic text generation engine. Experiments show that our approach locates high accuracy word bounding boxes and achieves state-of-the-art performance.

Keywords: bounding box regression, convolutional networks, fixed-layout documents, text localization

Procedia PDF Downloads 190

Authors: Alpesh Adeshara, Rajendrasinh Jadeja, Praghnesh Bhatt

Abstract:

Implementation of advanced technologies requires sophisticated instruments that deal with the operation, control, restoration and protection of rapidly growing power system network under normal and abnormal conditions. Presently, the applications of Phasor Measurement Unit (PMU) are widely found in real time operation, monitoring, controlling and analysis of power system network as it eliminates the various limitations of Supervisory Control and Data Acquisition System (SCADA) conventionally used in power system. The use of PMU data is very rapidly increasing its importance for online and offline analysis. Wide Area Measurement System (WAMS) is developed as new technology by use of multiple PMUs in power system. The present paper proposes a model of MATLAB based PMU using Discrete Fourier Transform (DFT) algorithm and evaluation of its operation under different contingencies. In this paper, PMU based two bus system having WAMS network is presented as a case study.

Keywords: GPS global positioning system, PMU phasor measurement system, WAMS wide area monitoring system, DFT, PDC

Procedia PDF Downloads 491

Authors: Hamd Rezaeifar, Hamid Reza Sahriari

Abstract:

Accidents are considered to be one of the challenges of modern life. Due to the fact that the victims of this problem and also internal transportations are getting increased day by day in Iran, studying effective factors of accidents and identifying suitable models and parameters about this issue are absolutely essential. The main purpose of this research has been studying the factors and spatial data affecting accidents of Mashhad during 2007- 2008. In this paper it has been attempted to – through matching spatial layers on each other and finally by elaborating them with the place of accident – at the first step by adding landmarks of the accident and through adding especial fields regarding the existence or non-existence of effective phenomenon on accident, existing information banks of the accidents be completed and in the next step by means of data mining tools and analyzing by neural network, the relationship between these data be evaluated and a logical model be designed for predicting accident-prone spots with minimum error. The model of this article has a very accurate prediction in low-accident spots; yet it has more errors in accident-prone regions due to lack of primary data.

Keywords: accident, data mining, neural network, GIS

Procedia PDF Downloads 44

Authors: Mala Nath, Nagamani Kompelli, Partha Roy, Snehasish Das

Abstract:

Two new metal-based anticancer chemotherapeutic agents, [(Ph2Sn)2(HGuO)2(phen)Cl2] 1 and [(Ph3Sn)(HGuO)(phen)]- Cl.CH3OH.H2O 2, were designed, prepared and characterized by analytical and spectral (IR, ESI-Mass, 1H, 13C and 119Sn NMR) techniques. The proposed geometry of Sn(IV) in 1 and 2 is distorted octahedral and distorted trigonal-bipyramidal, respectively. Both 1 and 2 exhibit potential cytotoxicity in vitro against MCF-7, HepG-2 and DU-145 cell lines. The intrinsic binding constant (Kb) values of 1 (2.33 × 105 M-1) and 2 (2.46 × 105 M-1) evaluated from UV-Visible absorption studies suggest non-classical electrostatic mode of interaction via phosphate backbone of DNA double helix. The Stern-Volmer quenching constant (Ksv) of 1 (9.74 × 105 M-1) and 2 (2.9 × 106 M-1) determined by fluorescence studies suggests the groove binding and intercalation mode for 1 and 2, respectively. Effective cleavage of pBR322 DNA is induced by 1. Their interaction with DNA of cancer cells may account for potency.

Keywords: anticancer agents, DNA binding studies, NMR spectroscopy, organotin

Procedia PDF Downloads 253

Authors: S. Nickolas, Shobha K.

