Search results for: SIP server clusters

Authors: Lillian Musila, Valerie Oundo, Daniel Erwin, Willie Sang

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Staphylococcus aureus infections are a major cause of nosocomial infections in Kenya. Methicillin resistant S. aureus (MRSA) infections are a significant burden to public health and are associated with considerable morbidity and mortality. At a hospital in Western Kenya two clusters of MRSA cases emerged within short periods of time. In this study we explored whether these clusters represented a nosocomial outbreak by characterizing the isolates using phenotypic and molecular assays and examining epidemiological data to identify possible transmission patterns. Specimens from the site of infection of the subjects were collected, cultured and S. aureus isolates identified phenotypically and confirmed by APIStaph™. MRSA were identified by cefoxitin disk screening per CLSI guidelines. MRSA were further characterized based on their antibiotic susceptibility patterns and spa gene typing. Characteristics of cases with MRSA isolates were compared with those with MSSA isolated around the same time period. Two cases of MRSA infection were identified in the two week period between 21 April and 4 May 2015. A further 2 MRSA isolates were identified on the same day on 7 September 2015. The antibiotic resistance patterns of the two MRSA isolates in the 1st cluster of cases were different suggesting that these were distinct isolates. One isolate had spa type t2029 and the other had a novel spa type. The 2 isolates were obtained from urine and an open skin wound. In the 2nd cluster of MRSA isolates, the antibiotic susceptibility patterns were similar but isolates had different spa types: one was t037 and the other a novel spa type different from the novel MRSA spa type in the first cluster. Both cases in the second cluster were admitted into the hospital but one infection was community- and the other hospital-acquired. Only one of the four MRSA cases was classified as an HAI from an infection acquired post-operatively. When compared to other S. aureus strains isolated within the same time period from the same hospital only one spa type t2029 was found in both MRSA and non-MRSA strains. None of the cases infected with MRSA in the two clusters shared any common epidemiological characteristic such as age, sex or known risk factors for MRSA such as prolonged hospitalization or institutionalization. These data suggest that the observed MRSA clusters were multi strain clusters and not an outbreak of a single strain. There was no clear relationship between the isolates by spa type suggesting that no transmission was occurring within the hospital between these cluster cases but rather that the majority of the MRSA strains were circulating in the community. There was high diversity of spa types among the MRSA strains with none of the isolates sharing spa types. Identification of disease clusters in space and time is critical for immediate infection control action and patient management. Spa gene typing is a rapid way of confirming or ruling out MRSA outbreaks so that costly interventions are applied only when necessary.

Keywords: cluster, Kenya, MRSA, spa typing

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Authors: M. F. Miah, K. M. A. Zinnah, M. J. Raihan, H. Ali, M. N. Naser

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As genetic diversity is most important for existing, breeding and production of any fish; this study was undertaken for investigating genetic diversity of freshwater mud eel, Monopterus cuchia at population level where three ecological populations such as flooded area of Sylhet (P1), open water of Moulvibazar (P2) and open water of Sunamganj (P3) districts of Bangladesh were considered. Four arbitrary RAPD primers (OPB-12, C0-4, B-03 and OPB-08) were screened and RAPD banding patterns were analyzed among the populations considering 15 individuals of each population. In total 174, 138 and 149 bands were detected in the populations of P1, P2 and P3 respectively; however, each primer revealed less number of bands in each population. 100% polymorphic loci were recorded in P2 and P3 whereas only one monomorphic locus was observed in P1, recorded 97.5% polymorphism. Different genetic parameters such as inter-individual pairwise similarity, genetic distance, Nei genetic similarity, linkage distances, cluster analysis and allelic information, etc. were considered for measuring genetic diversity. The average inter-individual pairwise similarity was recorded 2.98, 1.47 and 1.35 in P1, P2 and P3 respectively. Considering genetic distance analysis, the highest distance 1 was recorded in P2 and P3 and the lowest genetic distance 0.444 was found in P2. The average Nei genetic similarity was observed 0.19, 0.16 and 0.13 in P1, P2 and P3, respectively; however, the average linkage distance was recorded 24.92, 17.14 and 15.28 in P1, P3 and P2 respectively. Based on linkage distance, genetic clusters were generated in three populations where 6 clades and 7 clusters were found in P1, 3 clades and 5 clusters were observed in P2 and 4 clades and 7 clusters were detected in P3. In addition, allelic information was observed where the frequency of p and q alleles were observed 0.093 and 0.907 in P1, 0.076 and 0.924 in P2, 0.074 and 0.926 in P3 respectively. The average gene diversity was observed highest in P2 (0.132) followed by P3 (0.131) and P1 (0.121) respectively.

