Search results for: metabolic networks

Authors: Rahil Sahragard, Mahmoud Hatami, Rostam Bahadori Khalili

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Diabetes is one of the most important health problems in the world and a chronic disease requiring continuous care and therefore, it is necessary for patients to undergo self-care and nutrition education. This study was conducted to evaluate the effect of nutrition education on the metabolic control of diabetic patients in Tehran in 2015. An experimental study was conducted on 100 patients who had previously been approved by a specialist physician for diabetes and at least one year after their onset. At first, patients without any knowledge of the educational program were selected as sample and from them a checklist containing demographic and specific information about diabetes was filled and were taken three fasting blood glucose and three times fasting blood glucose (5 p.m.) Then, the patients received face-to-face training in the same conditions for 2 weeks in a Mehregan hospital of Tehran, and received 3 months of training, while they were fully monitored and during this time, samples that had a cold or blood pressure-related disease or were admitted to the hospital were excluded from the study. After the end of the study, the checklist was filled again and 3 fasting blood glucose and 3 fasting blood glucose samples were taken, the results were statistically analyzed by MC Nemar's statistical test. The research findings were performed on 100 patients 41.7% male and 58.3% women, the range of age was between 22 and 60 years old, with a duration of diabetes ranging from 1 to 15 years. Abnormal fasting blood glucose from 95% to 48.3% (P <0.0001) and non-fasting blood glucose decreased from 91.6% to 71.2% (P <0.001). Research has shown that training on blood glucose control has been successful, therefore, it is recommended that more research is done in the field of education to help patients with diabetes more comfortable.

Keywords: nutrition education, diabetes, function, insulin, chronic, metabolic control

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Authors: Gon Park

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Seoul in South Korea established the 2030 Seoul City Master Plan that contains green-link projects to connect critical green areas within the city. However, the plan does not have detailed analyses for green infrastructure to incorporate land-cover information to many structural classes. This study maps green infrastructure networks of Seoul for complementing their green plans with identifying and raking green areas. Hubs and links of main elements of green infrastructure have been identified from incorporating cadastral data of 967,502 parcels to 135 of land use maps using geographic information system. Network analyses were used to rank hubs and links of a green infrastructure map with applying a force-directed algorithm, weighted values, and binary relationships that has metrics of density, distance, and centrality. The results indicate that network analyses using cadastral parcel data can be used as the framework to identify and rank hubs, links, and networks for the green infrastructure planning under a variable scenarios of green areas in cities.

Keywords: cadastral data, green Infrastructure, network analysis, parcel data

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Authors: H. Scholz, P. Kuehne, G. Heckenberger

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Dietary Anion-Cation Balance (DCAB) in grazing systems under German conditions has a tendency to decrease from May until September and often are measured DCAB lower than 100 meq per kg dry matter. Lower DCAB in grass feeding system can change the metabolic status of suckler cows and often are results in acidotic metabolism. Measurement of acid-base excretion in dairy cows has been proved to a method to evaluate the acid-base status. The hypothesis was that metabolic imbalances could be identified by urine measurement in suckler cows. The farm study was conducted during the grazing seasons 2017 and 2018 and involved 7 suckler cow farms in Germany. Suckler cows were grazing during the whole time of the investigation and had no access to other feeding components. Cows had free access to water and salt block and free access to minerals (loose). The dry matter of the grass was determined at 60 °C and were then analysed for energy and nutrient content and for the Dietary Cation-Anion Balance (DCAB). Urine was collected in 50 ml-glasses and analysed for net acid-base excretion (NSBA) and the concentration of creatinine and urea in the laboratory. Statistical analysis took place with ANOVA with fixed effects of farms (1-7), month (May until September), and number of lactations (1, 2, and ≥ 3 lactations) using SPSS Version 25.0 for windows. An alpha of 0.05 was used for all statistical tests. During the grazing periods of years 2017 and 2018, an average DCAB was observed in the grass of 167 meq per kg DM. A very high mean variation could be determined from -42 meq/kg to +439 meq/kg. Reference values in relation to DCAB were described between 150 meq and 400 meq per kg DM. It was found the high chlorine content with reduced potassium level led to this reduction in DCAB at the end of the grazing period. Between the DCAB of the grass and the NSBA in urine of suckler cows was a correlation according to PEARSON of r = 0.478 (p ≤ 0.001) or after SPEARMAN of r = 0.601 (p ≤ 0.001) observed. For the control of urine values of grazing suckler cows, the wide spread of the values poses a challenge of the interpretation, especially since the DCAB is unknown. The influence of several feeding components such as chlorine, sulfur, potassium, and sodium (ions for the DCAB) and dry matter feed intake during the grazing period of suckler cows should be taken into account in further research. The results obtained show that up a decrease in the DCAB is related to a decrease in NSBA in urine of suckler cows. Monitoring of metabolic disturbances should include analysis of urine, blood, milk, and ruminal fluid.