Abstract:

The treatment of incomplete data is an important step in the data pre-processing. Missing values creates a noisy environment in all applications and it is an unavoidable problem in big data management and analysis. Numerous techniques likes discarding rows with missing values, mean imputation, expectation maximization, neural networks with evolutionary algorithms or optimized techniques and hot deck imputation have been introduced by researchers for handling missing data. Among these, imputation techniques plays a positive role in filling missing values when it is necessary to use all records in the data and not to discard records with missing values. In this paper we propose a novel artificial neural network based clustering algorithm, Adaptive Resonance Theory-2(ART2) for imputation of missing values in mixed attribute data sets. The process of ART2 can recognize learned models fast and be adapted to new objects rapidly. It carries out model-based clustering by using competitive learning and self-steady mechanism in dynamic environment without supervision. The proposed approach not only imputes the missing values but also provides information about handling the outliers.

Keywords: ART2, data imputation, clustering, missing data, neural network, pre-processing

Procedia PDF Downloads 272

Authors: Yaser Miaji, Mohammed Aloryani

Abstract:

The rise of recent applications in everyday implementation like videoconferencing, online recreation and voice speech communication leads to pressing the need for novel mechanism and policy to serve this steep improvement within the application itself and users‟ wants. This diversity in web traffics needs some classification and prioritization of the traffics since some traffics merit abundant attention with less delay and loss, than others. This research is intended to reinforce the mechanism by analysing the performance in application according to the proposed mechanism implemented. The mechanism used is quite direct and analytical. The mechanism is implemented by modifying the queue limit in the algorithm.

Keywords: traffic classification, IPv6, internet, application categorization

Procedia PDF Downloads 559

Authors: Ji-yang Yu, Dan Huang, Guo-ping Feng, Xin Li, Lu-yuan Wang

Abstract:

The navigation satellite network, especially the Beidou MEO Constellation, can relay data effectively with wide coverage and is applied in navigation, detection, and position widely. But the constellation has not been completed, and the amount of satellites on-board is not enough to cover the earth, which makes the data-relay disrupted or delayed in the transition process. The data-relay function needs to tolerant the delay or disruption in some extension, which make the Beidou MEO Constellation a delay/disruption-tolerant network (DTN). The traditional DTN designs mainly employ the relay table as the basic of data path schedule computing. But in practical application, especially in critical condition, such as the war-time or the infliction heavy losses on the constellation, parts of the nodes may become invalid, then the traditional DTN design could be useless. Furthermore, when transmitting the critical message in the navigation system, the maximum priority strategy is used, but the nodes still inquiry the relay table to design the path, which makes the delay more than minutes. Under this circumstances, it needs a function which could compute the optimum data path on-board in real-time according to the constellation states. The on-board critical message transmission design for navigation satellite delay/disruption-tolerant network (DTN) is proposed, according to the characteristics of navigation satellite network. With the real-time computation of parameters in the network link, the least-delay transition path is deduced to retransmit the critical message in urgent conditions. First, the DTN model for constellation is established based on the time-varying matrix (TVM) instead of the time-varying graph (TVG); then, the least transition delay data path is deduced with the parameters of the current node; at last, the critical message transits to the next best node. For the on-board real-time computing, the time delay and misjudges of constellation states in ground stations are eliminated, and the residual information channel for each node can be used flexibly. Compare with the minute’s delay of traditional DTN; the proposed transmits the critical message in seconds, which improves the re-transition efficiency. The hardware is implemented in FPGA based on the proposed model, and the tests prove the validity.