Keywords: genetic diversity, Monopterus cuchia, population, RAPD, Bangladesh

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Authors: Laura A. Rodriguez-Villamizar, Alvaro Osornio-Vargas, Brian H. Rowe, Rhonda J. Rosychuk

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Introduction/Objectives: The Census Metropolitan Area of Edmonton (CMAE) has important industrial emissions to the air from the Industrial Heartland Alberta (IHA) at the Northeast and the coal-fired power plants (CFPP) at the West. The objective of the study was to explore the presence of clusters of children asthma ED visits in the areas around the IHA and the CFPP. Methods: Retrospective data on children asthma ED visits was collected at the dissemination area (DA) level for children between 2 and 14 years of age, living in the CMAE between April 1, 2004, and March 31, 2010. We conducted a spatial analysis of disease clusters around putative sources with count (ecological) data using descriptive, hypothesis testing, and multivariable modeling analysis. Results: The mean crude rate of asthma ED visits was 9.3/1,000 children population per year during the study period. Circular spatial scan test for cases and events identified a cluster of children asthma ED visits in the DA where the CFPP are located in the Wabamum area. No clusters were identified around the IHA area. The multivariable models suggest that there is a significant decline in risk for children asthma ED visits as distance increases around the CFPP area this effect is modified at the SE direction with mean angle 125.58 degrees, where the risk increases with distance. In contrast, the regression models for IHA suggest that there is a significant increase in risk for children asthma ED visits as distance increases around the IHA area and this effect is modified at SW direction with mean angle 216.52 degrees, where the risk increases at shorter distances. Conclusions: Different methods for detecting clusters of disease consistently suggested the existence of a cluster of children asthma ED visits around the CFPP but not around the IHA within the CMAE. These results are probably explained by the direction of the air pollutants dispersion caused by the predominant and subdominant wind direction at each point. The use of different approaches to detect clusters of disease is valuable to have a better understanding of the presence, shape, direction and size of clusters of disease around pollution sources.

Keywords: air pollution, asthma, disease cluster, industry

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Authors: Mohammad Rostami, Mohammad Reza Forghani, Elahe Neshat, Fatemeh Yaghoobi

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An Ad Hoc network consists of wireless mobile equipment which connects to each other without any infrastructure, using connection equipment. The best way to form a hierarchical structure is clustering. Various methods of clustering can form more stable clusters according to nodes' mobility. In this research we propose an algorithm, which allocates some weight to nodes based on factors, i.e. link stability and power reduction rate. According to the allocated weight in the previous phase, the cellular learning automaton picks out in the second phase nodes which are candidates for being cluster head. In the third phase, learning automaton selects cluster head nodes, member nodes and forms the cluster. Thus, this automaton does the learning from the setting and can form optimized clusters in terms of power consumption and link stability. To simulate the proposed algorithm we have used omnet++4.2.2. Simulation results indicate that newly formed clusters have a longer lifetime than previous algorithms and decrease strongly network overload by reducing update rate.

Keywords: mobile Ad Hoc networks, clustering, learning automaton, cellular automaton, battery power

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Authors: A. S. Grewal, R. S. Sangwan, Dharambir, Vikas Dhanda

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The purpose of this paper is to develop and analyze two reliability models for a system of non-identical firearms – one is standard firearm (called as original unit) and the other is a country-made firearm (called as duplicate /substandard unit). There is a single server who comes immediately to do inspection and repair whenever needed. On the failure of standard firearm, the server inspects the operative country-made firearm to see whether the unit is capable of performing the desired function well or not. If country-made firearm is not capable to do so, the operation of the system is stopped and server starts repair of the standard firearms immediately. However, no inspection is done at the failure of the country-made firearm as the country-made firearm alone is capable of performing the given task well. In model I, priority to repair the standard firearm is given in case system fails completely and country-made firearm is already under repair, whereas in model II there is no such priority. The failure and repair times of each unit are assumed to be independent and uncorrelated random variables. The distributions of failure time of the units are taken as negative exponential while that of repair and inspection times are general. By using semi-Markov process and regenerative point technique some econo-reliability measures are obtained. Graphs are plotted to compare the MTSF (mean time to system failure), availability and profit of the models for a particular case.

Keywords: non-identical firearms, inspection, priority to repair, semi-Markov process, regenerative point

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Authors: Adhithy Menon E., Biju C. A.

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In the 1960s, the concept of preserving heritage monuments was first introduced. During the 1990s, the concept of cultural landscapes gained importance, highlighting the importance of culture and heritage. Throughout this paper, we examine the second category of the cultural landscape, which is an organically evolving landscape as it represents a web of tangible, intangible, and ecological heritage and the ways in which they can be rejuvenated. Cultural landscapes in various regions, such as the Chettinad Village clusters, are in serious decline, which is identified through the Heritage Passport program of this area (2007). For this reason, it is necessary to conduct a detailed analysis of the factors that contribute to this degradation to ensure its protection in the future. An analysis of the cultural landscape of the Chettinad Village clusters and its impact on the community is presented in this paper. The paper follows the first objective, which is to understand cultural landscapes and their different criteria and categories. It is preceded by the study of various methods for protecting cultural landscapes. To identify a core area of intervention based on the parameters of Cultural Landscapes and Community Based Tourism, a study and analysis of the regional context of Chettinad village clusters considering tourism development must first be conducted. Lastly, planning interventions for integrating community-based tourism in Chettinad villages for the purpose of rejuvenating the cultural landscapes of the villages as well as their communities. The major findings include the importance of the local community in protecting cultural landscapes. The parameters identified to have an impact on Chettinad Village clusters are a community (community well-being, local maintenance, and enhancement, demand, alternative income for community, public participation, awareness), tourism (location and physical access, journey time, tourist attractions), integrity (natural factors, natural disasters, demolition of structures, deterioration of materials) authenticity (sense of place, living elements, building techniques, artistic expression, religious context) disaster management (natural disasters) and environmental impact (pollution). This area can be restored to its former glory and preserved as part of the cultural landscape for future generations by focusing on and addressing these parameters within the identified core area of the Chettinad Villages cluster (Kanadukathan TP, Kothamangalam, Kottaiyur, Athangudi, Karikudi, and Palathur).

