Location prediction based on a sector snapshot for location-based services

In location-based services (LBSs), the service is provided based on the users' locations through location determination and mobility realization. Most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shaped cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. Such techniques are the new Markov-based mobility prediction (NMMP) and prediction location model (PLM) that deal with inner cell structure and different levels of prediction, respectively. The NMMP and PLM techniques suffer from complex computation, accuracy rate regression, and insufficient accuracy. In this paper, a novel cell splitting algorithm is proposed. Also, a new prediction technique is introduced. The cell splitting is universal so it can be applied to all types of cells. Meanwhile, this algorithm is implemented to the Micro cell in parallel with the new prediction technique. The prediction technique, compared with two classic prediction techniques and the experimental results, show the effectiveness and robustness of the new splitting algorithm and prediction technique

Identifying sources, pathways and risk drivers in ecosystems of Japanese Encephalitis in an epidemic-prone north Indian district

Japanese Encephalitis (JE) has caused repeated outbreaks in endemic pockets of India. This study was conducted in Kushinagar, a highly endemic district, to understand the human-animal-ecosystem interactions, and the drivers that influence disease transmission. Utilizing the ecosystems approach, a cross-sectional, descriptive study, employing mixed methods design was employed. Four villages (two with pig-rearing and two without) were randomly selected from a high, a medium and a low burden (based on case counts) block of Kushinagar. Children, pigs and vectors were sampled from these villages. A qualitative arm was incorporated to explain the findings from the quantitative surveys. All human serum samples were screened for JE-specific IgM using MAC ELISA and negative samples for JE RNA by rRT-PCR in peripheral blood mononuclear cells. In pigs, IgG ELISA and rRT-PCR for viral RNA were used. Of the 242 children tested, 24 tested positive by either rRT-PCR or MAC ELISA; in pigs, 38 out of the 51 pigs were positive. Of the known vectors, Culex vishnui was most commonly isolated across all biotopes. Analysis of 15 blood meals revealed human blood in 10 samples. Univariable analysis showed that gender, religion, lack of indoor residual spraying of insecticides in the past year, indoor vector density (all species), and not being vaccinated against JE in children were significantly associated with JE positivity. In multivariate analysis, only male gender remained as a significant risk factor. Based on previous estimates of symptomatic: asymptomatic cases of JE, we estimate that there should have been 618 cases from Kushinagar, although only 139 were reported. Vaccination of children and vector control measures emerged as major control activities; they had very poor coverage in the studied villages. In addition, lack of awareness about the cause of JE, lack of faith in the conventional medical healthcare system and multiple referral levels causing delay in diagnosis and treatment emerged as factors likely to result in adverse clinical outcomes

Gut microbiome composition is linked to whole grain-induced immunological improvements

The involvement of the gut microbiota in metabolic disorders, and the ability of whole grains to affect both host metabolism and gut microbial ecology, suggest that some benefits of whole grains are mediated through their effects on the gut microbiome. Nutritional studies that assess the effect of whole grains on both the gut microbiome and human physiology are needed. We conducted a randomized cross-over trial with four-week treatments in which 28 healthy humans consumed a daily dose of 60 g of whole-grain barley (WGB), brown rice (BR), or an equal mixture of the two (BR+WGB), and characterized their impact on fecal microbial ecology and blood markers of inflammation, glucose and lipid metabolism. All treatments increased microbial diversity, the Firmicutes/Bacteroidetes ratio, and the abundance of the genus Blautia in fecal samples. The inclusion of WGB enriched the genera Roseburia, Bifidobacterium and Dialister, and the species Eubacterium rectale, Roseburia faecis and Roseburia intestinalis. Whole grains, and especially the BR+WGB treatment, reduced plasma interleukin-6 (IL-6) and peak postprandial glucose. Shifts in the abundance of Eubacterium rectale were associated with changes in the glucose and insulin postprandial response. Interestingly, subjects with greater improvements in IL-6 levels harbored significantly higher proportions of Dialister and lower abundance of Coriobacteriaceae. In conclusion, this study revealed that a short-term intake of whole grains induced compositional alterations of the gut microbiota that coincided with improvements in host physiological measures related to metabolic dysfunctions in humans

A Neural Network–Based Connectivity Management for Mobile Computing Environment

The mobile computing environment experiences wireless problems and suffers from limited bandwidth, which leads to frequent disconnections. This has posed a challenge in maintaining user-to-user connectivity in the mobile computing environment. In this paper, we propose a neural network (NN) based connectivity management for mobile computing environment to maintain the mobile user-to-user connectivity throughout the transaction. Here the connectivity management maintains the status information of mobile hosts at the base station to handle frequent disconnection of mobile hosts (MHs), which occur because of hand-offs and interruptions. The disconnection of an MH because of wireless problems is called interruption, and the disconnection due to MH crossing the cell boundary is called hand-off. The neural networks are trained with respect to the status information to provide an intelligent decision for the connectivity management. The simulation results demonstrate that the proposed technique performs well in terms of percentage acceptance of disconnections and resource utilization (bandwidth and buffer) for the volatile mobile computing environment. It is also observed that the intelligent decision by neural network has improved the performance of the syste

