Search

Software-defined wireless sensor networks (SDWSNs) are a great step forward to centralize and facilitate the management of low-power networks. However, the continuous sending of local information from the sensor nodes to the controller causes excessive energy loss and increases the network overhead. This dilemma has become one of the hot topics of current research to make SDWSNs more efficient. This paper has proposed an efficient energy predictor for SDWSNs (E2-SDWSN) to improve the performance of the sensor network. The proposed approach reduces the cost of packet delivery and energy consumption using the well-known MapReduce framework with an intelligent selection of the reducers. Using MapReduce in the SDWSN by in-network processing significantly reduces energy consumption and balances traffic. Furthermore, the controller using an energy prediction model predicts the residual energy level of the nodes. Therefore, assuming the position of the nodes is fixed, the need to sequential send local packets is reduced. Reducing the number of local and energy information packets of the network decreases the network traffic. This consequently improves the quality of service and reduces energy consumption. Therefore, the proposed model increases the lifetime of the network. The results also confirm that the proposed E2-SDWSN significantly improves the energy throughput, the packet delivery ratio, and the delay through the sensor network. [ABSTRACT FROM AUTHOR]

The smart world is connecting all universe more than ever thought possible, benefiting from the significant advances of the Internet of Things (IoT) applications using wireless sensor networks (WSN) as the core technology. A challenging issue in the IoT paradigm is the heterogeneity in different parts of the network. The network developers need to use resources belonging to different platforms for their applications, and the software defined network (SDN) approach is a mainly considered solution. In this paper, a software defined wireless sensor network (SDWSN) with an energy predictor model (SDWSN-EPM) based on the Markov probabilistic model is proposed to reduce the energy consumption and the network latency. The energy consumption rate (ECR) of the sensor nodes is modeled using the Markov model and the states of the sensor nodes. The ECR is used by the SDN controller to predict the residual energy level of the nodes and consequently, the energy consumption of the network. The cumulative distribution functions (CDF) of the delay, power consumption, and the network lifetime in both SDWSN and SDWSN-EPM schemes are compared. The results confirm that the SDWSN-EPM model significantly improves the performance of the sensor networks. [ABSTRACT FROM AUTHOR]

In short-channel silicon-on-insulatormetal-oxidesemiconductor transistors (SOI MOSFETs) the high electric field near the drain increases the floating-body effect. The aim of this article is to introduce a novel structure that reduces the electric field near the drain, so improving the floating-body effect. In the proposed structure, a dual trench is created in the buried oxide exactly under the junctions of drain/source and channel and is filled with an n-type SiGe material. The dual trench regions absorb the electric field lines and hence, the electric characteristic significantly improve. The proposed structure is named as dual SiGe trench double gate SOIMOSFET. In addition, we observe a considerable improvement in self-heating effects due to the higher thermal conductivity of SiGe in comparison with silicon dioxide. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common cancer type. In several studies, hints have been provided that there is a correlation between selenium deficiency and the incidence of breast cancer. Findings of these published reports are, however, inconsistent. This study serves as a pioneering study aiming at combining the results of studies using a meta-analytic method. A total of 16 articles published between 1980 and 2012 worldwide were selected through searching PubMed, Scopus, and Google scholar databases, and the information were analyzed using a meta-analytic method [random effects model]. I statistics were used to examine heterogeneity. The information was then analyzed by STATA version 12. In this study, due to the non-uniform methods used to measure selenium concentrations, selenium levels were measured in the various subgroups in both case and control groups. There were significant correlations between selenium concentration and breast cancer [ P < 0.05]. Hence, the mean risk differentiating criteria were estimated to be 0.63 [95 % confidence interval [95% CI] 0.93 to 0.32] in serum and toenails. Subgroup analysis showed that the value in toenails was −0.07 [95% CI −0.16 to 0.03] and in serum −1.04 [95% CI 1.71 to −0.38]. In studies in which selenium concentrations were measured in serum, a significant correlation was observed between selenium concentration and breast cancer. In contrast, in studies in which selenium concentration was measured in toenails, the correlation was not significant. Therefore, the selenium concentration can be used as one predictor for breast cancer. [ABSTRACT FROM AUTHOR]

Background: Micro inflammation and cardiovascular disease such as left ventricular hypertrophy (LVH) are common in hemodialysis (HD) patients. Hence, we have evaluated the relationship between high-sensitive C-reactive protein (hs-CRP), as an inflammation marker, and left ventricular mass index (LVMi) and left ventricular mass (LVM) in HD patients. Methods: An analytical cross-sectional study was performed in 104 HD patients. Serum hs-CRP, LVMi, LVM, and blood pressure were evaluated; demographic data and duration of HD were also recorded. Finally, results were analyzed by using Student’s t test, Pearson’s correlation coefficient, one-way ANOVA and multiple regression to determine the relationship between LVMi and other variables. Results: A total of 66 male patients (63.46 %) and 38 female patients, with a mean age of 51.75 ± 15.98 years-old, participated in this study. Hypertension was the most common underlying disease (65.4 %). The mean LVMi was 366.98 ± 120.89 g/m 2 and the mean hs-CRP was 8.55 mg/l. Eighty-nine percent of patients had LVH. The hs-CRP level was significantly associated with age and with LVM ( P = 0.0001, P = 0.039, respectively). On multivariate analysis, hs-CRP and systolic blood pressure were found to be independent predictors of LVM and LVMi. Conclusions: This study shows that hs-CRP and systolic BP are independent predictors of LVH in HD patients. [ABSTRACT FROM AUTHOR]

This research targets persistency by introducing new features in 'Chatsistance', a persistent chat tool that employs speech act theory and an explicit referencing technique to structure the chat log, and by providing further options for retrieving information. Earlier theory is related to the linguistics and characteristics of user statements. The later technique shows the relation of artefacts in an interconnected context named 'explicit referencing'. Although speech acts were previously used in the field of chat, it is the first time that this theory has been used for the purpose of enhancing persistency combined with an explicit referencing technique, which together provide a well-established structure that provides greater flexibility for the analysis of content. In this paper, we show how this structuring approach could be useful, by evaluating the results of previous experiments that have used Chatsistance. [ABSTRACT FROM AUTHOR]