Keywords: Chettinad village clusters, community, cultural landscapes, organically evolved.

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Authors: Yi-Cheng Tian, Miin-Shen Yang

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The mixture likelihood approach to clustering is a popular clustering method where the expectation and maximization (EM) algorithm is the most used mixture likelihood method. In the literature, the EM algorithm had been used for mixture regression models. However, these EM mixture regression algorithms are sensitive to initial values with a priori number of clusters. In this paper, to resolve these drawbacks, we construct a learning-based schema for the EM mixture regression algorithm such that it is free of initializations and can automatically obtain an approximately optimal number of clusters. Some numerical examples and comparisons demonstrate the superiority and usefulness of the proposed learning-based EM mixture regression algorithm.

Keywords: clustering, EM algorithm, Gaussian mixture model, mixture regression model

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Authors: Ouafa Amira, Jiangshe Zhang

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Clustering is an unsupervised machine learning technique; its aim is to extract the data structures, in which similar data objects are grouped in the same cluster, whereas dissimilar objects are grouped in different clusters. Clustering methods are widely utilized in different fields, such as: image processing, computer vision , and pattern recognition, etc. Fuzzy c-means clustering (fcm) is one of the most well known fuzzy clustering methods. It is based on solving an optimization problem, in which a minimization of a given cost function has been studied. This minimization aims to decrease the dissimilarity inside clusters, where the dissimilarity here is measured by the distances between data objects and cluster centers. The degree of belonging of a data point in a cluster is measured by a membership function which is included in the interval [0, 1]. In fcm clustering, the membership degree is constrained with the condition that the sum of a data object’s memberships in all clusters must be equal to one. This constraint can cause several problems, specially when our data objects are included in a noisy space. Regularization approach took a part in fuzzy c-means clustering technique. This process introduces an additional information in order to solve an ill-posed optimization problem. In this study, we focus on regularization by relative entropy approach, where in our optimization problem we aim to minimize the dissimilarity inside clusters. Finding an appropriate membership degree to each data object is our objective, because an appropriate membership degree leads to an accurate clustering result. Our clustering results in synthetic data sets, gaussian based data sets, and real world data sets show that our proposed model achieves a good accuracy.

Keywords: clustering, fuzzy c-means, regularization, relative entropy

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Authors: Baljit Singh Khehra, Amar Partap Singh Pharwaha

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Clusters of Microcalcifications (MCCs) are most frequent symptoms of Ductal Carcinoma in Situ (DCIS) recognized by mammography. Least-Square Support Vector Machine (LS-SVM) is a variant of the standard SVM. In the paper, LS-SVM is proposed as a classifier for classifying MCCs as benign or malignant based on relevant extracted features from enhanced mammogram. To establish the credibility of LS-SVM classifier for classifying MCCs, a comparative evaluation of the relative performance of LS-SVM classifier for different kernel functions is made. For comparative evaluation, confusion matrix and ROC analysis are used. Experiments are performed on data extracted from mammogram images of DDSM database. A total of 380 suspicious areas are collected, which contain 235 malignant and 145 benign samples, from mammogram images of DDSM database. A set of 50 features is calculated for each suspicious area. After this, an optimal subset of 23 most suitable features is selected from 50 features by Particle Swarm Optimization (PSO). The results of proposed study are quite promising.

Keywords: clusters of microcalcifications, ductal carcinoma in situ, least-square support vector machine, particle swarm optimization

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Authors: Mukesh Khadka, Alexander Paz, Hanns de la Fuente-Mella

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Typically, pavement performance models are developed in two steps: (i) pavement segments with similar characteristics are grouped together to form a cluster, and (ii) the corresponding performance models are developed using statistical techniques. A challenge is to select the characteristics that define clusters and the segments associated with them. If inappropriate characteristics are used, clusters may include homogeneous segments with different performance behavior or heterogeneous segments with similar performance behavior. Prediction accuracy of performance models can be improved by grouping the pavement segments into more uniform clusters by including both characteristics and a performance measure. This grouping is not always possible due to limited information. It is impractical to include all the potential significant factors because some of them are potentially unobserved or difficult to measure. Historical performance of pavement segments could be used as a proxy to incorporate the effect of the missing potential significant factors in clustering process. The current state-of-the-art proposes Clusterwise Linear Regression (CLR) to determine the pavement clusters and the associated performance models simultaneously. CLR incorporates the effect of significant factors as well as a performance measure. In this study, a mathematical program was formulated for CLR models including multiple explanatory variables. Pavement data collected recently over the entire state of Nevada were used. International Roughness Index (IRI) was used as a pavement performance measure because it serves as a unified standard that is widely accepted for evaluating pavement performance, especially in terms of riding quality. Results illustrate the advantage of the using CLR. Previous studies have used CLR along with experimental data. This study uses actual field data collected across a variety of environmental, traffic, design, and construction and maintenance conditions.