Keywords: critical message, DTN, navigation satellite, on-board, real-time

Procedia PDF Downloads 338

Authors: Zyad Elkhadir, Khalid Chougdali, Mohammed Benattou

Abstract:

Most of the existing intrusion detection systems works on quantitative network traffic data with many irrelevant and redundant features, which makes detection process more time’s consuming and inaccurate. A several feature extraction methods, such as linear discriminant analysis (LDA), have been proposed. However, LDA suffers from the small sample size (SSS) problem which occurs when the number of the training samples is small compared with the samples dimension. Hence, classical LDA cannot be applied directly for high dimensional data such as network traffic data. In this paper, we propose two solutions to solve SSS problem for LDA and apply them to a network IDS. The first method, reduce the original dimension data using principal component analysis (PCA) and then apply LDA. In the second solution, we propose to use the pseudo inverse to avoid singularity of within-class scatter matrix due to SSS problem. After that, the KNN algorithm is used for classification process. We have chosen two known datasets KDDcup99 and NSLKDD for testing the proposed approaches. Results showed that the classification accuracy of (PCA+LDA) method outperforms clearly the pseudo inverse LDA method when we have large training data.

Keywords: LDA, Pseudoinverse, PCA, IDS, NSL-KDD, KDDcup99

Procedia PDF Downloads 223

Authors: S. Neelima, P. S. Subramanyam

Abstract:

The major tool for distribution planning is load forecasting, which is the anticipation of the load in advance. Artificial neural networks have found wide applications in load forecasting to obtain an efficient strategy for planning and management. In this paper, the application of neural networks to study the design of short term load forecasting (STLF) Systems was explored. Our work presents a pragmatic methodology for short term load forecasting (STLF) using proposed two-stage model of wavelet transform (WT) and artificial neural network (ANN). It is a two-stage prediction system which involves wavelet decomposition of input data at the first stage and the decomposed data with another input is trained using a separate neural network to forecast the load. The forecasted load is obtained by reconstruction of the decomposed data. The hybrid model has been trained and validated using load data from Telangana State Electricity Board.

Keywords: electrical distribution systems, wavelet transform (WT), short term load forecasting (STLF), artificial neural network (ANN)

Procedia PDF Downloads 433

Authors: Redouane Tlemsani, Abdelkader Benyettou

Abstract:

Work is in on line Arabic character recognition and the principal motivation is to study the Arab manuscript with on line technology. This system is a Markovian system, which one can see as like a Dynamic Bayesian Network (DBN). One of the major interests of these systems resides in the complete models training (topology and parameters) starting from training data. Our approach is based on the dynamic Bayesian Networks formalism. The DBNs theory is a Bayesians networks generalization to the dynamic processes. Among our objective, amounts finding better parameters, which represent the links (dependences) between dynamic network variables. In applications in pattern recognition, one will carry out the fixing of the structure, which obliges us to admit some strong assumptions (for example independence between some variables). Our application will relate to the Arabic isolated characters on line recognition using our laboratory database: NOUN. A neural tester proposed for DBN external optimization. The DBN scores and DBN mixed are respectively 70.24% and 62.50%, which lets predict their further development; other approaches taking account time were considered and implemented until obtaining a significant recognition rate 94.79%.

Keywords: Arabic on line character recognition, dynamic Bayesian network, pattern recognition, computer vision

Procedia PDF Downloads 425

Authors: Sima Aznavi, Poria Fajri, Hanif Livani

Abstract:

Smart homes are small energy systems which may be equipped with renewable energy sources, storage devices, and loads. Energy management strategy plays a main role in the efficient operation of smart homes. Effective energy scheduling of the renewable energy sources and storage devices guarantees efficient energy management in households while reducing the energy imports from the grid. Nevertheless, despite such strategies, independently day ahead energy schedules for multiple households can cause undesired effects such as high power exchange with the grid at certain times of the day. Therefore, the interactions between multiple smart home day ahead energy projections is a challenging issue in a smart grid system and if not managed appropriately, the imported energy from the power network can impose additional burden on the distribution grid. In this paper, a central energy management strategy for a network consisting of multiple households each equipped with renewable energy sources, storage devices, and Plug-in Electric Vehicles (PEV) is proposed. The decision-making strategy alongside the smart home energy management system, minimizes the energy purchase cost of the end users, while at the same time reducing the stress on the utility grid. In this approach, the smart home energy management system determines different operating scenarios based on the forecasted household daily load and the components connected to the household with the objective of minimizing the end user overall cost. Then, selected projections for each household that are within the same cost range are sent to the central decision-making system. The central controller then organizes the schedules to reduce the overall peak to average ratio of the total imported energy from the grid. To validate this approach simulations are carried out for a network of five smart homes with different load requirements and the results confirm that by applying the proposed central energy management strategy, the overall power demand from the grid can be significantly flattened. This is an effective approach to alleviate the stress on the network by distributing its energy to a network of multiple households over a 24- hour period.