Keywords: dietary anion-cation balance, DCAB, net acid-base excretion, NSBA, suckler cow, grazing period

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Authors: Mehdi Assefi, Keihan Hataminezhad

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One of the efficient architectures for exploring the resources in wireless ad-hoc networks is contact-based architecture. In this architecture, each node assigns a unique zone for itself and each node keeps all information from inside the zone, as well as some from outside the zone, which is called contact. Reducing the overlap between different zones of a node and its contacts increases its performance, therefore Edge Method (EM) is designed for this purpose. Contacts selected by EM do not have any overlap with their sources, but for choosing the contact a vast amount of information must be transmitted. In this article, we will offer a new protocol for contact selection, which is called PEM. The objective would be reducing the volume of transmitted information, using Non-Uniform Dissemination Probabilistic Protocols. Consumed energy for contact selection is a function of the size of transmitted information between nodes. Therefore, by reducing the content of contact selection message using the PEM will decrease the consumed energy. For evaluation of the PEM we applied the simulation method. Results indicated that PEM consumes less energy compared to EM, and by increasing the number of nodes (level of nodes), performance of PEM will improve in comparison with EM.

Keywords: wireless ad-hoc networks, contact selection, method for CARD, energy-efficient

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Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

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The assessment of disease levels in crop fields is an important and time-consuming task that generally relies on expert knowledge of trained individuals. Image classification in agriculture problems historically has been based on classical machine learning strategies that make use of hand-engineered features in the top of a classification algorithm. This approach tends to not produce results with high accuracy and generalization to the classes classified by the system when the nature of the elements has a significant variability. The advent of deep convolutional neural networks has revolutionized the field of machine learning, especially in computer vision tasks. These networks have great resourcefulness of learning and have been applied successfully to image classification and object detection tasks in the last years. The objective of this work was to propose a new method based on deep learning convolutional neural networks towards the task of disease level monitoring. Common RGB images of winter wheat were obtained during a growing season. Five categories of disease levels presence were produced, in collaboration with agronomists, for the algorithm classification. Disease level tasks performed by experts provided ground truth data for the disease score of the same winter wheat plots were RGB images were acquired. The system had an overall accuracy of 84% on the discrimination of the disease level classes.

Keywords: crop disease assessment, deep learning, precision agriculture, residual neural networks

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Authors: Kirstie Smith

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This paper elucidates the value of neighborhood perception as it contributes to the advancement of well-being for individuals and families within a neighborhood. Through in-depth interviews with city residents, this paper examines the degree to which key stakeholders’ (residents) evaluate their neighborhood and perception of resources and identify, access, and utilize local assets existing in the community. Additionally, the research objective included conducting a community inventory that qualified the community assets and resources of lower-income neighborhoods of a medium-sized industrial city. Analysis of the community’s assets was compared with the interview results to allow for a better understanding of the community’s condition. Community mapping revealed the key informants’ reflections of assets were somewhat validated. In each neighborhood, there were more assets mapped than reported in the interviews. Another chief supposition drawn from this study was the identification of key development partners and social networks that offer the potential to facilitate locally-driven community development. Overall, the participants provided invaluable local knowledge of the perception of neighborhood assets, the well-being of residents, the condition of the community, and suggestions for responding to the challenges of the entire community in order to mobilize the present assets and networks.

Keywords: community mapping, family, resource allocation, social networks

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Authors: Wasif Mughees, Malik Al-Ahmad, Muhammad Naeem

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This research involves the design and analysis of pinch-based water/wastewater networks to minimize water utility in the petrochemical and petroleum industries. A study has been done on Tehran Oil Refinery to analyze feasibilities of regeneration, reuse and recycling of water network. COD is considered as a single key contaminant. Amount of freshwater was reduced about 149m3/h (43.8%) regarding COD. Re-design (or retrofitting) of water allocation in the networks was undertaken. The results were analyzed through graphical method and mathematical programming technique which clearly demonstrated that amount of required water would be determined by mass transfer of COD.