Keywords: clusterwise regression, pavement management system, performance model, optimization

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Authors: Ana Paula Lisboa Sohn, Filipa Dionísio Vieria, Nelson Casarotto, Idaulo José Cunha

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This paper aims at identifying and analyzing the knowledge transmission channels in textile and clothing clusters located in Brazil and in Europe. Primary data was obtained through interviews with key individuals. The collection of primary data was carried out based on a questionnaire with ten categories of indicators of knowledge transmission. Secondary data was also collected through a literature review and through international organizations sites. Similarities related to the use of the main transmission channels of knowledge are observed in all cases. The main similarities are: influence of suppliers of machinery, equipment and raw materials; imitation of products and best practices; training promoted by technical institutions and businesses; and cluster companies being open to acquire new knowledge. The main differences lie in the relationship between companies, where in Europe the intensity of this relationship is bigger when compared to Brazil. The differences also occur in importance and frequency of the relationship with the government, with the cultural environment, and with the activities of research and development. It is also found factors that reduce the importance of geographical proximity in transmission of knowledge, and in generating trust and the establishment of collaborative behavior.

Keywords: industrial clusters, interorganizational learning, knowledge transmission channels, textile and clothing industry

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Authors: Olumide Simeon Ogunnusi, Shukor Abd Razak, Michael Kolade Adu

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Inter-agent communication manager facilitates communication among mobile agents via message passing mechanism. Until now, all Foundation for Intelligent Physical Agents (FIPA) compliant agent systems are capable of exchanging messages following the standard format of sending and receiving messages. Previous works tend to secure messages to be exchanged among a community of collaborative agents commissioned to perform specific tasks using cryptosystems. However, the approach is characterized by computational complexity due to the encryption and decryption processes required at the two ends. The proposed approach to secure agent communication allows only agents that are created by the host agent server to communicate via the agent communication channel provided by the host agent platform. These agents are assumed to be harmless. Therefore, to secure communication of legitimate agents from intrusion by external agents, a 2-phase policy enforcement system was developed. The first phase constrains the external agent to run only on the network server while the second phase confines the activities of the external agent to its execution environment. To implement the proposed policy, a controller agent was charged with the task of screening any external agent entering the local area network and preventing it from migrating to the agent execution host where the legitimate agents are running. On arrival of the external agent at the host network server, an introspector agent was charged to monitor and restrain its activities. This approach secures legitimate agent communication from Man-in-the Middle and Replay attacks.

Keywords: agent communication, introspective agent, isolation of agent, policy enforcement system

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Authors: U. K. Hulihalli, Shantveerayya

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Buckwheat (Fagopyrum esculentum Moench) is an annual crop belongs to family Poligonaceae. The cultivated buckwheat species are notable for their exceptional nutritive values. It is an important source of carbohydrates, fibre, macro, and microelements such as K, Ca, Mg, Na and Mn, Zn, Se, and Cu. It also contains rutin, flavonoids, riboflavin, pyridoxine and many amino acids which have beneficial effects on human health, including lowering both blood lipid and sugar levels. Rutin, quercetin and some other polyphenols are potent carcinogens against colon and other cancers. Buckwheat has significant nutritive value and plenty of uses. Cultivation of buckwheat in Sothern part of India is very meager. Hence, a study was planned with an objective to know the performance of buckwheat genotypes to different planting geometries and fertility levels. The field experiment was conducted at Main Agriculture Research Station, University of Agriculture Sciences, Dharwad, India, during 2017 Kharif. The experiment was laid-out in split-plot design with three replications having three planting geometries as main plots, two genotypes as sub plots and three fertility levels as sub-sub plot treatments. The soil of the experimental site was vertisol. The standard procedures are followed to record the observations. The planting geometry of 30*10 cm was recorded significantly higher seed yield (893 kg/ha⁻¹), stover yield (1507 kg ha⁻¹), clusters plant⁻¹ (7.4), seeds clusters⁻¹ (7.9) and 1000 seed weight (26.1 g) as compared to 40*10 cm and 20*10 cm planting geometries. Between the genotypes, significantly higher seed yield (943 kg ha⁻¹) and harvest index (45.1) was observed with genotype IC-79147 as compared to PRB-1 genotype (687 kg ha⁻¹ and 34.2, respectively). However, the genotype PRB-1 recorded significantly higher stover yield (1344 kg ha⁻¹) as compared to genotype IC-79147 (1173 kg ha⁻¹). The genotype IC-79147 was recorded significantly higher clusters plant⁻¹ (7.1), seeds clusters⁻¹ (7.9) and 1000 seed weight (24.5 g) as compared PRB-1 (5.4, 5.8 and 22.3 g, respectively). Among the fertility levels tried, the fertility level of 60:30 NP kg ha⁻¹ recorded significantly higher seed yield (845 kg ha^-1) and stover yield (1359 kg ha⁻¹) as compared to 40:20 NP kg ha^-1 (808 and 1259 kg ha⁻¹ respectively) and 20:10 NP kg ha^-1 (793 and 1144 kg ha⁻¹ respectively). Within the treatment combinations, IC 79147 genotype having 30*10 cm planting geometry with 60:30 NP kg ha⁻¹ recorded significantly higher seed yield (1070 kg ha⁻¹), clusters plant⁻¹ (10.3), seeds clusters⁻¹ (9.9) and 1000 seed weight (27.3 g) compared to other treatment combinations.