Keywords: energy management, renewable energy sources, smart grid, smart home

Procedia PDF Downloads 242

Authors: Oktay Nusratov, Ramin Rzaev, Aydin Goyushov

Abstract:

Fuzzy inference method based approach to the forming of modular intellectual system of assessment the quality of communication services is proposed. Developed under this approach the basic fuzzy estimation model takes into account the recommendations of the International Telecommunication Union in respect of the operation of packet switching networks based on IP-protocol. To implement the main features and functions of the fuzzy control system of quality telecommunication services it is used multilayer feedforward neural network.

Keywords: quality of communication, IP-telephony, fuzzy set, fuzzy implication, neural network

Procedia PDF Downloads 466

Authors: Vinod Kumar Jatav, Dr. Vrijendra Singh

Abstract:

WiMAX is an emerging wireless technology designed by WiMAX forum. WiMAX technology delivers broadband internet access with QoS, mobility and robust security. WiMAX is among the prominent mobile broadband wireless technology which laid the foundation for the next generation networks (NGN). The next-generation communication system for railway should facilitate high level network availability, fast mobility for high speed trains with reliability, high handover rate, the firmness of train operations, and high QoS. The system should also be capable to provide various railway services by transmitting big data efficiently. One of the most promising technologies for the next generation railway wireless communication is Mobile WiMAX. This paper analyses some of the network architectures for railway wireless communication and considers the elementary concepts to facilitate the users with broadband internet access on trains. The paper aims to recognize the suitability of Mobile WiMAX technology for the special requirements of broadband internet facilities and wireless telecommunication services of Railways.

Keywords: Broadband internet, IEEE 802.16e, mobile WiMAX, Railway wireless communication

Procedia PDF Downloads 519

Authors: Mohamad Ibrahim Al Ladan

Abstract:

Social networks, such as Facebook, Myspace, LinkedIn, Google+, and Twitter have experienced exponential growth and a remarkable adoption rate in recent years. They provide attractive means of online social interactions and communications with family, friends, and colleagues from around the corner or across the globe, and they have become an important part of daily digital interactions for more than one and a half billion users around the world. The various personal information sharing practices that social network providers encourage have led to their success as innovative social interaction platforms. However, these practices have resulted in ample concerns with respect to privacy and security from different stakeholders. Addressing these privacy and security concerns in social networks is a must for these networks to be sustainable. Existing security and privacy tools may not be enough to address existing concerns. Some guidelines should be followed to protect users from the existing risks. In this paper, we have investigated and discussed the various privacy and security issues and concerns pertaining to social networks. Moreover, we have classified these privacy and security issues and presented a thorough discussion of the implications of these issues and concerns on the future of the social networks. In addition, we have presented a set of guidelines as precaution measures that users can consider to address these issues and concerns.

Keywords: social networks privacy issues, social networks security issues, social networks privacy precautions measures, social networks security precautions measures

Procedia PDF Downloads 298

Authors: Mehdi Destani, Asghar Heydari

Abstract:

Objective: The present study aimed to investigate the association between pathological personality dimensions and emotion dysregulation through the mediating role of Cyberchondria behaviors among users of virtual networks. Materials and methods: A descriptive–correlational research method was used in this study, and the statistical population consisted of all people active on social network sites in 2020. The sample size was 300 people who were selected through Convenience Sampling. Data collection was carried out in a survey method using online questionnaires, including the "Difficulties in Emotion Regulation Scale" (DERS), Personality Inventory for DSM-5 Brief Form (PID-5-BF), and Cyberchondria Severity Scale Brief Form (CSS-12). Data analysis was conducted using Pearson's Correlation Coefficient and Structural Equation Modeling (SEM). Findings: Findings suggested that pathological personality dimensions and Cyberchondria behaviors have a positive and significant association with emotion dysregulation (p<0.001). The presented model had a good fit with the data. The variable “pathological personality dimensions” with an overall effect (p<0.001, β=0.658), a direct effect (p<0.001, β=0.528), and an indirect mediating effect through Cyberchondria Behaviors (p<.001), β=0.130), accounted for emotion dysregulation among virtual network users. Conclusion: The research findings showed a necessity to pay attention to the pathological personality dimensions as a determining variable and Cyberchondria behaviors as a mediator in the vulnerability of users of social network sites to emotion dysregulation.

Keywords: cyberchondria, emotion dysregulation, pathological personality dimensions, social networks

Procedia PDF Downloads 97

Authors: Grzegorz Sierpiński, Ireneusz Celiński, Marcin Staniek

Abstract:

The article discusses multi modal mobility in contemporary societies as a main planning and organization issue in the functioning of administrative bodies, a problem which really exists in the space of contemporary cities in terms of shaping modern transport systems. The article presents classification of available resources and initiatives undertaken for developing multi modal mobility. Solutions can be divided into three groups of measures–physical measures in the form of changes of the transport network infrastructure, organizational ones (including transport policy) and information measures. The latter ones include in particular direct support for people travelling in the transport network by providing information about ways of using available means of transport. A special measure contributing to this end is a trip planner. The article compares several selected planners. It includes a short description of the Green Travelling Project, which aims at developing a planner supporting environmentally friendly solutions in terms of transport network operation. The article summarizes preliminary findings of the project.

Keywords: mobility, modal split, multimodal trip, multimodal platforms, sustainable transport

Procedia PDF Downloads 408

Authors: Rasool Sadeghi, Sayed Mahdi Faghih Imani, Negar Najafi

Abstract:

The inherent features of Named Data Networking (NDN) provides a robust solution for Internet of Thing (IoT). Therefore, NDN-IoT has emerged as a combined architecture which exploits the benefits of NDN for interconnecting of the heterogeneous objects in IoT. In NDN-IoT, caching schemes are a key role to improve the network performance. In this paper, we consider the effectiveness of cache replacement schemes in NDN-IoT scenarios. We investigate the impact of replacement schemes on average delay, average hop count, and average interest retransmission when replacement schemes are Least Frequently Used (LFU), Least Recently Used (LRU), First-In-First-Out (FIFO) and Random. The simulation results demonstrate that LFU and LRU present a stable performance when the cache size changes. Moreover, the network performance improves when the number of consumers increases.

Keywords: NDN-IoT, cache replacement, performance, ndnSIM

Procedia PDF Downloads 359

Authors: Chris George, Adam Hamlin, Lily Pereg, Richard Charlesworth, Gal Winter

Abstract:

Background: According to the ‘hygiene hypothesis’ the current rise in allergies and autoimmune diseases stems mainly from reduced microbial exposure due, amongst other factors, to urbanisation and distance from soil. However, this hypothesis is based on epidemiological and not biological data. Useful insights into the underlying mechanisms of this hypothesis can be gained by studying our interaction with soil. Soil microbiota may be directly ingested or inhaled by humans, enter the body through skin-soil contact or using plants as vectors. This study aims to examine the ability of soil microbiota to colonise the gut, study the interaction of soil microbes with the immune system and their potential protective activity. Method: The nutrition of the rats was supplemented daily with fresh or autoclaved soil for 21 days followed by 14 days of no supplementations. Faecal samples were collected throughout and analysed using 16S sequencing. At the end of the experiment rats were sacrificed and tissues and digesta were collected. Results/Conclusion: Results showed significantly higher richness and diversity following soil supplementation even after recovery. Specific soil microbial groups identified as able to colonise the gut. Of particular interest was the mucosal layer which emerged as a receptive host for soil microorganisms. Histological examination revealed innate and adaptive immune activation. Findings of this study reinforce the ‘hygiene hypothesis’ by demonstrating the ability of soil microbes to colonise the gut and activate the immune system. This paves the way for further studies aimed to examine the interaction of soil microorganisms with the immune system.