Keywords: minimization, water pinch, water management, pollution prevention

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Authors: Abdelhadi Lotfi, Abdelkader Benyettou

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In this work, a training algorithm for probabilistic neural networks (PNN) is presented. The algorithm addresses one of the major drawbacks of PNN, which is the size of the hidden layer in the network. By using a cross-validation training algorithm, the number of hidden neurons is shrunk to a smaller number consisting of the most representative samples of the training set. This is done without affecting the overall architecture of the network. Performance of the network is compared against performance of standard PNN for different databases from the UCI database repository. Results show an important gain in network size and performance.

Keywords: classification, probabilistic neural networks, network optimization, pattern recognition

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Authors: Jeremiah Madzimure

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In order to stay competitive in business and improve performance, Small and Medium Enterprises (SMEs) need to make use of business networking and logistics integration. Strategic networking and logistics integration in business companies have become critical as they allow supplier partnering, exchange of vital information/ access to valuable resources allowing innovation, gaining access to additional resources, sharing risks and costs which is required for enhancing company performance. The purpose of this study was to examine the influence of strategic networks and logistics integration on company performance: the case of small and medium enterprises in South Africa. A quantitative research design was adopted in this study, and 137 SMEs owners and managers completed and returned the survey questionnaire. Confirmatory Factor Analysis (CFA) was conducted using the Analysis of Moment Structures (AMOS), version 24.0 to assess psychometric properties of the measurement scales. Path modelling techniques were used to test the proposed hypothesis. Three research hypotheses were postulated. The results indicate that strategic networks had a positive and significant influence on logistics integration and company performance. As well logistics integration had a strong positive and significant influence on company performance. This study provides a useful model for analysing the relationship between strategic networks and logistics integration on company performance. Moreover, the findings of the study provide useful insights into how SMEs should benefit from business networking and logistics integration so as to improve their performance. The implications of the study are discussed, and finally, limitations and recommendations are indicated.

Keywords: strategic networking, logistics integration, company performance, SMEs

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Authors: Lydia Foucan, Christine Rambhojan, Rachel Billy, Christophe Armand, Carl-Thony Michel, Jean-Marc Lacorte, Laurent Larifla

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Leptin, an adipocyte-derived hormone, modulates insulin secretion and action via the leptin receptor (LEPR) that is expressed in pancreatic beta cells, adipose tissue, and muscle. Several polymorphisms have been described in the human LEPR gene including p.K109R (rs1137100), p.Q223R (rs1137101) and p.K656N (rs1805094) polymorphisms. The role of these polymorphisms is not yet studied in Guadeloupian population. Our aim was to explore the association of LEPR polymorphisms (K109R, Q223R and K656N) with leptin levels and obesity in non-diabetic Afro-Caribbean subjects. Genotypic analysis of the three polymorphisms was performed in 425 subjects using TaqMan and KASPar Assays. Serum leptin was measured with ELISA kits Biovendor® (RD191001100). Logistic regressions were used for assessment of statistical associations. Mean age was 47.6 ± 12.7 years. Among the participants, 238 (56 %) were women, 124 (30%) were obese and 155 (36.5%) had abdominal obesity. Carriers of LEPR K656N rs1805094 rare allele had significant higher frequencies of obesity (P = 0.007), abdominal obesity (P = 0.004) and metabolic syndrome (P = 0.021) but mean leptin level was not significantly different between both groups (P = 0.075). Odds ratios, adjusted for age and sex associated with presence of rs1805094 rare allele were 1.8 (1.1-2.9), P = 0.012 for obesity, 2.0 (1.2-3.3), P = 0.008 for abdominal obesity and 1.8 (1.1-3.0), P = 0.031 for MetS. No significant association was found with K109R, Q223R. These findings suggest that the K656N polymorphism (but not the K109R or Q223R polymorphism) of LEPR is associated with obesity, abdominal obesity and metabolic syndrome in this Afro-Caribbean non-diabetic population.

