Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. However, WSN developers are usually not or not completely aware of the potential CI algorithms offer. On the other side, CI researchers are not familiar with all real problems and subtle requirements of WSNs. This mismatch makes collaboration and development difficult. This paper intends to close this gap and foster collaboration by offering a detailed introduction to WSNs and their properties. An extensive survey of CI applications to various problems in WSNs from various research areasand publication venues is presented in the paper. Besides, a discussion on advantages and disadvantages of CI algorithms over traditional WSN solutions is offered. In addition, a general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for WSNs.

Leaves of the monoterpene emitter Quercus ilex were exposed to a temperature ramp with 5°C steps from 30 to 55°C while maintained under conditions in which endogenous emission of monoterpenes was allowed or suppressed, or under fumigation with selected exogenous monoterpenes. Fumigation with monoterpenes reduced the decline of photosynthesis, photorespiration and monoterpene emission found in non-fumigated leaves exposed to high temperatures. It also substantially increased respiration when photosynthesis and photorespiration were inhibited by low O 2 and CO 2 -free air. These results indicate that, as previously reported for isoprene, monoterpenes may help plants cope with heat stress. Monoterpenes may enhance membrane stability, thus providing a rather non-specific protection of photosynthetic and respiratory processes. Monoterpene emission was maximal at a temperature of 35°C and was inhibited at higher temperatures. This is likely to be the result of the temperature dependency of the enzymes involved in monoterpene synthesis. In contrast to other monoterpenes, cis-and trans-β-ocimene did not respond to exposure to high temperatures. Cis-β-ocimene also did not respond to low O 2 or to fumigation. These results indicate that cis and trans-β-ocimene may have a different pathway of formation that probably does not involve enzymatic synthesis.

Based on the memory for the re-expression of certain cytokine genes, different subsets of Th cells have been defined. In Th type 1 (Th1) and Th2 memory lymphocytes, the genes for the cytokines interferon-c and interleukin (IL)-4 are imprinted for expression upon restimulation by the expression of the transcription factors T-bet and GATA-3, respectively, and epigenetic modification of the cytokine genes. In Th17 cells, IL-17 expression is dependent on the transcription factors RORct and RORa. Here, we analyze the stability and plasticity of IL-17 memory in Th17 cells. We have developed a cytometric IL-17 secretion assay for the isolation of viable Th cells secreting IL-17. For Th17 cells generated in vitro, IL-17 expression itself is dependent on continued TGF-b/IL-6 or IL-23 signaling and is blocked by interferon-c and IL-4 signaling. In response to IL-12 and IL-4, in vitro generated Th17 cells are converted into Th1 or Th2 cells, respectively. Th17 cells isolated ex vivo, however, maintain their IL-17 memory upon subsequent in vitro culture, even in the absence of IL-23. Their cytokine memory is not regulated by IL-12 or IL-4. Th17 cells generated in vivo are a stable and distinct lineage of Th cell differentiation.Key words: Cytokine memory . Interleukin-17 . T-cell differentiation Supporting Information available online IntroductionTh memory lymphocytes are imprinted for the re-expression of distinct cytokine genes upon restimulation. Originally, two types of Th effector memory cells had been defined: T helper type 1 (Th1) cells re-expressing interferon-g (IFN-g), and Th2 cells, reexpressing interleukin (IL)-4, -5 and -13 [1]. Recently, a third lineage of Th effector memory cells has been described, characterized by the re-expression of IL-17A, ). Th17 cells can induce autoimmune inflammation [3] and are protective in response to fungal infection [4]. In vitro, naïve murine Th cells can be induced to differentiate into Th17 cells by combined TGF-b and IL-6 signalling [5,6]. IL-23 promotes survival and proliferation of Th17 cells [6]. IL-21 can induce IL-17 independent of IL-6 and is expressed by Th17 cells themselves, as part of a positive regulatory feedback loop for IL-17 re-expression [7,8]. In human Th cells, similar signals are required for the differentiation of IL-17 re-expressing Th memory cells [9][10][11]. STAT3 is involved as a signal transducer and and the retinoic acid receptor-related orphan 2654Frontline receptors RORgt [13] and RORa [14] as transcription factors controlling lineage development. Ectopic over-expression of RORgt and RORa in naïve Th cells is sufficient to induce IL-17 expression [14].As part of their functional memory, the capacity of effector memory Th cells to stably re-express particular cytokines has been demonstrated for Th1 cells and IFN-g expression and for Th2 cells and their IL-4 and IL-10 expression [15,16]. This memory cytokine expression depends on TcR signals, but does not require the original instructive signals. It even occurs in the presence of adverse instruc...

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Abstract-Simulation is one of the most powerful tools we have for evaluating the performance of Opportunistic Networks. In this survey, we focus on available tools and models, compare their performance and precision and experimentally show the scalability of different simulators. We also perform a gap analysis of state-of-the-art Opportunistic Network simulations and sketch out possible further development and lines of research.This survey is targeted at students starting work and research in this area while also serving as a valuable source of information for experienced researchers.