Reliable multicast routing in mobile networks: a neural-network approach

Mobile networks that handle multicast communication services, such as video-on-demand, news-on-demand etc., require a kind of reliable and secure point-to-point, point-to-multipoint specific group communications for sophisticated organisation of multicast communications. A reliable multicast tree is an efficient connectivity between the source node and the multicast group members through the dependable hosts. Because the mobile host changes its access point over time, multicast routes must be updated. This poses several challenges to provide an efficient multicast routing. A neural-network-based multicast routing algorithm is proposed for constructing a reliable multicast tree that connects the participants of a multicast group. The problem is tackled by dividing a mobile network into clusters of nodes based on the adjacency relation between the nodes (mobile support stations), by considering a suitable neighbourhood distance. The centre cluster, whose nodes are almost equidistant from the multicast group members, is computed to construct a shortest multicast tree that passes through the centre cluster and reliable routers among all the group members. A Kohonens self-organising-map neural network has been used for clustering. Hopfield neural networks are used to construct a multicast tree with a minimum number of links which passes through the nodes that belong to centre cluster. In addition, the tree is constructed as and when the member(s) moves. This scheme should construct a reliable multicast tree and minimise recomputation time of the multicast tree as and when the multicast route is updated when the mobile hosts change their access point due to mobility. The computational power of the proposed neural-network-based multicast routing algorithm is demonstrated through simulation. The algorithm is also tested for mobility of the participating mobile hosts. The proposed work facilitates a possible multicast routing algorithm for future high-speed mobile networks

An Efficient Resource Allocation Scheme for Mobile Multimedia Networks

In mobile multimedia networks the traffic fluctuation is unpredictable and also due to limited resource availability, the resource allocation to multimedia applications of varying quality of service (QoS) requirement becomes a complex issue. This paper proposes an efficient resource allocation scheme based on resource reduction of running applications without hampering their QoS guarantee, in a single mobile cellular environment. We propose a linear programming (LP) based resource reduction for efficient resource allocation (RA). Artificial neural network model is used to solve the linear programming problem, which facilitates in real time control decision in the practical systems. The model is computationally less expensive and faster for resource allocation. The suggested scheme along with the LP-based resource reduction has shown that it is appropriate for the reduction of assigned resources for running applications during over load conditions and allocation of resources to requesting applications. The simulation results for the proposed scheme yielded an improved resource utilization and lower percentage of rejection to hand-off and new applications due to efficient resource allocation

A LP-based Admission Control Using Artificial Neural Networks for Integrated Services in Mobile Networks

In mobile networks the traffic fluctuation is unpredictable dueto mobility and varying resource requirements of multimedia applications.Henceit is essential to maintain the traffic within the network capacity to providethe service guarantees to running applications. Thispaper proposes an Admission Control (AC) scheme in a single mobile cellularenvironment supporting real-time and non-real-time application traffic. In thecase of a real-time and non-real-time multimedia applications, eachapplication has its own distinct range of acceptable Quality of Service (QoS)requirements(e.g., packet loss, delay, jitter, etc.). The network provides the service bymaintaining the application specified QoS range. We propose a LinearProgrammingResource Reduction (LP-RR) principle for admission control by maintainingQoSguarantees to existing applications and to increase the percentage ofadmissionto real-time and non-real-time applications. Artificial Neural Networks (ANNs)are used to solve linear programming problem, which facilitates an on-lineadmissioncontrol decision in the practical systems.The simulation results demonstrate that the proposed AC schemeperforms well in terms of admitted applications and maintains lower percentageof rejection to hand-off and new applications of different traffic classes.The suggested principle also shown that it is appropriate for the fairresourceallocation with improved resource utilization

A Neural Network–Based Connectivity Management for Mobile Computing Environment

The mobile computing environment experiences wireless problems and suffers from limited bandwidth, which leads to frequent disconnections. This has posed a challenge in maintaining user-to-user connectivity in the mobile computing environment. In this paper, we propose a neural network (NN) based connectivity management for mobile computing environment to maintain the mobile user-to-user connectivity throughout the transaction. Here the connectivity management maintains the status information of mobile hosts at the base station to handle frequent disconnection of mobile hosts (MHs), which occur because of hand-offs and interruptions. The disconnection of an MH because of wireless problems is called interruption, and the disconnection due to MH crossing the cell boundary is called hand-off. The neural networks are trained with respect to the status information to provide an intelligent decision for the connectivity management. The simulation results demonstrate that the proposed technique performs well in terms of percentage acceptance of disconnections and resource utilization (bandwidth and buffer) for the volatile mobile computing environment. It is also observed that the intelligent decision by neural network has improved the performance of the syste

Neural network based optimal routing algorithm for communication networks

This paper presents the capability of the neural networks as a computational tool for solving constrained optimization problem, arising in routing algorithms for the present day communication networks. The application of neural networks in the optimum routing problem, in case of packet switched computer networks, where the goal is to minimize the average delays in the communication have been addressed. The effectiveness of neural network is shown by the results of simulation of a neural design to solve the shortest path problem. Simulation model of neural network is shown to be utilized in an optimum routing algorithm known as flow deviation algorithm. It is also shown that the model will enable the routing algorithm to be implemented in real time and also to be adaptive to changes in link costs and network topology. (C) 2002 Elsevier Science Ltd. All rights reserved

Cilostazol for secondary stroke prevention: systematic review and meta-analysis.

Stroke and Vascular Neurolog