Keywords: Afro-Caribbean, leptin levels, leptin receptor gene polymorphisms, obesity

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Authors: Piotr J. Balwierz, Mikhail Pachkov, Phil Arnold, Andreas J. Gruber, Mihaela Zavolan, Erik van Nimwegen

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Understanding the key players and interactions in the regulatory networks that control gene expression and chromatin state across different cell types and tissues in metazoans remains one of the central challenges in systems biology. Our laboratory has pioneered a number of methods for automatically inferring core gene regulatory networks directly from high-throughput data by modeling gene expression (RNA-seq) and chromatin state (ChIP-seq) measurements in terms of genome-wide computational predictions of regulatory sites for hundreds of transcription factors and micro-RNAs. These methods have now been completely automated in an integrated webserver called ISMARA that allows researchers to analyze their own data by simply uploading RNA-seq or ChIP-seq data sets and provides results in an integrated web interface as well as in downloadable flat form. For any data set, ISMARA infers the key regulators in the system, their activities across the input samples, the genes and pathways they target, and the core interactions between the regulators. We believe that by empowering experimental researchers to apply cutting-edge computational systems biology tools to their data in a completely automated manner, ISMARA can play an important role in developing our understanding of regulatory networks across metazoans.

Keywords: gene expression analysis, high-throughput sequencing analysis, transcription factor activity, transcription regulation

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Authors: Hwayeon Song

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The long-term purpose of research and development (R&D) programs is to strengthen national competitiveness by developing new knowledge and technologies. Thus, it is important to determine a proper budget for government programs to maintain the vigor of R&D when the total funding is tight due to the national deficit. In this regard, a ripple effect analysis for the budgetary changes in R&D programs is necessary as well as an investigation of the current status. This study proposes a new approach using Artificial Neural Networks (ANN) for both tasks. It particularly focuses on R&D programs related to Construction and Transportation (C&T) technology in Korea. First, key factors in C&T technology are explored to draw impact indicators in three areas: economy, society, and science and technology (S&T). Simultaneously, ANN is employed to evaluate the relationship between data variables. From this process, four major components in R&D including research personnel, expenses, management, and equipment are assessed. Then the ripple effect analysis is performed to see the changes in the hypothetical future by modifying current data. Any research findings can offer an alternative strategy about R&D programs as well as a new analysis tool.

Keywords: Artificial Neural Networks, construction and transportation technology, Government Research and Development, Ripple Effect

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Authors: Nouria Bouchikhi, Soufiane Bedjaoui, C. Tewfik Bouchaour, Lamia Alachaher Bedjaoui, Ulrich Maschke

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Swelling properties and phase diagrams of binary systems composed of liquid crystalline networks and a low molecular mass liquid crystal (LMWLC) have been investigated. The networks were prepared by ultraviolet (UV) irradiation of reactive mixtures including a monomer, a cross-linking agent and a photo-initiator. These networks were prepared using two cross-linking agents: 1,6 hexanedioldiacrylate (HDDA) and a mesogenic acrylic acid 6-(4’-(6-acryloyloxy-hexyloxy) biphenyl-4-yl oxy) hexyl ester (AHBH). The obtained dry networks were characterized by differential scanning calorimetry, and immersed in an excess of a LMWLC solvent 4-cyano-4’-pentylbiphenyl (5CB), forming polymer gels. A detailed study by polarized optical microscopy allowed to determine the swelling degree of the gels and to follow the phase behavior of the solvent inside the polymer matrix in a wide range of temperature. It has been found that the gels undergo a sharp decrease of their swelling degree in response to an infinitesimal change of temperature. This finding adds new and interesting aspects on the actuators applications. We have subsequently explored the effect of different parameters on volume phase transition of these liquid crystalline materials. Such as the cross-linking density (CD), a nature of cross-linking agent and the photo initiator concentration.