Keywords: buckwheat, planting geometry, genotypes, fertility levels

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Authors: Shaban Guma, Naim Bajcinca

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We propose an event-triggered algorithm for the solution of a distributed optimization problem by means of the projected subgradient method. Thereby, we invoke an OFDMA resource allocation scheme by applying an event-triggered sensitivity analysis at the access point. The optimal resource assignment of the subcarriers to the involved wireless nodes is carried out by considering the sensitivity analysis of the overall objective function as defined by the control of vehicle clusters with respect to the information exchange between the nodes.

Keywords: consensus, cross-layer, distributed, event-triggered, multi-vehicle, protocol, resource, OFDMA, wireless

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Authors: Akanksha Pareek

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The Indian Handloom clusters are known for its tradition and heritage of excellent craftsmanship. The design development of Indian handloom clusters are oriented on traditionally dobby and jacquard design. This comprehensive paper proposes practises on handloom woven design based on primary colour gradation with the help of basic weaved on four shaft. The innovative design ideas are inspired from Nature and transferred into the handloom samples to achieve colour gradation with primary colours. In this paper, design methodology where in woven samples are strategically designed in such way that traditional knowledge of the weavers will be oriented to leveraged their skills.

Keywords: handloom, weaving, colour gradation, shaft

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Authors: Hamza Javar Magnier, Leslie V. Woodcock

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It has been reported that at temperatures above the critical there is no “continuity of liquid and gas”, as originally hypothesized by van der Waals. Rather, both gas and liquid phases, with characteristic properties as such, extend to supercritical temperatures. Each phase is bounded by the locus of a percolation transition, i.e. a higher-order thermodynamic phase change associated with percolation of gas clusters in a large void, or liquid interstitial vacancies in a large cluster. Between these two-phase bounds, it is reported there exists a mesophase that resembles an otherwise homogeneous dispersion of gas micro-bubbles in liquid (foam) and a dispersion of liquid micro-droplets in gas (mist). Such a colloidal-like state of a pure one-component fluid represents a hitherto unchartered equilibrium state of matter besides pure solid, liquid or gas. Here we provide compelling evidence, from molecular dynamics (MD) simulations, for the existence of this supercritical mesophase and its colloidal nature. We report preliminary results of computer simulations for a model fluid using a simplistic representation of atoms or molecules, i.e. a hard-core repulsion with an attraction so short that the atoms are referred to as “adhesive spheres”. Molecular clusters, and hence percolation transitions, are unambiguously defined. Graphics of color-coded clusters show colloidal characteristics of the supercritical mesophase.

Keywords: critical phenomena, mesophase, supercritical, square-well, critical parameters

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Authors: Markus Mazzola, Gunther Schaaf

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A vehicle driving with an Adaptive Cruise Control System (ACC) is usually controlled decentrally, based on the information of radar systems and in some publications based on C2X-Communication (CACC) to guarantee stable platoons. In this paper, we present a Model Predictive Control (MPC) design of a centralized, server-based ACC-System, whereby the vehicular platoon is modeled and controlled as a whole. It is then proven that the proposed MPC design guarantees asymptotic stability and hence string stability of the platoon. The Networked MPC design is chosen to be able to integrate system constraints optimally as well as to reduce the effects of communication delay and packet loss. The performance of the proposed controller is then simulated and analyzed in an LTE communication scenario using the LTE/EPC Network Simulator LENA, which is based on the ns-3 network simulator.

Keywords: adaptive cruise control, centralized server, networked model predictive control, string stability

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Authors: Ágoston Nagy

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This paper introduces a method of using unsupervised machine learning to visualize the feature space of a dataset in 2D, in order to find most characteristic segments in the set. After dimension reduction, users can select clusters by manual drawing. Selected clusters are recorded into a data model that is used for later predictions, based on realtime data. Predictions are made with supervised learning, using Gesture Recognition Toolkit. The paper introduces two example applications: a semantic audio organizer for analyzing incoming sounds, and a gesture database organizer where gestural data (recorded by a Leap motion) is visualized for further manipulation.

Keywords: gesture recognition, machine learning, real-time interaction, visualization

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Authors: Beulah Rani Inbanathan

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In the present era, it is vivid from the numerous outcomes that data privacy is being compromised in various ways. Machine learning is one technology that uses the centralized server, and then data is given as input which is being analyzed by the algorithms present on this mentioned server, and hence outputs are predicted. However, each time the data must be sent by the user as the algorithm will analyze the input data in order to predict the output, which is prone to threats. The solution to overcome this issue is federated learning, where the models alone get updated while the data resides on the local machine and does not get exchanged with the other local models. Nevertheless, even on these local models, there are chances of data poisoning, and it is crystal clear from various experiments done by many people. This paper delves into many ways where data poisoning occurs and the many methods through which it is prevalent that data poisoning still exists. It includes the poisoning attacks on IoT devices, Edge devices, Autoregressive model, and also, on Industrial IoT systems and also, few points on how these could be evadible in order to protect our data which is personal, or sensitive, or harmful when exposed.