Keywords: gut microbiota, hygiene hypothesis, microbiome, soil

Procedia PDF Downloads 248

Authors: Amit Tribedi

Abstract:

Genuine Quantumness present in Quantum Systems is the resource for implementing Quantum Information and Computation Protocols which can outperform the classical counterparts. These Quantumness measures encompass non-local ones known as quantum entanglement (QE) and quantum information theoretic (QIT) ones, e.g. Quantum Discord (QD). In this paper, some well-known measures of QE and QD in some wheel-like frustrated molecular magnetic systems have been studied. One of the systems has already been synthesized using coordination chemistry, and the other is hypothetical, where the dominant interaction is the spin-spin exchange interaction. Exact analytical methods and exact numerical diagonalization methods have been used. Some counter-intuitive non-trivial features, like non-monotonicity of quantum correlations with temperature, persistence of multipartite entanglement over bipartite ones etc. indicated by the behaviour of the correlations and the QIT measures have been found. The measures, being operational ones, can be used to realize the resource of Quantumness in experiments.

Keywords: 0D Magnets, discord, entanglement, frustration

Procedia PDF Downloads 221

Authors: Romain Lestage, Marc Bourreau

Abstract:

In this paper we analyze the interplay between access to the last-mile network and net neutrality in the market for Internet access. We consider two Internet Service Providers (ISPs), which act as platforms between Internet users and Content Providers (CPs). One of the ISPs is vertically integrated and provides access to its last-mile network to the other (non-integrated) ISP. We show that a lower access price increases the integrated ISP's incentives to charge CPs positive termination fees (i.e., to deviate from net neutrality), and decreases the non-integrated ISP's incentives to charge positive termination fees.

Keywords: net neutrality, access regulation, internet access, two-sided markets

Procedia PDF Downloads 372

Authors: Ramón Cruzat, Sergio F. Ochoa, Ignacio Casas, Luis A. Guerrero, José Bravo

Abstract:

Patients under health treatments that involve long stays at a hospital or health centre (e.g. cancer, organ transplants and severe burns), tend to get bored or depressed because of the lack of social interaction with family and friends. Such a situation also affects the evolution and effectiveness of their treatments. In many cases, the solution to this problem involves extra challenges, since many patients need to rest quietly (or remain in bed) to their being contagious. Considering the weak health condition in which usually are these kinds, keeping them motivated and quiet represents an important challenge for nurses and caregivers. This article presents a mobile ubiquitous game called MagicRace, which allows hospitalized kinds to interact socially with one another without putting to risk their sensitive health conditions. The game does not require a communication infrastructure at the hospital, but instead, it uses a mobile ad hoc network composed of the handheld devices used by the kids to play. The usability and performance of this application was tested in two different sessions. The preliminary results show that users experienced positive feelings from this experience.

Keywords: ubiquitous game, children's emotional support, social isolation, mobile collaborative interactions

Procedia PDF Downloads 426

Authors: Faheem Ur Rehman, Muhammad Khalil Khan, Miao Qing

Abstract:

This study investigated the asymmetric links and portfolio strategies between green bonds and the markets of three different cryptocurrencies, i.e., green, Islamic, and conventional, using data from January 1, 2018, to April 8, 2022, and employing asymmetric TVP-VAR model to quantify risk spillovers in the network analysis. In addition, we use the minimum variance, minimum correlation, and minimum connectedness methodologies to assess the portfolio implications. The results of the asymmetric dynamic connectedness index (TCI) model show that by adopting cryptocurrencies for digital finance, risk spillovers are found to be reduced. The findings of net directional connectedness demonstrate that during the study period, green bonds consistently get return spillovers from all other network variables. Positive return spillovers are bigger in magnitude than negative ones. These results imply that the influence of the green bond market on the cryptocurrency markets is decreasing. Positive return spillovers generate higher connectedness values for (HG, BNB, and TRX) coins and persistent net recipients in the specific network. On the other hand, Cardano and ADA coins are persistent net transmitters in the system. XLM and MIOTA's responsibilities shift over time, and there is evidence of asymmetry when both positive and negative returns are considered. According to the pairwise portfolio weights, BNB vs. BTC has the largest portfolio weights in the system, followed by BNB vs. Ethereum, suggesting the best investment strategies in the network.

Keywords: asymmetric TVP-VAR, global sustainable index, cryptocurrency, portfolios

Procedia PDF Downloads 73

Authors: Swati Tyagi, Syed Abbas

Abstract:

Fractional-order Hopfield neural networks are generally used to model the information processing among the interacting neurons. To show the constancy of the processed information, it is required to analyze the stability of these systems. In this work, we perform Mittag-Leffler stability for the corresponding Caputo fractional-order bidirectional associative memory (BAM) neural networks with various time-delays. We derive sufficient conditions to ensure the existence and uniqueness of the equilibrium point by using the theory of topological degree theory. By applying the fractional Lyapunov method and Mittag-Leffler functions, we derive sufficient conditions for the global Mittag-Leffler stability, which further imply the global asymptotic stability of the network equilibrium. Finally, we present two suitable examples to show the effectiveness of the obtained results.

Keywords: bidirectional associative memory neural network, existence and uniqueness, fractional-order, Lyapunov function, Mittag-Leffler stability

Procedia PDF Downloads 356

Authors: Norah Alosayl

Abstract:

English is considered the most important foreign language in the Kingdom of Saudi Arabia (KSA) because of the usefulness of English as a global language compared to Arabic. As students’ desire to improve their English language skills has grown, English writing has been identified as the most difficult problem for Saudi students in their language learning. Although the English language in Saudi Arabia is taught beginning in the seventh grade, many students have problems at the university level, especially in writing, due to a gap between what is taught in secondary and high schools and university expectations- pupils generally study English at school, based on one book with few exercises in vocabulary and grammar exercises, and there are no specific writing lessons. Moreover, from personal teaching experience at King Saud bin Abdulaziz University, students face real problems with their writing. This paper revolves around the blackboard-based collaborative writing to help the undergraduate Saudi EFL students, in their first year enrolled in two sections of ENGL 101 in the first semester of 2021 at King Saud bin Abdulaziz University, practice the most difficult skill they found in their writing through a small group. Therefore, a sequential mixed methods design will be suited. The first phase of the study aims to highlight the most difficult skill experienced by students from an official writing exam that is evaluated by their teachers through an official rubric used in King Saud bin Abdulaziz University. In the second phase, this study will intend to investigate the benefits of social interaction on the process of learning writing. Students will be provided with five collaborative writing tasks via discussion feature on Blackboard to practice a skill that they found difficult in writing. the tasks will be formed based on social constructivist theory and pedagogic frameworks. The interaction will take place between peers and their teachers. The frequencies of students’ participation and the quality of their interaction will be observed through manual counting, screenshotting. This will help the researcher understand how students actively work on the task through the amount of their participation and will also distinguish the type of interaction (on task, about task, or off-task). Semi-structured interviews will be conducted with students to understand their perceptions about the blackboard-based collaborative writing tasks, and questionnaires will be distributed to identify students’ attitudes with the tasks.

Keywords: writing difficulties, blackboard-based collaborative writing, process of learning writing, interaction, participations

Procedia PDF Downloads 185