Keywords: cross-linking density, liquid crystalline elastomers, phase diagrams, swelling

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Authors: S. Neelima, P. S. Subramanyam

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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)

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Authors: Redouane Tlemsani, Abdelkader Benyettou

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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

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Authors: Tahir Nawaz Cheema, Dumitru Baleanu, Ali Raza

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In this research work, we design intelligent computing through Bayesian Regularization artificial neural networks (BRANNs) introduced to solve the mathematical modeling of infectious diseases (Covid-19). The dynamical transmission is due to the interaction of people and its mathematical representation based on the system's nonlinear differential equations. The generation of the dataset of the Covid-19 model is exploited by the power of the explicit Runge Kutta method for different countries of the world like India, Pakistan, Italy, and many more. The generated dataset is approximately used for training, testing, and validation processes for every frequent update in Bayesian Regularization backpropagation for numerical behavior of the dynamics of the Covid-19 model. The performance and effectiveness of designed methodology BRANNs are checked through mean squared error, error histograms, numerical solutions, absolute error, and regression analysis.

Keywords: mathematical models, beysian regularization, bayesian-regularization backpropagation networks, regression analysis, numerical computing

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Authors: W. Hashim, H. Burok, N. Ghazaly, H. Ahmad Nasir, N. Mohamad Anas, A. F. Ismail, K. L. Yau

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Future communication networks require devices that are able to work on a single platform but support heterogeneous operations which lead to service diversity and functional flexibility. This paper proposes two cognitive mechanisms termed cognitive hybrid function which is applied in multiple broadband user terminals in order to maintain reliable connectivity and preventing unnecessary interferences. By employing such mechanisms especially for future hyper-dense network, we can observe their performances in terms of optimized speed and power saving efficiency. Results were obtained from several empirical laboratory studies. It was found that selecting reliable network had shown a better optimized speed performance up to 37% improvement as compared without such function. In terms of power adjustment, our evaluation of this mechanism can reduce the power to 5dB while maintaining the same level of throughput at higher power performance. We also discuss the issues impacting future telecommunication standards whenever such devices get in place.

Keywords: dense network, intelligent network selection, multiple networks, transmit power adjustment

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Authors: Farahnaz Karami

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Routing is an important, challenging task in mobile ad hoc networks due to node mobility, lack of central control, unstable links, and limited resources. An ant colony has been found to be an attractive technique for routing in Mobile Ad Hoc Networks (MANETs). However, existing swarm intelligence based routing protocols find an optimal path by considering only one or two route selection metrics without considering correlations among such parameters making them unsuitable lonely for routing real time applications. Fuzzy logic combines multiple route selection parameters containing uncertain information or imprecise data in nature, but does not have multipath routing property naturally in order to provide load balancing. The objective of this paper is to design a routing algorithm using fuzzy logic and ant colony that can solve some of routing problems in mobile ad hoc networks, such as nodes energy consumption optimization to increase network lifetime, link failures rate reduction to increase packet delivery reliability and providing load balancing to optimize available bandwidth. In proposed algorithm, the path information will be given to fuzzy inference system by ants. Based on the available path information and considering the parameters required for quality of service (QoS), the fuzzy cost of each path is calculated and the optimal paths will be selected. NS2.35 simulation tools are used for simulation and the results are compared and evaluated with the newest QoS based algorithms in MANETs according to packet delivery ratio, end-to-end delay and routing overhead ratio criterions. The simulation results show significant improvement in the performance of these networks in terms of decreasing end-to-end delay, and routing overhead ratio, and also increasing packet delivery ratio.

Keywords: mobile ad hoc networks, routing, quality of service, ant colony, fuzzy logic

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Authors: Shaharuddin Salleh, Sara Marouf, Hosein Mohammadi

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Directional sensor networks (DSNs) have recently attracted a great deal of attention due to their extensive applications in a wide range of situations. One of the most important problems associated with DSNs is covering a set of targets in a given area and, at the same time, maximizing the network lifetime. This is due to limitation in sensing angle and battery power of the directional sensors. This problem gets more complicated by the possibility that targets may have different coverage requirements. In the present study, this problem is referred to as priority-based target coverage (PTC). As sensors are often densely deployed, organizing the sensors into several cover sets and then activating these cover sets successively is a promising solution to this problem. In this paper, we propose a learning automata-based algorithm to organize the directional sensors into several cover sets in such a way that each cover set could satisfy coverage requirements of all the targets. Several experiments are conducted to evaluate the performance of the proposed algorithm. The results demonstrated that the algorithms were able to contribute to solving the problem.