Keywords: data poisoning, federated learning, Internet of Things, edge computing

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Authors: Mohammad Alawairdhi

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Customer Relationship Management (CRM) started gaining attention as late as the 1990s, and since then efforts are ongoing to define the domain’s precise specifications. There is yet no single agreed upon definition. However, a predominant majority perceives CRM as a mechanism for enhancing interaction with customers, thereby strengthening the relationship between a business and its clients. From the perspective of Information Technology (IT) companies, CRM systems can be viewed as facilitating software products or services to automate the marketing, selling and servicing functions of an organization. In this paper, we have proposed a Bluetooth enabled CRM system for small- and medium-scale organizations. In the proposed system, Bluetooth technology works as an automatic identification token in addition to its common use as a communication channel. The system comprises a server side accompanied by a user-interface support for both client and server sides. The system has been tested in two environments and users have expressed ease of use, convenience and understandability as major advantages of the proposed solution.

Keywords: customer relationship management, CRM, bluetooth, automatic identification token

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Authors: Nidhi Gaur, Padmaja Joshi, Vijay Jain, Rajeev Srivastava

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Web application architecture is important to achieve the desired performance for the application. Performance analysis studies are conducted to evaluate existing or planned systems. Web applications are used by hundreds of thousands of users simultaneously, which sometimes increases the risk of server failure in real time operations. We use Coloured Petri Net (CPN), a very powerful tool for modelling dynamic behaviour of a web application system. CPNs extend the vocabulary of ordinary Petri nets and add features that make them suitable for modelling large systems. The major focus of this work is on server side of web applications. The presented work focuses on modelling restructuring aspects, with major focus on concurrency and architecture, using CPN. It also focuses on bringing out the appropriate architecture for web and database servers given the number of concurrent users.

Keywords: coloured Petri Nets (CPNs), concurrent users, per- formance modelling, web application architecture

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Authors: Romeo Botes, Imelda Smit

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In today’s modern age of technology vast amounts of data needs to be processed in real-time to keep users satisfied. This data comes from various sources and in many formats, including electronic and mobile devices such as GPRS modems and GPS devices. They make use of different protocols including TCP, UDP, and HTTP/s for data communication to web servers and eventually to users. The data obtained from these devices may provide valuable information to users, but are mostly in an unreadable format which needs to be processed to provide information and business intelligence. This data is not always current, it is mostly historical data. The data is not subject to implementation of consistency and redundancy measures as most other data usually is. Most important to the users is that the data are to be pre-processed in a readable format when it is entered into the database. To accomplish this, programmers build processing programs and scripts to decode and process the information stored in databases. Programmers make use of various techniques in such programs to accomplish this, but sometimes neglect the effect some of these techniques may have on database performance. One of the techniques generally used,is to pull data from the database server, process it and push it back to the database server in one single step. Since the processing of the data usually takes some time, it keeps the database busy and locked for the period of time that the processing takes place. Because of this, it decreases the overall performance of the database server and therefore the system’s performance. This paper follows on a paper discussing the performance increase that may be achieved by utilizing array lists along with a pull-process-push data processing technique split in three steps. The purpose of this paper is to expand the number of clients when comparing the two techniques to establish the impact it may have on performance of the CPU storage and processing time.

Keywords: performance measures, algorithm techniques, data processing, push data, process data, array list

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Authors: M. I. Abdelhamid, A. O. Ghallab, R. S. Ettouney, M. A. El-Rifai

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An optimization scheme based on COM server is suggested for communication between Genetic Algorithm (GA) toolbox of MATLAB and Aspen HYSYS. The structure and details of the proposed framework are discussed. The power of the developed scheme is illustrated by its application to the optimization of a recently developed natural gas liquefaction process in which Aspen HYSYS was used for minimization of the power consumption by optimizing the values of five operating variables. In this work, optimization by coupling between the GA in MATLAB and Aspen HYSYS model of the same process using the same five decision variables enabled improvements in power consumption by 3.3%, when 77% of the natural gas feed is liquefied. Also on inclusion of the flow rates of both nitrogen and carbon dioxide refrigerants as two additional decision variables, the power consumption decreased by 6.5% for a 78% liquefaction of the natural gas feed.