Keywords: directional sensor networks, target coverage problem, cover set formation, learning automata

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Authors: Julien Romieux, Fabio Verdicchio

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Wireless Sensor Networks (WSNs), which sense environmental data with battery-powered nodes, require multi-hop communication. This power-demanding task adds an extra workload that is unfairly distributed across the network. As a result, nodes run out of battery at different times: this requires an impractical individual node maintenance scheme. Therefore we investigate a new Cooperative Sensing approach that extends the WSN operational life and allows a more practical network maintenance scheme (where all nodes deplete their batteries almost at the same time). We propose a novel cooperative algorithm that derives a piecewise representation of the sensed signal while controlling approximation accuracy. Simulations show that our algorithm increases WSN operational life and spreads communication workload evenly. Results convey a counterintuitive conclusion: distributing workload fairly amongst nodes may not decrease the network power consumption and yet extend the WSN operational life. This is achieved as our cooperative approach decreases the workload of the most burdened cluster in the network.

Keywords: cooperative signal processing, signal representation and approximation, power management, wireless sensor networks

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Authors: Ren Moses

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The process of classifying vehicles on a highway is hereby viewed as a pattern recognition problem in which connectionist techniques such as artificial neural networks (ANN) can be used to assign vehicles to their correct classes and hence to establish optimum axle spacing thresholds. In the United States, vehicles are typically classified into 13 classes using a methodology commonly referred to as “Scheme F”. In this research, the ANN model was developed, trained, and applied to field data of vehicles. The data comprised of three vehicular features—axle spacing, number of axles per vehicle, and overall vehicle weight. The ANN reduced the classification error rate from 9.5 percent to 6.2 percent when compared to an existing classification algorithm that is not ANN-based and which uses two vehicular features for classification, that is, axle spacing and number of axles. The inclusion of overall vehicle weight as a third classification variable further reduced the error rate from 6.2 percent to only 3.0 percent. The promising results from the neural networks were used to set up new thresholds that reduce classification error rate.

Keywords: artificial neural networks, vehicle classification, traffic flow, traffic analysis, and highway opera-tions

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Authors: Thiago M. R Dias, Gray F. Moita

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The analysis of scientific collaboration networks has contributed significantly to improving the understanding of how does the process of collaboration between researchers and also to understand how the evolution of scientific production of researchers or research groups occurs. However, the identification of collaborations in large scientific databases is not a trivial task given the high computational cost of the methods commonly used. This paper proposes a method for identifying collaboration in large data base of curriculum researchers. The proposed method has low computational cost with satisfactory results, proving to be an interesting alternative for the modeling and characterization of large scientific collaboration networks.

Keywords: extraction, data integration, information retrieval, scientific collaboration

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Authors: Mallikarjuna Rao Yamarthy, Subramanyam Makam Venkata, Satya Prasad Kodati

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The main objective of the proposed work is to reduce the overall network traffic incurred by mobility management, packet delivery cost and to increase the resource utilization. The proposed algorithm, An Efficient Resource Management Algorithm (ERMA) for mobility management in wireless mesh networks, relies on pointer based mobility management scheme. Whenever a mesh client moves from one mesh router to another, the pointer is set up dynamically between the previous mesh router and current mesh router based on the distance constraints. The algorithm evaluated for signaling cost, data delivery cost and total communication cost performance metrics. The proposed algorithm is demonstrated for both internet sessions and intranet sessions. The proposed algorithm yields significantly better performance in terms of signaling cost, data delivery cost, and total communication cost.

Keywords: data delivery cost, mobility management, pointer forwarding, resource management, wireless mesh networks

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Authors: Navneet Kaur, Amarpreet Singh

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In Vehicular Adhoc Networks, Data Dissemination is a challenging task. There are number of techniques, types and protocols available for disseminating the data but in order to preserve limited bandwidth and to disseminate maximum data over networks makes it more challenging. There are broadcasting, multicasting and geocasting based protocols. Multicasting based protocols are found to be best for conserving the bandwidth. One such protocol named BEAM exists that improves the performance of Vehicular Adhoc Networks by reducing the number of in-network message transactions and thereby efficiently utilizing the bandwidth during an emergency situation. But this protocol may result in multicar chain collision as there was no V2V communication. So, this paper proposes a new protocol named Enhanced Bandwidth Efficient Cluster Based Multicasting Protocol (EBECM) that will overcome the limitations of existing BEAM protocol. And Simulation results will show the improved performance of EBECM in terms of Routing overhead, throughput and PDR when compared with BEAM protocol.