Keywords: stranded gas liquefaction, genetic algorithm, COM server, single nitrogen expansion, carbon dioxide pre-cooling

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Authors: Saurabh Shukla, G. N. Pandey

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In recent times, oil exploration and monitoring in on-shore areas have gained much importance considering the fact that in India the oil import is 62 percent of the total imports. Thus, architectural model like wireless sensor network to monitor on-shore deep sea oil well is being developed to get better estimate of the oil prospects. The problem we are facing nowadays that we have very few restricted areas of oil left today. Countries like India don’t have much large areas and resources for oil and this problem with most of the countries that’s why it has become a major problem when we are talking about oil exploration in on-shore areas also the increase of oil prices has further ignited the problem. For this the use of wireless network system having relative simplicity, smallness in size and affordable cost of wireless sensor nodes permit heavy deployment in on-shore places for monitoring oil wells. Deployment of wireless sensor network in large areas will surely reduce the cost it will be very much cost effective. The objective of this system is to send real time information of oil monitoring to the regulatory and welfare authorities so that suitable action could be taken. This system architecture is composed of sensor network, processing/transmission unit and a server. This wireless sensor network system could remotely monitor the real time data of oil exploration and monitoring condition in the identified areas. For wireless sensor networks, the systems are wireless, have scarce power, are real-time, utilize sensors and actuators as interfaces, have dynamically changing sets of resources, aggregate behaviour is important and location is critical. In this system a communication is done between the server and remotely placed sensors. The server gives the real time oil exploration and monitoring conditions to the welfare authorities.

Keywords: sensor, wireless sensor network, oil, sensor, on-shore level

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Authors: Arup K. Sarma, Jayshree Hazarika

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The North-eastern part of India, which receives heavier rainfall than other parts of the subcontinent, is of great concern now-a-days with regard to climate change. High intensity rainfall for short duration and longer dry spell, occurring due to impact of climate change, affects river morphology too. In the present study, an attempt is made to delineate the North-Eastern region of India into some homogeneous clusters based on the Fuzzy Clustering concept and to compare the resulting clusters obtained by using conventional methods and non conventional methods of clustering. The concept of clustering is adapted in view of the fact that, impact of climate change can be studied in a homogeneous region without much variation, which can be helpful in studies related to water resources planning and management. 10 IMD (Indian Meteorological Department) stations, situated in various regions of the North-east, have been selected for making the clusters. The results of the Fuzzy C-Means (FCM) analysis show different clustering patterns for different conditions. From the analysis and comparison it can be concluded that non conventional method of using GCM data is somehow giving better results than the others. However, further analysis can be done by taking daily data instead of monthly means to reduce the effect of standardization.

Keywords: climate change, conventional and nonconventional methods of clustering, FCM analysis, homogeneous regions

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Authors: Panita Wannapiroon, Noppadon Phumeechanya, Sitthichai Laisema

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This study is the research and development which is intended to: 1) design the ubiquitous cloud learning management system and: 2) assess the suitability of the design of the ubiquitous cloud learning management system. Its methods are divided into 2 phases. Phase 1 is the design of the ubiquitous cloud learning management system, phase 2 is the assessment of the suitability of the design the samples used in this study are work done by 25 professionals in the field of Ubiquitous cloud learning management systems and information and communication technology in education selected using the purposive sampling method. Data analyzed by arithmetic mean and standard deviation. The results showed that the ubiquitous cloud learning management system consists of 2 main components which are: 1) the ubiquitous cloud learning management system server (u-Cloud LMS Server) including: cloud repository, cloud information resources, social cloud network, cloud context awareness, cloud communication, cloud collaborative tools, and: 2) the mobile client. The result of the system suitability assessment from the professionals is in the highest range.

Keywords: learning management system, cloud computing, ubiquitous learning, ubiquitous learning management system

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Authors: Sangeeta Yadav, Mantosh Biswas

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In this paper, we proposed an improved color based K-mean algorithm for clustering of satellite Image (SAR). Our method comprises of two stages. The first step is an interactive selection process where users are required to input the number of colors (ncolor), number of clusters, and then they are prompted to select the points in each color cluster. In the second step these points are given as input to K-mean clustering algorithm that clusters the image based on color and Minimum Square Euclidean distance. The proposed method reduces the mixed pixel problem to a great extent.

Keywords: cluster, ncolor method, K-mean method, interactive selection process

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Authors: Rohit T. P., Sahil Athrij, Sasi Gopalan

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Currently, the content entertainment industry is dominated by mobile devices. As the trends slowly shift towards Augmented/Virtual Reality applications the computational demands on these devices are increasing exponentially and we are already reaching the limits of hardware optimizations. This paper proposes a software solution to this problem. By leveraging the capabilities of cloud computing we can offload the work from mobile devices to dedicated rendering servers that are way more powerful. But this introduces the problem of latency. This paper introduces a protocol that can achieve high-performance low latency Augmented/Virtual Reality experience. There are two parts to the protocol, 1) In-flight compression The main cause of latency in the system is the time required to transmit the camera frame from client to server. The round trip time is directly proportional to the amount of data transmitted. This can therefore be reduced by compressing the frames before sending. Using some standard compression algorithms like JPEG can result in minor size reduction only. Since the images to be compressed are consecutive camera frames there won't be a lot of changes between two consecutive images. So inter-frame compression is preferred. Inter-frame compression can be implemented efficiently using WebGL but the implementation of WebGL limits the precision of floating point numbers to 16bit in most devices. This can introduce noise to the image due to rounding errors, which will add up eventually. This can be solved using an improved interframe compression algorithm. The algorithm detects changes between frames and reuses unchanged pixels from the previous frame. This eliminates the need for floating point subtraction thereby cutting down on noise. The change detection is also improved drastically by taking the weighted average difference of pixels instead of the absolute difference. The kernel weights for this comparison can be fine-tuned to match the type of image to be compressed. 2) Dynamic Load distribution Conventional cloud computing architectures work by offloading as much work as possible to the servers, but this approach can cause a hit on bandwidth and server costs. The most optimal solution is obtained when the device utilizes 100% of its resources and the rest is done by the server. The protocol balances the load between the server and the client by doing a fraction of the computing on the device depending on the power of the device and network conditions. The protocol will be responsible for dynamically partitioning the tasks. Special flags will be used to communicate the workload fraction between the client and the server and will be updated in a constant interval of time ( or frames ). The whole of the protocol is designed so that it can be client agnostic. Flags are available to the client for resetting the frame, indicating latency, switching mode, etc. The server can react to client-side changes on the fly and adapt accordingly by switching to different pipelines. The server is designed to effectively spread the load and thereby scale horizontally. This is achieved by isolating client connections into different processes.