Keywords: BEAM, data dissemination, emergency situation, vehicular adhoc network

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Authors: Shara Toibayeva, Irbulat Utepbergenov, Lyazzat Issabekova, Aidana Bodesova

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The article discusses the importance of quality assessment as a strategic tool in business and emphasizes the significance of the effectiveness of quality management systems (QMS) for enterprises. The evaluation of these systems takes into account the specificity of quality indicators, the multilevel nature of the system, and the need for optimal selection of the number of indicators and evaluation of the system state, which is critical for making rational management decisions. Methods and models of automated enterprise quality management are proposed, including an intelligent automated quality management system integrated with the Management Information and Control System. These systems make it possible to automate the implementation and support of QMS, increasing the validity, efficiency, and effectiveness of management decisions by automating the functions performed by decision makers and personnel. The paper also emphasizes the use of recurrent neural networks to improve automated quality management. Recurrent neural networks (RNNs) are used to analyze and process sequences of data, which is particularly useful in the context of document quality assessment and non-conformance detection in quality management systems. These networks are able to account for temporal dependencies and complex relationships between different data elements, which improves the accuracy and efficiency of automated decisions. The project was supported by a grant from the Ministry of Education and Science of the Republic of Kazakhstan under the Zhas Galym project No. AR 13268939, dedicated to research and development of digital technologies to ensure consistency of QMS regulatory documents.

Keywords: automated control system, quality management, document structure, formal language

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Authors: Alexandra Amling, Emina Sadic

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The migration routes linking the Horn of Africa to Europe shift in response to political and humanitarian developments across the region. Abrupt changes to those routes can have profound effects on the relative ease of movement and the well-being of migrants. Somali migrants have traditionally been able to rely on a sophisticated, well-established, and reliable network of smugglers to facilitate their journey through the Sahel to Libya, but changes to the routes have undermined those networks. Recently, these shifts have made the journey from Somalia to Europe much more perilous. As the Libyan coast guard intensifies its efforts to stymie boats leaving its coast for Italian shores, arrivals in Spain are trending upwards. This paper thus, will examine how the instability in transit countries that are most commonly used by Somali migrants has had an impact on the reliability of their massive network of smuggling, and how resurgence in the Western route toward Spain provides a potentially new opportunity to reach Europe—a route that has rarely been used by the Somali migrant population in the past. First, the paper will discuss what scholars have called the pastoralist, nomadic tradition of Somalis which reportedly has allowed them to endure the long journeys from Somalia to their chosen destinations. Facilitated by relatives or clan affiliation, Somali migrants have historically been able to rely on a smuggling network that – at least tangentially – provided more security nets during their travels. Given the violence and chaos that unfolded both in Libya and Yemen in 2011 and 2015, respectively, the paper will, secondly, examine which actors in smuggling hubs increase the vulnerabilities of Somalis, pushing them to consider other routes. As a result, this paper will consider to what extent Somalis could follow the stream of other migrants to Algeria and Morocco to enter Europe via Spain. By examining one particular group of migrants and the nature and limitations of the networks associated with their movements, the paper will demonstrate the resilience and adaptability of both the migrants and the networks regardless of the ever-changing nature of migration routes and actors.

Keywords: Europe, migration, smuggling networks, Somalia

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Authors: Hend Alrasheed

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Due to their different properties that have led to avoid several limitations of classic client/server systems, there has been a great interest in the development and the improvement of different peer to peer systems. Understanding the properties of complex peer to peer networks is essential for their future improvements. It was shown that the performances of peer to peer protocols are directly related to their underlying topologies. Therefore, multiple efforts have analyzed the topologies of different peer to peer systems. This study presents an overview of major findings of close experimental analyses to different topologies of three unstructured peer to peer systems: BitTorrent, Gnutella, and FreeNet.

Keywords: peer to peer networks, network topology, graph diameter, clustering coefficient, small-world property, random graph, degree distribution

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Authors: Abdullah Almowanes

Abstract:

The speed of adoption of new information and communication technologies is often seen as an indicator of the growth of knowledge- and technological innovation-based regional economies. Indeed, technological progress and scientific inquiry in any society have undergone a particularly profound transformation with the introduction of computer networks. In the spring of 1981, the Bitnet network was launched to link thousands of nodes all over the world. In 1985 and as one of the first adopters of Bitnet, Saudi Arabia launched a Bitnet-based network named Gulfnet that linked computer centers, universities, and libraries of Saudi Arabia and other Gulf countries through high speed communication lines. In this paper, the origins and the deployment of Gulfnet are discussed as well as social, economical, political, and cultural ramifications of the new information reality created by the network. Despite its significance, the social and cultural aspects of Gulfnet have not been investigated in history of science and technology literature to a satisfactory degree before. The presented research is based on an extensive archival research aimed at seeking out and analyzing of primary evidence from archival sources and records. During its decade and a half-long existence, Gulfnet demonstrated that the scope and functionality of public computer networks in Saudi Arabia have to be fine-tuned for compliance with Islamic culture and political system of the country. It also helped lay the groundwork for the subsequent introduction of the Internet. Since 1980s, in just few decades, the proliferation of computer networks has transformed communications world-wide.

Keywords: Bitnet, computer networks, computing and culture, Gulfnet, Saudi Arabia

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Authors: Judith Margaret Tymon

Abstract:

As cities struggle to incorporate informal settlements into the fabric of urban areas, the focus has often been on the provision of housing. This study explores the underlying structure of street networks, with the goal of understanding the morphogenesis of informal settlements through the lens of the access network. As the stages of development progress from infill to consolidation and eventually, to a planned in-situ settlement, the access networks retain the form of the core segments; however, a majority of street patterns are adapted to a grid design to support infrastructure in the final upgraded phase. A case study is presented to examine the street network in the informal settlement of Gobabis Namibia as it progresses from its initial stages to a planned, in-situ, and permanently upgraded development. The Informal Development Stages framework of foundation, infill, and consolidation, as developed by Dr. Jota Samper, is utilized to examine the evolution of street networks. Data is gathered from historical Google Earth satellite images for the time period between 2003 and 2022. The results demonstrate that during the foundation through infill stages, incremental changes follow similar patterns, with pathways extended, lengthened, and densified as housing is created and the settlement grows. In the final stage of consolidation, the resulting street layout is transformed to support the installation of infrastructure; however, some elements of the original street patterns remain. The core pathways remain intact to accommodate the installation of infrastructure and the creation of housing plots, defining the shape of the settlement and providing the basis of the urban form. The adaptations, growth, and consolidation of the street network are critical to the eventual formation of the spatial layout of the settlement. This study will include a comparative analysis of findings with those of recent research performed by Kamalipour, Dovey, and others regarding incremental urbanism within informal settlements. Further comparisons will also include studies of street networks of well-established urban centers that have shown links between the morphogenesis of access networks and the eventual spatial layout of the city. The findings of the study can be used to guide and inform strategies for in-situ upgrading and can contribute to the sustainable development of informal settlements.

Keywords: Gobabis Namibia, incremental urbanism, informal development stages, informal settlements, street networks

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Authors: Wipada Chaiwchan

Abstract:

This research aims to study behaviors in using social networks of Corporate personnel of Suan Sunandha Rajabhat University. The sample used in the study were two groups: 1) Academic Officer 70 persons and 2) Operation Officer 143 persons were used in this study. The tools in this research consisted of questionnaire which the data were analyzed by using percentage, average (X) and Standard deviation (S.D.) and Independent Sample T-Test to test the difference between the mean values obtained from two independent samples, and One-way anova to analysis of variance, and Multiple comparisons to test that the average pair of different methods by Fisher’s Least Significant Different (LSD). The study result found that the most of corporate personnel have purpose in using social network to information awareness aspect was knowledge and online conference with social media. By using the average more than 3 hours per day in everyday. Using time in working in one day and there are computers connected to the Internet at home, by using the communication in the operational processes. Behaviors using social networks in relation to gender, age, job title, department, and type of personnel. Hypothesis testing, and analysis of variance for the effects of this analysis is divided into three aspects: The use of online social networks, the attitude of the users and the security analysis has found that Corporate Personnel of Suan Sunandha Rajabhat University. Overall and specifically at the high level, and considering each item found all at a high level. By sorting of the social network (X=3.22), The attitude of the users (X= 3.06) and the security (X= 3.11). The overall behaviors using of each side (X=3.11).

Keywords: social network, behaviors, social media, computer information systems

Procedia PDF Downloads 395