Keywords: 2D kernelling, augmented reality, cloud computing, dynamic load distribution, immersive experience, mobile computing, motion tracking, protocols, real-time systems, web-based augmented reality application

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Authors: Rasha Al-Saedi, Anthony Meijer

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The progress of new porous materials has increased rapidly over the past decade for use in applications such as catalysis, gas storage and removal of environmentally unfriendly species due to their high surface area and high thermal stability. In this work, a theoretical study of the zirconium-based metal organic framework (MOFs) were examined in order to determine their potential for gas adsorption of various guest molecules: CO2, N2, CH4 and H2. The zirconium cluster consists of an inner Zr6O4(OH)4 core in which the triangular faces of the Zr6- octahedron are alternatively capped by O and OH groups which bound to nine formate groups and three benzoate groups linkers. General formula is [Zr(μ-O)4(μ-OH)4(HCOO)9((phyO2C)3X))] where X= CH2OH, CH2NH2, CH2CONH2, n(NH2); (n = 1-3). Three types of adsorption sites on the Zr metal center have been studied, named according to capped chemical groups as the ‘−O site’; the H of (μ-OH) site removed and added to (μ-O) site, ‘–OH site’; (μ-OH) site removed, the ‘void site’ where H2O molecule removed; (μ-OH) from one site and H from other (μ-OH) site, in addition to no defect versions. A series of investigations have been performed aiming to address this important issue. First, density functional theory DFT-B3LYP method with 6-311G(d,p) basis set was employed using Gaussian 09 package in order to evaluate the gas adsorption performance of missing-linker defects in zirconium cluster. Next, study the gas adsorption behaviour on different functionalised zirconium clusters. Those functional groups as mentioned above include: amines, alcohol, amide, in comparison with non-substitution clusters. Then, dispersion-corrected density functional theory (DFT-D) calculations were performed to further understand the enhanced gas binding on zirconium clusters. Finally, study the water effect on CO2 and N2 adsorption. The small functionalized Zr clusters were found to result in good CO2 adsorption over N2, CH4, and H2 due to the quadrupole moment of CO2 while N2, CH4 and H2 weakly polar or non-polar. The adsorption efficiency was determined using the dispersion method where the adsorption binding improved as most of the interactions, for example, van der Waals interactions are missing with the conventional DFT method. The calculated gas binding strengths on the no defect site are higher than those on the −O site, −OH site and the void site, this difference is especially notable for CO2. It has been stated that the enhanced affinity of CO2 of no defect versions is most likely due to the electrostatic interactions between the negatively charged O of CO2 and the positively charged H of (μ-OH) metal site. The uptake of the gas molecule does not enhance in presence of water as the latter binds to Zr clusters more strongly than gas species which attributed to the competition on adsorption sites.

Keywords: density functional theory, gas adsorption, metal- organic frameworks, molecular simulation, porous materials, theoretical chemistry

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Authors: H. M. Soroush

Abstract:

The problem of scheduling products and services for on-time deliveries is of paramount importance in today’s competitive environments. It arises in many manufacturing and service organizations where it is desirable to complete jobs (products or services) with different weights (penalties) on or before their due dates. In such environments, schedules should frequently decide whether to schedule a job based on its processing time, due-date, and the penalty for tardy delivery to improve the system performance. For example, it is common to measure the weighted number of late jobs or the percentage of on-time shipments to evaluate the performance of a semiconductor production facility or an automobile assembly line. In this paper, we address the problem of scheduling a set of jobs on a server where processing times or due-dates of jobs are random variables and fixed weights (penalties) are imposed on the jobs’ late deliveries. The goal is to find the schedule that minimizes the expected weighted number of tardy jobs. The problem is NP-hard to solve; however, we explore three scenarios of the problem wherein: (i) both processing times and due-dates are stochastic; (ii) processing times are stochastic and due-dates are deterministic; and (iii) processing times are deterministic and due-dates are stochastic. We prove that special cases of these scenarios are solvable optimally in polynomial time, and introduce efficient heuristic methods for the general cases. Our computational results show that the heuristics perform well in yielding either optimal or near optimal sequences. The results also demonstrate that the stochasticity of processing times or due-dates can affect scheduling decisions. Moreover, the proposed problem is general in the sense that its special cases reduce to some new and some classical stochastic single machine models.

Keywords: number of late jobs, scheduling, single server, stochastic

Procedia PDF Downloads 460